id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
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maritaca-ai/boolq_pt | 2023-02-09T00:38:29.000Z | [
"region:us"
] | maritaca-ai | BoolQ is a question answering dataset for yes/no questions containing 15942 examples. These questions are naturally
occurring ---they are generated in unprompted and unconstrained settings.
Each example is a triplet of (question, passage, answer), with the title of the page as optional additional context.
The text-pair classification setup is similar to existing natural language inference tasks. | @inproceedings{clark2019boolq,
title = {BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},
author = {Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},
booktitle = {NAACL},
year = {2019},
} | 1 | 230 | 2023-01-29T21:30:08 | Entry not found | 15 | [
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jonathanli/hyperpartisan-longformer-split | 2022-12-31T16:08:16.000Z | [
"arxiv:2004.05150",
"region:us"
] | jonathanli | null | null | 0 | 229 | 2022-12-31T15:56:50 | # Hyperpartisan news detection
This dataset has the hyperpartisan new dataset, processed and split exactly as it was for [longformer](https://arxiv.org/abs/2004.05150) experiments.
Code for processing was found at [here](https://github.com/allenai/longformer/blob/master/scripts/hp_preprocess.py).
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] |
GATE-engine/cubirds200_bbcrop | 2023-06-04T23:55:39.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 228 | 2023-06-04T23:54:48 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 669852245.5
num_examples: 8204
- name: validation
num_bytes: 141492702.625
num_examples: 1771
- name: test
num_bytes: 146984302.75
num_examples: 1770
download_size: 958308742
dataset_size: 958329250.875
---
# Dataset Card for "cubirds200_bbcrop"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 543 | [
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] |
togethercomputer/llama-instruct | 2023-08-18T05:04:06.000Z | [
"language:en",
"license:llama2",
"arxiv:2304.12244",
"region:us"
] | togethercomputer | null | null | 20 | 228 | 2023-08-03T04:52:19 | ---
license: llama2
language:
- en
---
# llama-instruct
This dataset was used to finetune [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct).
We follow the distillation paradigm that is used by [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Vicuna](https://lmsys.org/blog/2023-03-30-vicuna/), [WizardLM](https://arxiv.org/abs/2304.12244), [Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)
— producing instructions by querying a powerful LLM, which in our case, is the [Llama-2-70B-Chat](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) model released by [Meta](https://ai.meta.com/llama/).
To build [Llama-2-7B-32K-Instruct](https://huggingface.co/togethercomputer/Llama-2-7B-32K-Instruct), we collect instructions from 19K human inputs extracted from [ShareGPT-90K](https://huggingface.co/datasets/philschmid/sharegpt-raw) (only using human inputs, not ChatGPT outputs).
The actual script handles multi-turn conversations and also supports restarting and caching via a SQLite3 database.
You can find the full script [here](https://github.com/togethercomputer/Llama-2-7B-32K-Instruct/blob/main/scripts/distill.py), with merely 122 lines!
The output of this step is a jsonl file, each line corresponding to one conversation:
```
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
{"text": "[INST] ... instruction ... [/INST] ... answer ... [INST] ... instruction ... [/INST] ..."}
```
For more details, please refer to the [Github repo](https://github.com/togethercomputer/Llama-2-7B-32K-Instruct).
## Languages
The language of the data is entirely English. | 1,853 | [
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greek_legal_code | 2023-06-12T14:25:00.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:el",
"license:cc-by-4.0",
"arxiv:2109.15298",
"region:us"
] | null | Greek_Legal_Code contains 47k classified legal resources from Greek Legislation. Its origin is “Permanent Greek Legislation Code - Raptarchis”,
a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories. | @inproceedings{papaloukas-etal-2021-glc,
title = "Multi-granular Legal Topic Classification on Greek Legislation",
author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis",
booktitle = "Proceedings of the 3rd Natural Legal Language Processing (NLLP) Workshop",
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "",
url = "",
doi = "",
pages = ""
} | 8 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- el
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
pretty_name: Greek Legal Code
dataset_info:
- config_name: volume
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ
'1': ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ
'2': ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ
'3': ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ
'4': ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ
'5': ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ
'6': ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ
'7': ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ
'8': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ
'9': ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ
'10': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ
'11': ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ
'12': ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ
'13': ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ
'14': ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ
'15': ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'16': ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ
'17': ΔΗΜΟΣΙΑ ΕΡΓΑ
'18': ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ
'19': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ
'20': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'21': ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'22': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ
'23': ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ
'24': ΣΥΓΚΟΙΝΩΝΙΕΣ
'25': ΕΘΝΙΚΗ ΑΜΥΝΑ
'26': ΣΤΡΑΤΟΣ ΞΗΡΑΣ
'27': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ
'28': ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ
'29': ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ
'30': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ
'31': ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ
'32': ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'33': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ
'34': ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'35': ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'36': ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ
'37': ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ
'38': ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ
'39': ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ
'40': ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'41': ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'42': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ
'43': ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ
'44': ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ
'45': ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ
'46': ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
splits:
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num_bytes: 216757887
num_examples: 28536
- name: test
num_bytes: 71533786
num_examples: 9516
- name: validation
num_bytes: 68824457
num_examples: 9511
download_size: 45606292
dataset_size: 357116130
- config_name: chapter
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΟΡΥΧΕΙΑ
'1': ΣΤΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ
'2': ΠΑΡΟΧΕΣ ΑΝΕΡΓΙΑΣ
'3': ΣΙΔΗΡΟΔΡΟΜΙΚΑ ΔΙΚΤΥΑ
'4': ΕΙΔΙΚΑ ΣΤΡΑΤΙΩΤΙΚΑ ΑΔΙΚΗΜΑΤΑ
'5': ΚΡΑΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ
'6': ΑΓΡΟΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ
'7': ΑΞΙΩΜΑΤΙΚΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'8': ΣΧΕΔΙΑ ΠΟΛΕΩΝ
'9': ΣΥΚΑ
'10': ΠΡΟΛΗΨΙΣ ΚΑΙ ΔΙΩΞΙΣ ΤΟΥ ΕΓΚΛΗΜΑΤΟΣ
'11': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ
'12': ΓΕΝΙΚΗ ΣΥΓΚΟΙΝΩΝΙΑ ΚΑΙ ΔΙΑΤΑΞΕΙΣ
'13': ΚΛΗΡΟΝΟΜΙΚΟ ΔΙΚΑΙΟ
'14': ΚΟΙΝΩΝΙΚΗ ΑΝΤΙΛΗΨΗ
'15': ΝΑΥΤΙΛΙΑΚΕΣ ΣΗΜΑΝΣΕΙΣ
'16': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ
'17': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ Ε.Ν
'18': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ
'19': ΣΠΟΡΟΠΑΡΑΓΩΓΗ
'20': ΥΠΗΡΕΣΙΑΙ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ
'21': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΤΡΑΠΕΖΩΝ
'22': ΠΥΡΟΣΒΕΣΤΙΚΟ ΣΩΜΑ
'23': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ
'24': ΕΚΤΕΛΕΣΗ ΚΑΙ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ
'25': ΔΙΕΘΝΕΙΣ ΑΣΦΑΛΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'26': ΝΟΜΟΘΕΣΙΑ
'27': ΒΑΜΒΑΚΙ
'28': ΠΑΡΟΧΕΣ ΣΥΝΤΑΞΕΩΝ
'29': ΝΟΜΙΣΜΑ
'30': ΣΥΜΒΑΣΗ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ
'31': ΟΡΓΑΝΙΣΜΟΊ ΚΟΙΝΩΝΙΚΉΣ ΑΣΦΑΛΊΣΕΩΣ
'32': ΑΓΡΟΤΙΚΗ ΑΣΦΑΛΕΙΑ
'33': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ
'34': ΜΟΥΣΕΙΑ ΚΑΙ ΣΥΛΛΟΓΕΣ
'35': ΠΡΟΣΩΠΙΚΟ Ι.Κ.Α
'36': ΞΕΝΟΔΟΧΕΙΑ
'37': ΚΡΑΤΙΚΗ ΑΣΦΑΛΕΙΑ
'38': ΣΥΝΕΤΑΙΡΙΣΜΟΙ
'39': ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΦΩΝΙΕΣ
'40': ΕΤΕΡΟΔΟΞΟΙ
'41': ΜΕΣΗ ΕΚΠΑΙΔΕΥΣΙΣ
'42': ΓΕΩΡΓΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ
'43': ΓΕΝΙΚΟ ΛΟΓΙΣΤΗΡΙΟ
'44': ΡΥΘΜΙΣΗ ΤΗΣ ΑΓΟΡΑΣ ΕΡΓΑΣΙΑΣ
'45': ΠΑΡΟΧΟΙ ΚΙΝΗΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ
'46': ΕΜΠΡΑΓΜΑΤΟΣ ΑΣΦΑΛΕΙΑ
'47': ΦΟΡΟΛΟΓΙΑ ΑΚΑΘΑΡΙΣΤΟΥ ΠΡΟΣΟΔΟΥ
'48': ΚΤΗΜΑΤΙΚΕΣ ΤΡΑΠΕΖΕΣ
'49': ΣΤΑΤΙΣΤΙΚΗ
'50': ΚΕΡΑΙΕΣ – ΣΤΑΘΜΟΙ ΚΕΡΑΙΩΝ
'51': ΠΟΙΝΙΚΟΣ ΝΟΜΟΣ
'52': ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ
'53': ΕΜΠΟΡΙΟ ΦΑΡΜΑΚΩΝ
'54': ΔΙΑΦΟΡΑ
'55': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ
'56': ΕΙΣΦΟΡΕΣ Ι.Κ.Α
'57': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ
'58': ΠΡΟΣΩΠΙΚΟ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'59': ΔΗΜΟΣΙΟ ΧΡΕΟΣ
'60': ΑΠΟΤΑΜΙΕΥΣΗ
'61': ΑΛΛΟΘΡΗΣΚΟΙ
'62': ΠΛΟΗΓΙΚΗ ΥΠΗΡΕΣΙΑ
'63': ΤΥΠΟΣ ΚΑΙ ΠΛΗΡΟΦΟΡΙΕΣ
'64': ΤΡΟΠΟΠΟΙΗΣΗ ΚΑΙ ΚΑΤΑΡΓΗΣΗ ΤΗΣ ΠΟΙΝΗΣ
'65': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΥΠΟΥ
'66': ΟΙΚΟΓΕΝΕΙΑΚΟ ΔΙΚΑΙΟ
'67': ΔΙΟΙΚΗΣΗ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ
'68': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ
'69': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΝΟΙΑΣ
'70': ΠΡΟΣΩΠΙΚΟ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ
'71': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ ΕΙΔΙΚΩΝ ΚΑΤΗΓΟΡΙΩΝ
'72': ΠΑΡΟΧΕΣ ΑΣΘΕΝΕΙΑΣ
'73': ΜΕΤΑΝΑΣΤΕΥΣΗ
'74': ΥΠΟΥΡΓΕΙΟ ΠΑΙΔΕΙΑΣ
'75': ΑΣΦΑΛΕΙΑ ΝΑΥΣΙΠΛΟΪΑΣ
'76': ΟΔΟΠΟΙΪΑ
'77': ΣΤΡΑΤΟΔΙΚΕΙΑ
'78': ΜΙΣΘΩΣΗ
'79': ΕΙΣΠΡΑΞΗ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ
'80': ΟΠΛΙΤΕΣ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΕΣ
'81': ΟΡΓΑΝΙΣΜΟΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΛΛΑΔΑΣ (Ο.Τ.Ε.)
'82': ΌΡΓΑΝΑ ΆΣΚΗΣΗΣ ΔΙΑΧΕΙΡΙΣΤΙΚΟΎ ΕΛΈΓΧΟΥ ΟΡΓΑΝΙΣΜΏΝ ΚΑΙ ΕΠΙΧΕΙΡΉΣΕΩΝ
'83': ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ ΤΥΠΟΥ
'84': ΕΞΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ
'85': ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'86': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΓΡΟΤΙΚΕΣ ΣΥΝΕΤΑΙΡΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ
'87': ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ
'88': ΟΧΥΡΩΣΕΙΣ
'89': ΕΚΤΑΚΤΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ
'90': ΕΚΤΕΛΕΣΗ
'91': ΔΙΟΙΚΗΤΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ
'92': ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ
'93': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'94': ΕΚΚΑΘΑΡΙΣΕΙΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'95': ΔΙΟΙΚΗΣΗ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ
'96': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ
'97': ΑΡΤΟΣ
'98': ΕΙΣΑΓΩΓΙΚΟ ΕΜΠΟΡΙΟ
'99': ΑΛΙΕΙΑ
'100': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΠΕΡΙΟΥΣΙΑ
'101': ΔΙΑΦΟΡΑ ΔΗΜΟΣΙΑ ΕΡΓΑ
'102': ΜΟΝΕΣ
'103': ΠΡΟΕΔΡΟΣ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ ΚΑΙ ΠΡΟΕΔΡΙΑ ΤΗΣ ΔΗΜΟΚΡΑΤΙΑΣ
'104': ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ
'105': ΑΡΧΑΙΟΤΗΤΕΣ
'106': ΝΑΟΙ ΚΑΙ ΛΕΙΤΟΥΡΓΟΙ ΑΥΤΩΝ
'107': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'108': ΕΝΙΣΧΥΣΙΣ ΤΗΣ ΓΕΩΡΓΙΑΣ
'109': ΕΚΘΕΣΕΙΣ
'110': ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΣΥΝΑΛΛΑΓΩΝ
'111': ΑΣΦΑΛΙΣΗ
'112': ΚΤΗΝΟΤΡΟΦΙΑ
'113': ΕΚΠΑΙΔΕΥΤΙΚΑ ΤΕΛΗ
'114': ΔΙΟΙΚΗΣΗ ΕΚΠΑΙΔΕΥΣΕΩΣ
'115': ΤΑΜΕΙΟ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ
'116': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ
'117': ΦΟΡΟΛΟΓΙΚΑ ΔΙΚΑΣΤΗΡΙΑ
'118': ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ
'119': ΒΙΒΛΙΟΘΗΚΕΣ-ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΔΙΑΔΟΣΗ ΛΟΓΟΤΕΧΝΙΑΣ
'120': ΤΗΛΕΠΙΚΟΙΝΩΝΙΑΚΕΣ ΚΑΙ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'121': ΙΔΙΩΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'122': ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ
'123': ΑΣΥΡΜΑΤΟΣ
'124': ΑΠΟΔΟΧΕΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΩΝ
'125': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ
'126': ΦΑΡΜΑΚΕΙΑ
'127': ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ
'128': ΝΑΥΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'129': ΕΞΥΠΗΡΕΤΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'130': ΠΑΡΟΧΕΣ Ι.Κ.Α
'131': ΓΕΝΙΚΑ ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ
'132': ΕΚΜΕΤΑΛΛΕΥΣΗ ΘΑΛΑΣΣΙΩΝ ΣΥΓΚΟΙΝΩΝΙΩΝ
'133': ΠΡΟΣΩΠΙΚΟ ΤΑΧΥΔΡΟΜΕΙΩΝ
'134': ΕΚΤΕΛΕΣΤΙΚΗ ΕΞΟΥΣΙΑ
'135': ΣΥΣΤΑΣΗ ΚΑΙ ΕΔΡΑ ΤΟΥ ΚΡΑΤΟΥΣ
'136': ΦΟΡΟΛΟΓΙΑ ΔΙΑΣΚΕΔΑΣΕΩΝ
'137': ΤΗΛΕΦΩΝΑ
'138': ΣΤΡΑΤΟΛΟΓΙΑ
'139': ΕΚΠΑΙΔΕΥΣΗ ΕΡΓΑΤΩΝ
'140': ΥΠΟΥΡΓΕΙΟ ΠΟΛΙΤΙΣΜΟΥ
'141': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ
'142': ΥΠΟΥΡΓΕΙΟ ΓΕΩΡΓΙΑΣ
'143': ΣΩΜΑΤΕΙΑ
'144': ΕΙΔΙΚΕΣ ΜΟΡΦΕΣ ΑΠΑΣΧΟΛΗΣΗΣ
'145': ΥΠΟΥΡΓΕΙΟ ΔΙΚΑΙΟΣΥΝΗΣ
'146': ΝΑΥΤΙΛΙΑΚΟΙ ΟΡΓΑΝΙΣΜΟΙ
'147': ΤΟΥΡΙΣΜΟΣ
'148': ΚΑΠΝΟΣ
'149': ΠΡΟΣΤΑΣΙΑ ΗΘΩΝ
'150': ΕΙΔΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΝΑΥΤΙΚΟΥ
'151': ΑΠΟΔΟΧΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ
'152': ΠΡΟΝΟΙΑ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν
'153': ΕΙΔΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝ.ΕΤΑΙΡΕΙΩΝ
'154': ΔΗΜΟΣΙΑ ΔΙΟΙΚΗΣΗ
'155': ΤΟΠΙΚΑ ΣΧΕΔΙΑ ΠΟΛΕΩΝ
'156': ΠΡΟΣΤΑΣΙΑ ΠΑΙΔΙΚΗΣ ΗΛΙΚΙΑΣ
'157': ΕΛΛΗΝΙΚΗ ΑΣΤΥΝΟΜΙΑ
'158': ΛΙΜΕΝΙΚΟ ΣΩΜΑ
'159': ΤΟΥΡΙΣΤΙΚΗ ΑΣΤΥΝΟΜΙΑ
'160': ΒΙΟΜΗΧΑΝΙΑ
'161': ΣΧΟΛΕΣ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΑΘΗΝΩΝ
'162': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ ΣΤΡΑΤΟΥ
'163': ΑΛΥΚΕΣ
'164': ΕΣΩΤΕΡΙΚΟ ΕΜΠΟΡΙΟ
'165': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΥΓΕΙΑΣ
'166': ΝΟΜΟΘΕΤΙΚΗ ΕΞΟΥΣΙΑ
'167': ΔΙΟΙΚΗΣH ΚΟΙΝΩΝIKΗΣ ΠΡΟΝΟΙΑΣ
'168': ΠΛΗΡΩΜΑΤΑ
'169': ΜΑΘΗΤΙΚΗ ΠΡΟΝΟΙΑ
'170': ΔΙΟΙΚΗΣΗ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ
'171': ΕΠΟΙΚΙΣΜΟΣ
'172': ΤΡΟΧΙΟΔΡΟΜΟΙ
'173': ΕΠΑΓΓΕΛΜΑΤΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'174': ΑΕΡΟΠΟΡΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'175': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ
'176': ΘΕΑΤΡΟ
'177': ΥΔΡΕΥΣΗ
'178': ΔΙΕΘΝΕΙΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'179': ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ
'180': ΥΠΟΥΡΓΕΙΟ ΕΞΩΤΕΡΙΚΩΝ
'181': ΕΥΡΩΠΑΪΚΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ
'182': ΕΛΕΥΘΕΡΙΑ ΤΗΣ ΕΡΓΑΣΙΑΣ
'183': ΥΠΟΥΡΓΕΙΟ ΕΣΩΤΕΡΙΚΩΝ ΔΗΜ.ΔΙΟΙΚΗΣΗΣ ΚΑΙ ΑΠΟΚΕΝΤΡΩΣΗΣ
'184': ΔΙΑΦΟΡΕΣ ΕΝΟΧΙΚΕΣ ΣΧΕΣΕΙΣ
'185': ΛΗΞΙΑΡΧΕΙΑ
'186': ΕΙΔΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ
'187': ΤΕΛΩΝΕΙΑΚΕΣ ΣΥΜΒΑΣΕΙΣ
'188': ΝΑΥΤΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ
'189': ΣΤΕΓΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ
'190': ΠΛΗΡΩΜΑΤΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'191': ΣΥΝΤΑΓΜΑΤΙΚΟΣ ΧΑΡΤΗΣ
'192': ΗΛΕΚΤΡΙΣΜΟΣ
'193': ΑΣΦΑΛΙΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ
'194': ΛΕΣΧΕΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'195': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΑΣ TAΞΗΣ
'196': ΕΚΤΕΛΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ
'197': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ
'198': ΔΑΣΙΚΗ ΝΟΜΟΘΕΣΙΑ
'199': ΕΙΔΙΚΕΣ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ
'200': ΕΔΑΦΟΣ ΤΟΥ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ
'201': ΔΙΚΗΓΟΡΟΙ
'202': ΔΙΚΑΙΟ ΤΩΝ ΠΡΟΣΩΠΩΝ
'203': ΔΙΟΙΚΗΣΗ ΤΑΧΥΔΡΟΜΙΚΗΣ, ΤΗΛΕΓΡΑΦΙΚΗΣ
'204': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ ΚΑΙ ΤΑΜΕΙΑ
'205': ΑΕΡΟΛΙΜΕΝΕΣ
'206': ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΑ
'207': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ ΤΑΞΗΣ
'208': ΔΙΑΧΕΙΡΙΣΕΙΣ ΤΟΥ ΔΗΜΟΣΙΟΥ
'209': ΕΜΠΡΑΓΜΑΤΟ ΔΙΚΑΙΟ
'210': ΦΟΡΤΟΕΚΦΟΡΤΩΣΕΙΣ
'211': ΑΝΩΝΥΜΕΣ ΕΤΑΙΡΕΙΕΣ
'212': ΕΙΔΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ
'213': ΕΚΚΛΗΣΙΕΣ ΑΛΛΟΔΑΠΗΣ
'214': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ
'215': ΟΡΓΑΝΙΣΜΟΣ ΑΣΦΑΛΙΣΗΣ ΕΛΕΥΘΕΡΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ
'216': ΑΣΦΑΛΕΙΑ ΑΕΡΟΠΛΟΪΑΣ
'217': ΤΑΜΕΙΑ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΑΡΩΓΗΣ
'218': ΑΝΩΤΑΤΗ ΕΚΠΑΙΔΕΥΣΗ
'219': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ
'220': ΠΟΙΝΙΚΟ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ
'221': ΦΟΡΟΛΟΓΙΑ ΕΠΙΤΗΔΕΥΜΑΤΟΣ
'222': ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ
'223': ΠΟΙΝΙΚΗ ΔΙΚΟΝΟΜΙΑ
'224': ΣΤΟΙΧΕΙΩΔΗΣ ΕΚΠΑΙΔΕΥΣΗ
'225': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ ΚΑΙ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ
'226': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ
'227': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΝΑΥΤΙΚΟΥ
'228': ΤΥΠΟΣ
'229': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ
'230': ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ
'231': ΧΡΕΩΓΡΑΦΑ
'232': ΠΡΟΪΟΝΤΑ ΕΛΑΙΑΣ
'233': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ
'234': ΔΙΟΙΚΗΣH ΥΓΙΕΙΝΗΣ
'235': ΑΕΡΟΠΟΡΙΚΟ ΠΟΙΝΙΚΟ ΔΙΚΑΙΟ
'236': ΚΑΤΑΠΟΛΕΜΗΣΗ ΝΟΣΩΝ ΚΑΤ’ ΙΔΙΑΝ
'237': ΕΙΔΙΚΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ
'238': ΘΗΡΑ
'239': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ
'240': ΔΙΟΙΚΗΣΗ ΣΥΓΚΟΙΝΩΝΙΩΝ
'241': ΑΠΟΣΤΟΛΙΚΗ ΔΙΑΚΟΝΙΑ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ
'242': ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ
'243': ΤΑΧΥΔΡΟΜΙΚΑ ΤΑΜΙΕΥΤΗΡΙΑ
'244': ΑΝΩΤΑΤΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ
'245': ΔΙΟΙΚΗΣΗ ΕΡΓΑΣΙΑΣ
'246': ΑΓΙΟΝ ΟΡΟΣ
'247': ΣΧΟΛΕΣ Π. ΝΑΥΤΙΚΟΥ
'248': ΤΡΑΠΕΖΕΣ
'249': ΕΛΕΓΧΟΣ ΚΙΝΗΣΕΩΣ ΜΕ ΤΟ ΕΞΩΤΕΡΙΚΟ
'250': ΕΙΔΙΚΑΙ ΚΑΤΗΓΟΡΙΑΙ ΠΛΟΙΩΝ
'251': ΓΕΩΡΓΙΚΗ ΥΓΙΕΙΝΗ
'252': ΕΞΟΔΑ ΠΟΙΝΙΚΗΣ ΔΙΑΔΙΚΑΣΙΑΣ
'253': ΕΡΓΑΣΙΑ ΓΥΝΑΙΚΩΝ ΚΑΙ ΑΝΗΛΙΚΩΝ
'254': ΔΙΟΙΚΗΣΗ ΕΦΟΔΙΑΣΜΟΥ
'255': ΕΜΠΟΡΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ
'256': ΕΚΤΕΛΩΝΙΣΤΕΣ
'257': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ ΚΛΠ
'258': ΟΡΓΑΝΙΣΜΟΙ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ
'259': ΕΝΙΣΧΥΣΗ ΕΠΙΣΤΗΜΩΝ ΚΑΙ ΤΕΧΝΩΝ
'260': ΔΙΑΦΟΡΟΙ ΦΟΡΟΛΟΓΙΚΟΙ ΝΟΜΟΙ
'261': ΤΕΧΝΙΚΕΣ ΠΡΟΔΙΑΓΡΑΦΕΣ
'262': ΜΗΤΡΩΑ ΔΗΜΟΤΩΝ
'263': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'264': ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'265': ΥΓΕΙΟΝΟΜΙΚΗ ΑΝΤΙΛΗΨΗ
'266': ΤΕΛΗ ΧΑΡΤΟΣΗΜΟΥ
'267': ΣΤΡΑΤΙΩΤΙΚΟΙ ΓΕΝΙΚΑ
'268': ΛΙΜΕΝΙΚΕΣ ΑΡΧΕΣ
'269': ΕΛΕΓΧΟΣ ΚΥΚΛΟΦΟΡΙΑΣ
'270': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΣ ΚΑΙ ΑΥΤΑΣΦΑΛΙΣΕΩΣ ΥΓΕΙΟΝΟΜΙΚΩΝ
'271': ΠΟΛΙΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ
'272': ΤΗΛΕΓΡΑΦΟΙ
'273': ΣΕΙΣΜΟΠΛΗΚΤΟΙ
'274': ΙΑΜΑΤΙΚΕΣ ΠΗΓΕΣ
'275': ΙΔΙΩΤΙΚΟ ΝΑΥΤΙΚΟ ΔΙΚΑΙΟ
'276': ΔΙΕΘΝΕΙΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'277': ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ
'278': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ
'279': ΠΡΟΣΤΑΣΙΑ ΝΟΜΙΣΜΑΤΟΣ
'280': ΠΡΟΣΤΑΣΙΑ ΠΡΟΪΟΝΤΩΝ ΑΜΠΕΛΟΥ
'281': ΑΝΑΠΗΡΟΙ ΚΑΙ ΘΥΜΑΤΑ ΠΟΛΕΜΟΥ
'282': ΠΑΡΟΧΕΣ ΔΙΑΦΟΡΕΣ
'283': ΤΟΠΙΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ
'284': OΡΓΑΝΩΣΗ ΣΤΡΑΤΟΥ ΞΗΡΑΣ
'285': ΔΙΑΚΟΠΕΣ ΤΗΣ ΕΡΓΑΣΙΑΣ
'286': ΟΡΓΑΝΙΣΜΟΣ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'287': ΕΠΙΜΕΛΗΤΗΡΙΑ
'288': ΕΚΚΛΗΣΙΑ ΤΗΣ ΕΛΛΑΔΟΣ
'289': ΝΑΡΚΩΤΙΚΑ
'290': ΕΚΜΕΤΑΛΛΕΥΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ
'291': ΜΟΥΣΙΚΗ
'292': ΝΟΜΑΡΧΙΕΣ
'293': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'294': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ
'295': ΚΡΑΤΙΚΗ
'296': ΔΙΟΙΚΗΣΗ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'297': ΠΑΡΟΧΟΙ ΣΤΑΘΕΡΩΝ ΗΛΕΚΤΡΟΝΙΚΩΝ ΕΠΙΚΟΙΝΩΝΙΩΝ
'298': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΚΙΝΔΥΝΟΣ
'299': ΕΝΟΧΕΣ ΣΕ ΧΡΥΣΟ ΚΑΙ ΣΥΝΑΛΛΑΓΜΑ
'300': ΙΠΠΟΠΑΡΑΓΩΓΗ
'301': ΑΥΤΟΚΙΝΗΤΑ
'302': ΑΓΟΡΑΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'303': ΠΡΟΣΦΥΓΕΣ
'304': ΔΙΑΦΟΡΑ ΣΤΡΑΤΙΩΤΙΚΑ ΘΕΜΑΤΑ
'305': ΓΕΝ. ΓΡΑΜΜ. ΒΙΟΜΗΧΑΝΙΑΣ - ΓΕΝ. ΓΡΑΜΜ. ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ
'306': ΔΙΑΜΕΤΑΚΟΜΙΣΗ
'307': ΔΙΚΑΙΟΣΤΑΣΙΟ
'308': ΥΔΑΤΑ
'309': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΚΑΙ ΑΠΑΛΛΑΓΕΣ
'310': ΜΟΝΟΠΩΛΙΑ
'311': ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ
'312': ΠΡΟΝΟΙΑ ΓΙΑ ΤΟΥΣ ΣΤΡΑΤΙΩΤΙΚΟΥΣ
'313': ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ
'314': ΟΡΓΑΝΩΣΗ ΧΡΟΝΟΥ ΕΡΓΑΣΙΑΣ
'315': ΠΡΟΣΩΠΙΚΟ ΤΥΠΟΥ
'316': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ
'317': ΛΟΥΤΡΟΠΟΛΕΙΣ
'318': ΤΕΛΩΝΕΙΑΚΟΣ ΚΩΔΙΚΑΣ
'319': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΝΟΜΙΚΩΝ
'320': ΔΙΑΦΟΡΟΙ ΤΕΛΩΝΕΙΑΚΟΙ ΝΟΜΟΙ
'321': ΔΙΟΙΚΗΣΗ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'322': ΑΕΡΟΠΟΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ
'323': ΕΜΠΟΡΙΚΕΣ ΠΡΑΞΕΙΣ
'324': ΔΙΚΑΣΤΗΡΙΑ
'325': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ
'326': ΠΡΟΣΩΠΙΚΟ ΠΟΛΕΜΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'327': ΠΡΟΣΤΑΣΙΑ ΚΑΙ ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ
'328': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ
'329': ΣΙΔΗΡΟΔΡΟΜΟΙ ΓΕΝΙΚΑ
'330': ΠΝΕΥΜΑΤΙΚΗ ΙΔΙΟΚΤΗΣΙΑ
'331': ΔΙΑΦΟΡΑ ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ
'332': ΥΓΕΙΟΝΟΜΙΚΑ ΕΠΑΓΓΕΛΜΑΤΑ
'333': ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ
'334': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ
'335': ΧΩΡΟΦΥΛΑΚΗ
'336': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ
'337': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΑΤΡΩΝ
'338': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ
'339': ΑΣΦΑΛΙΣΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ
'340': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ
'341': ΥΠΟΝΟΜΟΙ
'342': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ
'343': ΕΤΑΙΡΕΙΕΣ ΠΕΡΙΩΡΙΣΜΕΝΗΣ ΕΥΘΥΝΗΣ
'344': ΥΠΟΥΡΓΕΊΟ ΚΟΙΝΩΝΙΚΏΝ ΑΣΦΑΛΊΣΕΩΝ
'345': ΣΥΜΒΟΛΑΙΟΓΡΑΦΟΙ
'346': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ
'347': ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'348': ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ
'349': ΔΙΑΦΟΡΑ ΕΠΙΣΤΗΜΟΝΙΚΑ ΙΔΡΥΜΑΤΑ
'350': ΑΞΙΩΜΑΤΙΚΟΙ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'351': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε)
'352': ΣΤΡΑΤΙΩΤΙΚΗ ΠΟΙΝΙΚΗ
'353': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΠΝΕΥΜΑΤΟΣ
'354': ΟΡΓΑΝΙΣΜΟΣ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ
'355': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ ΕΡΓΑΣΙΑΣ
'356': ΧΡΗΜΑΤΙΣΤΗΡΙΑ
'357': ΠΟΛΙΤΙΚΑΙ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΑΙ ΣΥΝΤΑΞΕΙΣ
'358': ΚΟΙΝΩΝΙΚΗ ΣΤΕΓΑΣΤΙΚΗ ΣΥΝΔΡΟΜΗ
'359': ΚΑΤΟΧΥΡΩΣΗ ΕΠΑΓΓΕΛΜΑΤΩΝ
'360': ΦΟΡΟΛΟΓΙΑ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ
'361': ΠΕΡΙΦΕΡΕΙΕΣ
'362': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΔΙΚΑΙΟΣΥΝΗ
'363': ΥΠΟΥΡΓΕΙΟ ΟΙΚΟΝΟΜΙΚΩΝ
'364': ΕΘΝΙΚΑ ΚΛΗΡΟΔΟΤΗΜΑΤΑ
'365': ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ
'366': ΛΙΜΕΝΕΣ
'367': ΦΥΛΑΚΕΣ
'368': ΓΕΩΡΓΙΚΗ ΕΚΠΑΙΔΕΥΣΗ
'369': ΠΛΗΡΩΜΗ ΕΡΓΑΣΙΑΣ
'370': ΕΜΠΟΡΙΚΟΣ ΝΟΜΟΣ
'371': ΙΔΡΥΜΑ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ
'372': ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ ΤΡΑΠΕΖΩΝ
'373': ΕΙΔΙΚΟΙ ΑΓΡΟΤΙΚΟΙ ΝΟΜΟΙ
'374': ΔΙΕΘΝΕΙΣ ΔΙΚΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'375': ΥΠΟΥΡΓΕΙΑ ΜΑΚΕΔΟΝΙΑΣ–ΘΡΑΚΗΣ, ΑΙΓΑΙΟΥ Κ.Λ.Π
'376': ΑΣΤΥΝΟΜΙΚΟΊ ΣΚΎΛΟΙ
'377': ΔΙΑΦΟΡΑ ΘΕΜΑΤΑ
'378': ΕΚΔΟΣΗ ΕΓΚΛΗΜΑΤΙΩΝ
'379': ΑΓΟΡΑΝΟΜΙΑ
'380': ΔΙΚΑΣΤΙΚΟ ΤΟΥ ΔΗΜΟΣΙΟΥ
'381': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ
'382': ΤΕΛΩΝΕΙΑΚΕΣ ΑΤΕΛΕΙΕΣ
'383': ΑΓΡΟΤΙΚΕΣ ΜΙΣΘΩΣΕΙΣ
'384': ΛΕΩΦΟΡΕΙΑ
'385': ΓΕΝΙΚΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ
'386': ΑΣΤΥΝΟΜΙΑ ΠΟΛΕΩΝ
'387': ΜΗΧΑΝΙΚΟΙ ΚΑΙ ΕΡΓΟΛΑΒΟΙ
'388': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ
splits:
- name: train
num_bytes: 216757887
num_examples: 28536
- name: test
num_bytes: 71533786
num_examples: 9516
- name: validation
num_bytes: 68824457
num_examples: 9511
download_size: 45606292
dataset_size: 357116130
- config_name: subject
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ Π.Ν
'1': ΜΕΤΑΝΑΣΤΕΥΣΗ ΣΤΟ ΒΕΛΓΙΟ
'2': ΝΑΥΤΙΚΕΣ ΦΥΛΑΚΕΣ
'3': ΚΑΝΟΝΙΣΜΟΣ ΕΚΤΕΛΕΣΕΩΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΡΓΩΝ
'4': ΔΙΟΙΚΗΤΙΚΗ ΚΑΙ ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ
'5': ΑΣΚΗΣΗ ΠΟΙΝΙΚΗΣ ΑΓΩΓΗΣ
'6': ΚΑΝΟΝΙΣΜΟΣ ΕΣΩΤΕΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ
'7': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΠΑΛΑΙΟΣ
'8': ΚΑΤΑΣΤΑΤΙΚΟ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΕΜΠΟΡΩΝ (Τ.Α.Ε)
'9': ΜΗΧΑΝΟΛΟΓΟΙ, ΗΛΕΚΤΡΟΛΟΓΟΙ, ΝΑΥΠΗΓΟΙ ΚΑΙ ΜΗΧΑΝΟΔΗΓΟΙ
'10': ΣΤΕΓΑΣΗ ΠΑΡΑΠΗΓΜΑΤΟΥΧΩΝ
'11': ΝΟΜΙΣΜΑΤΙΚΗ ΕΠΙΤΡΟΠΗ
'12': ΠΕΡΙΦΕΡΕΙΑΚΑ ΤΑΜΕΙΑ
'13': ΜΗΤΡΩΑ ΑΡΡΕΝΩΝ
'14': ΔΙΚΑΣΤΙΚΕΣ ΔΙΑΚΟΠΕΣ
'15': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΠΡΟΞΕΝΙΚΩΝ ΣΧΕΣΕΩΝ
'16': ΠΑΛΑΙΟΙ ΑΣΤΙΚΟΙ ΚΩΔΙΚΕΣ
'17': ΚΛΑΔΟΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΔΙΚΗΓΟΡΩΝ (Κ.Ε.Α.Δ.)
'18': ΟΙΚΟΝΟΜΙΚΕΣ ΑΡΜΟΔΙΟΤΗΤΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΡΧΩΝ
'19': ΥΠΟΝΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ
'20': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΤΑΜΕΙΑ
'21': ΕΛΕΓΧΟΣ ΘΕΑΤΡΙΚΩΝ ΕΡΓΩΝ ΚΑΙ ΔΙΣΚΩΝ
'22': ΥΠΗΡΕΣΙΑ ΙΠΠΟΠΑΡΑΓΩΓΗΣ
'23': ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ
'24': ΕΚΔΙΚΑΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΠΑΡΑΒΑΣΕΩΝ
'25': ΚΙΝΗΤΡΑ ΙΔΙΩΤΙΚΩΝ ΕΠΕΝΔΥΣΕΩΝ ΣΤΗΝ ΠΕΡΙΦΕΡΕΙΑ
'26': ΜΕΛΗ ΟΙΚΟΓΕΝΕΙΑΣ ΑΣΦΑΛΙΣΜΕΝΩΝ
'27': ΚΕΡΜΑΤΑ
'28': ΕΠΙΔΟΜΑ ΑΝΑΠΡΟΣΑΡΜΟΓΗΣ
'29': ΕΚΤΕΛΕΣΗ ΔΑΣΙΚΩΝ ΕΡΓΩΝ
'30': ΛΙΠΑΣΜΑΤΑ
'31': ΕΠΙΧΟΡΗΓΗΣΗ ΣΠΟΥΔΑΣΤΩΝ ΤΕΚΝΩΝ ΕΡΓΑΖΟΜΕΝΩΝ
'32': ΠΡΟΣΤΑΣΙΑ ΟΙΝΟΥ
'33': ΠΤΗΤΙΚΟ ΚΑΙ ΚΑΤΑΔΥΤΙΚΟ ΕΠΙΔΟΜΑ
'34': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ (Τ.Ε.Α.Υ.Ε.Κ.)
'35': ΕΚΚΟΚΚΙΣΗ ΒΑΜΒΑΚΟΣ
'36': ΜΟΝΟΠΩΛΙΟ ΚΙΝΙΝΟΥ
'37': ΙΝΣΤΙΤΟΥΤΑ ΔΙΕΘΝΟΥΣ ΔΙΚΑΙΟΥ
'38': ΙΑΠΩΝΙΑ – ΙΝΔΙΑ –ΙΟΡΔΑΝΙΑ Κ.ΛΠ
'39': ΕΠΙΔΟΜΑ ΣΤΟΛΗΣ
'40': ΑΝΑΓΝΩΡΙΣΕΙΣ
'41': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΟΛΗΠΤΩΝ
'42': ΑΝΑΣΤΟΛΗ ΤΗΣ ΠΟΙΝΗΣ
'43': ΠΟΤΑΜΟΠΛΟΙΑ
'44': ΕΙΔΙΚΗ ΤΕΛΩΝΕΙΑΚΗ ΠΑΡΑΚΟΛΟΥΘΗΣΗ
'45': ΕΠΙΘΕΩΡΗΣΗ ΦΑΡΜΑΚΕΙΩΝ
'46': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΩΝ
'47': ΑΠΛΟΠΟΙΗΣΗ ΤΕΛΩΝΕΙΑΚΩΝ ΔΙΑΤΥΠΩΣΕΩΝ
'48': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Α.Κ.Ε
'49': ΥΠΗΡΕΣΙΑ ΥΠΟΔΟΧΗΣ ΠΛΟΙΩΝ ΚΑΙ ΠΟΛΕΜΙΚΗ ΧΡΗΣΗ ΛΙΜΕΝΩΝ
'50': ΦΑΡΜΑΚΕΙΟ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'51': ΤΑΜΕΙΟ ΑΠΟΚΑΤΑΣΤΑΣΕΩΣ ΠΡΟΣΦΥΓΩΝ ΣΥΜΒΟΥΛΙΟΥ ΤΗΣ ΕΥΡΩΠΗΣ
'52': ΝΑΥΤΙΚΕΣ ΕΤΑΙΡΕΙΕΣ
'53': ΙΣΡΑΗΛΙΤΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ
'54': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΣΤΕΡΕΑΣ ΕΛΛΑΔΑΣ (ΑΤΤΙΚΗΣ, ΒΟΙΩΤΙΑΣ Κ.Λ.Π.)
'55': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ Π.Ν
'56': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡ.ΚΑΙ ΒΙΟΜ.- ΕΠΑΓΓΕΛ. ΚΑΙ ΒΙΟΤΕΧΝ.
ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΤΟΥ ΚΡΑΤΟΥΣ
'57': ΕΘΝΙΚΗ ΚΤΗΜΑΤΙΚΗ ΤΡΑΠΕΖΑ
'58': ΝΑΥΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ
'59': ΔΗΜΟΣΙΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ
'60': ΜΙΚΡΟΦΩΤΟΓΡΑΦΙΕΣ
'61': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ-Τ.Σ.Α.Υ
'62': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'63': ΕΛΛΗΝΙΚΑ ΣΧΟΛΕΙΑ ΑΛΛΟΔΑΠΗΣ
'64': ΟΡΓΑΝΙΣΜΟΣ ΟΙΚΟΝΟΜΙΚΗΣ
'65': ΕΘΝΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ
'66': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ν.Π.Δ.Δ
'67': ΠΡΟΣΩΠΙΚΟ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ
'68': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΥΔΡΕΥΣΗΣ ΚΑΙ ΑΠΟΧΕΤΕΥΣΗΣ
ΠΡΩΤΕΥΟΥΣΗΣ (Τ.Ε.Α.Π.Ε.Υ.Α.Π.)
'69': ΣΩΜΑ ΟΙΚΟΝΟΜΙΚΟΥ ΕΛΕΓΧΟΥ
'70': ΣΥΜΒΑΣΗ ΠΕΡΙ ΔΙΕΚΔΙΚΗΣΕΩΣ ΔΙΑΤΡΟΦΗΣ
'71': ΙΣΟΤΗΤΑ ΤΩΝ ΔΥΟ ΦΥΛΩΝ
'72': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ
'73': ΤΟΥΡΙΣΤΙΚΟ ΔΕΛΤΙΟ
'74': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ
'75': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΠΕΙΡΑΙΩΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ
'76': ΕΚΚΑΘΑΡΙΣΙΣ ΔΙΟΡΙΣΜΩΝ ΚΑΙ ΠΡΟΑΓΩΓΩΝ ΚΑΤΟΧΗΣ
'77': ΤΑΞΙΝΟΜΗΣΗ ΒΑΜΒΑΚΟΣ
'78': ΠΡΥΤΑΝΕΙΣ ΚΑΙ ΚΟΣΜΗΤΟΡΕΣ
'79': ΥΠΗΡΕΣΙΑΚΟ ΣΥΜΒΟΥΛΙΟ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'80': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΤΗΝ ΒΙΟΜΗΧΑΝΙΑ ΚΑΙ ΒΙΟΤΕΧΝΙΑ
'81': ΧΑΡΤΗΣ ΟΡΓΑΝΙΣΜΟΥ ΟΙΚΟΝΟΜΙΚΗΣ ΣΥΝΕΡΓΑΣΙΑΣ
'82': ΓΥΜΝΑΣΙΟ ΑΠΟΔΗΜΩΝ ΕΛΛΗΝΟΠΑΙΔΩΝ
'83': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ
'84': ΕΚΔΟΣΕΙΣ ΥΠΟΥΡΓΕΙΟΥ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ
'85': ΠΛΗΤΤΟΜΕΝΟΙ ΑΠΟ ΘΕΟΜΗΝΙΕΣ ΚΑΙ ΑΛΛΑ ΕΚΤΑΚΤΑ ΓΕΓΟΝΟΤΑ
'86': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ
'87': ΓΕΩΜΗΛΑ
'88': ΦΟΡΟΛΟΓΙΑ ΑΝΑΤΙΜΗΣΗΣ ΑΚΙΝΗΤΩΝ
'89': ΠΑΝΩΛΗΣ
'90': ΣΧΟΛΕΣ ΝΗΠΙΑΓΩΓΩΝ
'91': ΦΑΡΜΑΚΑΠΟΘΗΚΕΣ
'92': ΦΡΟΝΤΙΣΤΗΡΙΑ ΝΟΜΙΚΩΝ ΣΠΟΥΔΩΝ
'93': ΟΙΚΟΓΕΝΕΙΑΚΑ ΕΠΙΔΟΜΑΤΑ ΜΙΣΘΩΤΩΝ
'94': ΗΛΕΚΤΡΟΚΙΝΗΤΑ ΛΕΩΦΟΡΕΙΑ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ (Η.Λ.Π.Α.Π.)
'95': ΑΣΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΛΛΟΔΑΠΩΝ
'96': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ
'97': ΔΙΚΑΣΤΙΚΗ ΕΚΠΡΟΣΩΠΗΣΗ Ι.Κ.Α
'98': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π.Σ
'99': ΥΓΕΙΟΝΟΜΙΚΟΙ ΣΤΑΘΜΟΙ
'100': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ ΚΑΙ ΑΝΘΥΠΑΣΠΙΣΤΩΝ
'101': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΔΕΡΜΑΤΟΣ
ΕΛΛΑΔΑΣ (Τ.Ε.Α.Ε.Υ.Δ.Ε.)
'102': ΠΡΑΤΗΡΙΑ ΑΡΤΟΥ
'103': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΗ
'104': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΙΚΟΠΤΕΡΩΝ
'105': ΔΙΕΘΝΕΙΣ ΤΑΧΥΔΡΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'106': ΔΙΚΑΣΤΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ ΤΟΥ ΔΗΜΟΣΙΟΥ
'107': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΔΙΑΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΑ
'108': ΔΙΕΥΘΥΝΣΗ ΚΤΗΝΟΤΡΟΦΙΑΣ
'109': ΕΠΙΘΕΩΡΗΣΗ ΣΦΑΓΙΩΝ
'110': ΠΛΩΙΜΟΤΗΤΑ ΑΕΡΟΣΚΑΦΩΝ
'111': ΑΓΟΡΑΝΟΜΙΚΟΣ ΚΩΔΙΚΑΣ
'112': ΔΙΕΘΝΕΙΣ ΜΕΤΑΦΟΡΕΣ ΕΠΙΒΑΤΩΝ ΚΑΙ ΕΜΠΟΡΕΥΜΑΤΩΝ
'113': ΠΡΟΜΗΘΕΙΕΣ
'114': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ
'115': ΔΙΑΙΤΗΣΙΑ ΣΥΛΛΟΓΙΚΩΝ ΔΙΑΦΟΡΩΝ - ΜΕΣΟΛΑΒΗΤΕΣ ΔΙΑΙΤΗΤΕΣ
'116': ΣΟΥΛΤΑΝΙΝΑ
'117': ΜΕΤΑΓΡΑΦΗ
'118': ΕΙΣΑΓΩΓΗ ΕΠΙΣΤΗΜΟΝΙΚΟΥ ΥΛΙΚΟΥ
'119': ΔΙΑΡΘΡΩΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Γ.Α
'120': ΔΙΚΑΣΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΙΚΑΣΤΩΝ
'121': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΚΑΙ ΔΙΚΑΙΟΛΟΓΗΤΙΚΑ
'122': ΑΣΚΗΣΗ ΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ
'123': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'124': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ
'125': ΑΛΛΟΔΑΠΕΣ ΝΑΥΤΙΛΙΑΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ
'126': ΛΑΤΟΜΕΙΑ
'127': ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ
'128': ΠΩΛΗΣΗ ΧΡΕΩΓΡΑΦΩΝ ΜΕ ΔΟΣΕΙΣ
'129': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΤΡΑΠΕΖΩΝ (ΓΕΝΙΚΑ)
'130': ΕΙΔΙΚΑ ΜΕΤΑΛΛΕΙΑ
'131': YΠΟΥΡΓΕΙΟ ΥΓΙΕΙΝΗΣ
'132': ΛΗΞΙΑΡΧΙΚΕΣ ΠΡΑΞΕΙΣ
'133': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟΝ ΤΥΠΟ
'134': ΕΘΝΙΚΟ ΣΥΣΤΗΜΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-ΚΑΤΑΡΤΙΣΗΣ
'135': ΑΡΟΥΡΑΙΟΙ ΚΑΙ ΑΚΡΙΔΕΣ
'136': ΠΡΟΣΤΑΣΙΑ ΦΥΜΑΤΙΚΩΝ ΝΑΥΤΙΚΩΝ
'137': ΑΠΟΡΡΗΤΟ ΕΠΙΣΤΟΛΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ
'138': ΠΟΡΘΜΕΙΑ ΚΑΙ ΟΧΗΜΑΤΑΓΩΓΑ
'139': ΜΕΤΡΑ ΕΞΟΙΚΟΝΟΜΗΣΗΣ ΕΝΕΡΓΕΙΑΣ
'140': ΣΤΟΙΧΕΙΑ ΠΡΟΣΩΠΙΚΟΥ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ ΚΑΙ Ν.Π.Δ.Δ
'141': ΠΑΓΙΕΣ ΑΜΟΙΒΕΣ ΔΙΚΗΓΟΡΩΝ
'142': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΗΣ ΕΥΕΛΠΙΔΩΝ
'143': ΟΙΚΟΝΟΜΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ
'144': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΕΡΓΑΣΙΑΣ
'145': ΔΙΑΦΗΜΙΣΕΙΣ
'146': ΔΙΑΦΟΡΕΣ ΥΠΟΤΡΟΦΙΕΣ
'147': ΦΟΡΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ (ΜS) ΜΕΧΡΙ 500 Κ.Ο.Χ
'148': ΕΠΙΤΡΟΠΗ ΣΥΝΕΡΓΑΣΙΑΣ UNICEF
'149': ΥΓΙΕΙΝΗ ΘΕΡΕΤΡΩΝ
'150': ΕΠΙΣΤΗΜΟΝΙΚΗ ΕΡΕΥΝΑ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑ
'151': ΑΠΑΓΟΡΕΥΣΕΙΣ ΕΞΑΓΩΓΗΣ
'152': ΑΜΠΕΛΟΥΡΓΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ
'153': ΥΠΟΥΡΓΕΙΟ ΥΓΕΙΑΣ ΚΑΙ ΠΡΟΝΟΙΑΣ
'154': ΔΙΕΘΝΗΣ ΝΑΥΤΙΛΙΑΚΟΣ ΟΡΓΑΝΙΣΜΟΣ
'155': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΑΚΟΥ ΕΛΕΓΧΟΥ
'156': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ Π. ΝΑΥΤΙΚΟΥ
'157': ΑΝΩΤΑΤΗ ΥΓΕΙΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ
'158': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ ΚΑΙ ΑΓΩΝΙΣΤΩΝ ΕΘΝ.
ΑΝΤΙΣΤΑΣΗΣ
'159': ΦΟΡΟΙ ΥΠΕΡ ΤΡΙΤΩΝ
'160': ΑΓΡΟΛΗΨΙΕΣ ΙΟΝΙΩΝ ΝΗΣΙΩΝ
'161': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΜΠΟΡΙΟΥ ΤΡΟΦΙΜΩΝ (Τ.Ε.Α.Υ.Ε.Τ)
'162': ΑΝΩΤΑΤΟ ΕΙΔΙΚΟ ΔΙΚΑΣΤΗΡΙΟ
'163': ΕΙΣΑΓΩΓΗ ΓΥΝΑΙΚΩΝ ΣΤΙΣ ΑΝΩΤΑΤΕΣ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΧΟΛΕΣ
'164': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΝΟΣΗΛΕΥΤΙΚΗΣ (Σ.Α.Ν.)
'165': ΔΙΑΔΙΚΑΣΙΑ ΔΙΟΙΚΗΤΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ
'166': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΟΥ ΠΑΙΔΙΟΥ
'167': ΑΜΝΗΣΤΙΑ
'168': ΣΧΟΛΕΣ ΚΑΛΛΙΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'169': ΧΑΡΗ ΚΑΙ ΜΕΤΡΙΑΣΜΟΣ
'170': ΤΥΦΛΟΙ
'171': ΣΥΜΒΟΥΛΙΟ ΤΗΣ ΕΥΡΩΠΗΣ
'172': ΕΡΓΟΣΤΑΣΙΑ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ
'173': ΜΗΤΡΩΑ Π. ΝΑΥΤΙΚΟΥ
'174': ΥΓΡΗ ΑΜΜΩΝΙΑ
'175': ΠΕΙΡΑΜΑΤΙΚΑ ΣΧΟΛΕΙΑ
'176': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Ε.Ν
'177': ΕΠΑΓΓΕΛΜΑΤΙΚΟΣ ΠΡΟΣΑΝΑΤΟΛΙΣΜΟΣ ΚΑΙ ΚΑΤΑΡΤΙΣΗ
'178': ΤΕΛΩΝΕΙΑΚΗ ΕΠΙΒΛΕΨΗ
'179': ΠΡΟΣΩΡΙΝΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ
'180': ΜΟΝΟΠΩΛΙΟ ΠΑΙΓΝΙΟΧΑΡΤΩΝ
'181': ΛΕΙΤΟΥΡΓΙΑ ΤΟΥΡΙΣΤΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'182': ΕΚΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΩΝ ΚΙΝΗΤΩΝ ΚΑΙ ΑΚΙΝΗΤΩΝ
'183': ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ (ΓΕΝΙΚΑ)
'184': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΕΚΤΟΣ ΕΔΡΑΣ
'185': ΣΤΕΓΑΣΤΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ
'186': ΑΝΩΤΑΤΑ ΣΥΜΒΟΥΛΙΑ ΕΚΠΑΙΔΕΥΣΕΩΣ
'187': ΑΡΧΕΙΑ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ
'188': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΥΠΟΥΡΓΙΚΟΥ ΣΥΜΒΟΥΛΙΟΥ
'189': ΠΕΡΙΠΤΕΡΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ
'190': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ ΕΜΠΟΡΩΝ, ΒΙΟΤΕΧΝΩΝ ΚΑΙ ΛΟΙΠΩΝ ΕΠΑΓΓΕΛΜΑΤΙΩΝ
'191': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΠΑΡΑΓΩΓΗΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
'192': ΘΕΑΤΡΙΚΑ ΕΡΓΑ
'193': ΜΕ ΤΗ ΝΕΑ ΖΗΛΑΝΔΙΑ
'194': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΣΑΚΧΑΡΕΩΣ
'195': ΝΟΜΑΡΧΙΑΚΑ ΤΑΜΕΙΑ
'196': ΑΓΩΓΕΣ ΚΑΚΟΔΙΚΙΑΣ
'197': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ
'198': ΑΤΟΜΑ ΒΑΡΙΑ ΝΟΗΤΙΚΑ ΚΑΘΥΣΤΕΡΗΜΕΝΑ
'199': ΜΕ ΤΗ ΣΟΥΗΔΙΑ
'200': ΑΕΡΟΝΑΥΤΙΚΗ ΜΕΤΕΩΡΟΛΟΓΙΑ
'201': ΙΔΙΩΤΙΚΕΣ ΣΧΟΛΕΣ ΓΥΜΝΑΣΤΙΚΗΣ
'202': ΠΕΡΙΟΥΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'203': ΑΓΟΡΑΠΩΛΗΣΙΕΣ ΚΑΤΟΧΗΣ
'204': ΕΚΚΛΗΣΙΑ ΠΑΡΙΣΙΩΝ
'205': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ ΦΥΤΩΝ
'206': ΚΑΤΟΧΥΡΩΣΗ ΘΡΗΣΚΕΥΤΙΚΗΣ ΕΛΕΥΘΕΡΙΑΣ
'207': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΜΗ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ
'208': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΠΟΛΕΜΟΥ 1940
'209': ΥΔΡΑΥΛΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ
'210': ΚΟΙΝΩΝΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ - ΚΟΙΝΩΝΙΚΟΙ ΣΥΜΒΟΥΛΟΙ
'211': ΔΙΑΦΟΡΕΣ ΠΡΟΣΩΡΙΝΕΣ ΑΤΕΛΕΙΕΣ
'212': ΟΙΚΟΝΟΜΙΚΗ ΔΙΑΧΕΙΡΙΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ
'213': ΕΞΗΛΕΚΤΡΙΣΜΟΣ ΝΗΣΩΝ
'214': ΕΚΠΑΙΔΕΥΣΗ ΣΤΕΛΕΧΩΝ
'215': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΑΤΑΣΤΗΜΑΤΩΝ ΚΑΙ ΓΡΑΦΕΙΩΝ
'216': ΗΜΕΡΟΛΟΓΙΟ ΓΕΦΥΡΑΣ
'217': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΣΤΑΦΙΔΑΣ
'218': ΠΑΛΑΙΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ
'219': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ
(Τ.Ε.Α.Π.Ο.Κ.Α.)
'220': ΠΑΡΟΧΕΣ ΥΓΕΙΑΣ ΑΣΦΑΛΙΣΤΙΚΩΝ ΟΡΓΑΝΙΣΜΩΝ
'221': ΠΛΑΝΟΔΙΟΙ ΙΧΘΥΟΠΩΛΕΣ
'222': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΠΕΡΙ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'223': ΥΠΟΧΡΕΩΣΕΙΣ ΕΦΟΠΛΙΣΤΩΝ ΣΕ ΑΣΘΕΝΕΙΑ Η ΘΑΝΑΤΟ ΝΑΥΤΙΚΩΝ
'224': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ
'225': ΓΕΝΙΚΑ ΠΕΡΙ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ
'226': ΕΞΑΙΡΕΣΕΙΣ ΑΠΟ ΤΗΝ ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ
'227': ΑΓΡΟΤΙΚΟ ΚΤΗΜΑΤΟΛΟΓΙΟ
'228': ΣΥΝΤΑΓΜΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ
'229': ΠΑΝΑΓΙΟΣ ΤΑΦΟΣ
'230': ΣΥΝΕΡΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ
'231': ΕΠΙΘΕΩΡΗΣΙΣ ΣΤΡΑΤΟΥ
'232': ΣΥΝΘΕΣΗ ΠΛΗΡΩΜΑΤΩΝ
'233': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΕΣΤΙΑΣ
'234': ΔΙΑΦΟΡΑ ΥΔΡΑΥΛΙΚΑ ΕΡΓΑ
'235': ΔΙΚΑΙΩΜΑ ΤΟΥ ΣΥΝΕΡΧΕΣΘΑΙ
'236': ΚΟΙΝΩΝΙΚΟΠΟΙΗΣΗ - ΑΠΟΚΡΑΤΙΚΟΠΟΙΗΣΗ ΕΠΙΧΕΙΡΗΣΕΩΝ ΔΗΜΟΣΙΟΥ ΧΑΡΑΚΤΗΡΑ
'237': ΛΑΙΚΗ ΚΑΤΟΙΚΙΑ
'238': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ
'239': ΤΕΧΝΙΚΗ ΥΠΗΡΕΣΙΑ
'240': ΜΕΤΕΚΠΑΙΔΕΥΣΗ ΔΗΜΟΔΙΔΑΣΚΑΛΩΝ
'241': ΣΥΝΤΑΞΕΙΣ ΥΠΟΥΡΓΩΝ ΚΑΙ ΒΟΥΛΕΥΤΩΝ
'242': ΟΡΙΟ ΗΛΙΚΙΑΣ
'243': ΣΤΡΑΤΙΩΤΙΚΕΣ ΠΡΟΜΗΘΕΙΕΣ
'244': ΑΠΟΣΤΟΛΑΙ ΕΞΩΤΕΡΙΚΟΥ
'245': ΦΟΡΟΛΟΓΙΑ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ
'246': ΧΡΟΝΟΣ ΕΡΓΑΣΙΑΣ - ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'247': ΝΑΥΤΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ
'248': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΚΑΙ ΛΟΓΙΣΤΙΚΟ
'249': ΔΑΣΜΟΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'250': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΧΡΗΜΑΤΙΣΤΩΝ ,ΜΕΣΙΤΩΝ,ΑΝΤΙΚΡΥΣΤΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ
ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΘΗΝΩΝ (Τ.Α.Χ.Μ.Α.)
'251': ΚΡΑΤΙΚΗ ΣΧΟΛΗ ΟΡΧΗΣΤΙΚΗΣ ΤΕΧΝΗΣ
'252': ΕΘΝΙΚΗ ΛΥΡΙΚΗ ΣΚΗΝΗ
'253': ΑΕΡΟΝΑΥΤΙΚΕΣ ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ
'254': ΚΕΝΤΡΟ ΒΙΟΤΕΧΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ
'255': ΑΡΧΑΙΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ
'256': ΥΠΕΡΩΚΕΑΝΕΙΑ
'257': ΔΑΣΗ
'258': ΑΣΚΗΣΗ ΚΤΗΝΙΑΤΡΙΚΟΥ ΕΠΑΓΓΕΛΜΑΤΟΣ
'259': ΚΤΗΣΗ ΚΑΙ ΑΠΩΛΕΙΑ
'260': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ
'261': ΑΕΡΟΛΙΜΕΝΑΣ ΑΘΗΝΩΝ
'262': ΠΡΩΤΟΒΑΘΜΙΑ ΕΚΠΑΙΔΕΥΣΗ
'263': ΣΤΕΛΕΧΟΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ
'264': ΠΤΩΧΕΥΣΗ ΚΑΙ ΣΥΜΒΙΒΑΣΜΟΣ
'265': ΠΟΛΙΤΙΚΟΣ ΓΑΜΟΣ
'266': ΙΔΙΩΤΙΚΗ ΕΠΙΧΕΙΡΗΣΗ ΑΣΦΑΛΙΣΕΩΣ
'267': ΠΛΟΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'268': ΙΑΤΡΙΚΕΣ ΑΜΟΙΒΕΣ
'269': ΕΛΛΗΝΙΚΟΣ ΕΡΥΘΡΟΣ ΣΤΑΥΡΟΣ
'270': ΑΝΩΜΑΛΕΣ ΚΑΤΑΘΕΣΕΙΣ ΣΕ ΧΡΥΣΟ
'271': ΣΥΜΒΟΥΛΙΟ ΤΙΜΗΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'272': ΔΙΑΦΟΡΟΙ ΑΡΔΕΥΤΙΚΟΙ ΝΟΜΟΙ
'273': ΚΥΒΕΡΝΗΤΙΚΟΣ ΕΠΙΤΡΟΠΟΣ
'274': ΕΚΤΕΛΕΣΗ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΡΓΩΝ
'275': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΑΡΩΓΗΣ
'276': ΔΑΣΙΚΕΣ ΜΕΤΑΦΟΡΕΣ
'277': ΜΕ ΤΗ ΔΗΜΟΚΡΑΤΙΑ ΤΟΥ ΚΕΜΠΕΚ
'278': ΕΠΑΝΕΞΑΓΟΜΕΝΑ ΜΕ ΕΓΓΥΗΣΗ
'279': ΔΙΑΝΟΜΗ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
'280': ΑΡΣΗ ΣΥΓΚΡΟΥΣΕΩΣ ΚΑΘΗΚΟΝΤΩΝ
'281': ΕΚΠΑΙΔΕΥΤΙΚΑ ΠΛΟΙΑ
'282': ΚΕΝΤΡΟ ΜΕΤΑΦΡΑΣΗΣ
'283': ΕΙΣΦΟΡΕΣ ΚΑΙ ΝΑΥΛΩΣΕΙΣ
'284': ΜΕΤΕΓΓΡΑΦΕΣ ΦΟΙΤΗΤΩΝ ΑΝΩΤ. ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ
'285': ΤΜΗΜΑΤΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ - ΑΘΛΗΤΙΣΜΟΥ
'286': ΨΥΧΙΑΤΡΕΙΑ
'287': ΦΟΡΟΛΟΓΙΑ ΚΕΦΑΛΑΙΟΥ ΑΝΩΝ. ΕΤΑΙΡΕΙΩΝ
'288': ΤΥΠΟΙ ΣΥΜΒΟΛΑΙΩΝ
'289': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ
'290': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΗΣ ΛΑΙΚΗΣ ΤΕΧΝΗΣ
'291': ΠΑΝΕΠΙΣΤΗΜΙΟ ΠΕΛΟΠΟΝΝΗΣΟΥ
'292': ΟΡΓΑΝΙΣΜΟΣ ΕΡΓΑΤΙΚΗΣ ΚΑΤΟΙΚΙΑΣ
'293': ΑΣΦΑΛΕΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΕ ΟΙΚΟΔΟΜΕΣ
'294': ΣΤΕΓΑΝΗ ΥΠΟΔΙΑΙΡΕΣΗ ΠΛΟΙΩΝ
'295': ΔΙΟΙΚΗΣΗ ΠΡΩΤΕΥΟΥΣΗΣ
'296': ΔΙΔΑΚΤΟΡΙΚΕΣ - ΜΕΤΑΠΤΥΧΙΑΚΕΣ ΣΠΟΥΔΕΣ ΕΘΝΙΚΟΥ ΜΕΤΣΟΒΙΟΥ
'297': ΕΙΣΦΟΡΑ ΚΑΤΟΧΩΝ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ
'298': ΔΙΑΦΟΡΟΙ ΔΙΚΟΝΟΜΙΚΟΙ ΝΟΜΟΙ
'299': ΔΙΕΘΝΕΙΣ ΛΙΜΕΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'300': ΥΓΕΙΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΕΛ.ΑΣ
'301': ΕΛΛΗΝΙΚΑ ΤΑΧΥΔΡΟΜΕΙΑ (ΕΛ.ΤΑ)
'302': ΜΙΣΘΟΙ ΚΑΙ ΕΠΙΔΟΜΑΤΑ Π. ΝΑΥΤΙΚΟΥ
'303': ΓΕΩΡΓΙΚΑ ΤΑΜΕΙΑ
'304': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'305': ΑΠΟΒΑΡΟ
'306': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΚΠΡΟΣΩΠΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ
'307': ΚΩΔΙΚΑΣ ΠΕΡΙ ΔΙΚΗΓΟΡΩΝ
'308': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΒΙΒΑΣΜΟΙ
'309': ΙΣΡΑΗΛΙΤΕΣ
'310': ΣΩΜΑ ΚΤΗΝΙΑΤΡΙΚΟ
'311': ΝΟΡΒΗΓΙΑ - ΝΕΑ ΖΗΛΑΝΔΙΑ – ΝΙΓΗΡΙΑ Κ.ΛΠ
'312': ΕΝΤΥΠΑ ΚΑΙ ΒΙΒΛΙΟΘΗΚΕΣ ΝΑΥΤΙΚΟΥ
'313': ΥΠΟΥΡΓΕΙΟ ΤΥΠΟΥ ΚΑΙ ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΝΗΜΕΡΩΣΗΣ
'314': ΝΑΥΤΙΚΕΣ ΠΕΙΘΑΡΧΙΚΕΣ ΠΟΙΝΕΣ
'315': ΜΙΣΘΩΣΕΙΣ ΑΓΡΟΤΙΚΩΝ ΑΚΙΝΗΤΩΝ
'316': ΔΙΑΦΟΡΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ
'317': ΑΓΡΟΤΙΚΗ ΠΙΣΤΗ
'318': ΛΑΙΚΕΣ ΑΓΟΡΕΣ-ΤΑΜΕΙΟ ΛΑΙΚΩΝ ΑΓΟΡΩΝ
'319': ΚΑΝΟΝΙΣΜΟΣ ΠΕΙΘΑΡΧΙΑΣ ΧΩΡΟΦΥΛΑΚΗΣ
'320': ΑΔΙΚΗΜΑΤΑ ΚΑΤΑ ΤΗΣ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ
'321': ΕΝΟΙΚΙΑΣΗ ΦΟΡΟΥ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ
'322': ΕΥΡΩΠΑΙΚΗ ΣΥΜΒΑΣΗ ΚΟΙΝΩΝΙΚΗΣ ΚΑΙ ΙΑΤΡΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ
'323': ΕΠΙΒΑΤΗΓΑ ΑΕΡΟΣΤΡΩΜΝΑ ΟΧΗΜΑΤΑ
'324': ΕΦΕΔΡΟΙ
'325': ΣΤΡΑΤΙΩΤΙΚΕΣ ΛΕΣΧΕΣ
'326': ΠΡΟΣΩΠΙΚΟ ΦΥΛΑΚΩΝ
'327': ΑΝΑΘΕΩΡΗΣΗ ΤΙΜΩΝ
'328': ΜΑΛΑΚΙΑ ΚΑΙ ΜΑΛΑΚΟΣΤΡΑΚΑ
'329': ΚΩΔΙΚΑΣ ΔΗΜΟΣΙΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ
'330': ΔΙΑΦΟΡΑ ΣΩΜΑΤΕΙΑ
'331': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'332': ΚΩΔΙΚΟΠΟΙΗΣΗ ΑΓΟΡΑΝΟΜΙΚΩΝ ΔΙΑΤΑΞΕΩΝ
'333': ΕΚΠΑΙΔΕΥΣΗ ΣΤΗΝ ΑΛΛΟΔΑΠΗ
'334': ΔΙΔΑΚΤΙΚΑ ΒΙΒΛΙΑ
'335': ΣΥΝΤΑΞΙΟΔΟΤΙΚΑ ΚΑΙ ΑΣΦΑΛΙΣΤΙΚΑ ΘΕΜΑΤΑ ΠΡΟΣΩΠΙΚΟΥ Ν.Π.Δ.Δ
'336': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΞΑΦΑΝΙΣΘΕΝΤΩΝ ΚΑΙ ΑΙΧΜΑΛΩΤΩΝ
'337': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ
'338': ΚΕΝΤΡΟ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΠΟΥΔΩΝ
'339': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΤΥΠΟΥ ΚΑΙ ΠΛΗΡΟΦΟΡΙΩΝ
'340': ΑΡΧΕΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ
'341': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ
'342': ΣΤΕΓΗ ΥΓΕΙΟΝΟΜΙΚΩΝ
'343': ΓΕΝΙΚΑ ΠΕΡΙ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ
'344': ΒΟΥΛΗ
'345': ΕΠΙΛΟΓΗ & ΑΞΙΟΛΟΓΗΣΗ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛ.ΑΣ
'346': ΧΟΙΡΟΤΡΟΦΙΑ
'347': ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΕΩΣ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ
'348': ΕΠΙΒΟΛΗ ΤΕΛΩΝΙΑΚΩΝ ΔΑΣΜΩΝ
'349': ΑΕΡΟΠΟΡΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ
'350': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΝΑΡΚΩΤΙΚΑ
'351': ΔΙΑΦΟΡΕΣ ΤΡΑΠΕΖΕΣ
'352': ΟΙΝΟΛΟΓΟΙ
'353': ΤΕΛΩΝΟΦΥΛΑΚΗ
'354': ΤΑΜΕΙΟ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ (T.EΘ.A.) - ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΕΞΟΠΛΙΣΜΟΥ ΕΝΟΠΛΩΝ
ΔΥΝΑΜΕΩΝ (Ε.Ε.Ε.Ε.Δ.)
'355': ΕΚΤΕΛΕΣΗ ΤΗΣ ΠΟΙΝΗΣ
'356': ΙΣΟΛΟΓΙΣΜΟΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ
'357': ΑΡΧΙΤΕΚΤΟΝΙΚΟΙ ΔΙΑΓΩΝΙΣΜΟΙ
'358': ΚΑΤΑΡΓΗΣΗ ΦΥΛΕΤΙΚΩΝ ΔΙΑΚΡΙΣΕΩΝ
'359': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΑΠΟΦΟΙΤΩΝ
'360': ΜΟΝΑΣΤΗΡΙΑΚΗ ΠΕΡΙΟΥΣΙΑ ΣΑΜΟΥ
'361': ΣΥΝΤΑΞΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'362': ΟΙΚΟΝΟΜΙΚΕΣ ΕΦΟΡΙΕΣ
'363': ΦΡΟΝΤΙΣΤΗΡΙΑ ΕΦΑΡΜΟΓΩΝ
'364': ΝΟΜΑΡΧΙΕΣ ΑΤΤΙΚΗΣ
'365': ΦΥΜΑΤΙΩΣΗ
'366': ΕΛΕΓΧΟΣ ΑΝΑΤΙΜΗΣΕΩΝ
'367': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΝΑΥΤΙΛΙΑΣ
'368': ΚΩΦΑΛΑΛΟΙ
'369': ΙΑΤΡΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ
'370': ΕΞΟΔΑ ΔΗΜΟΣΙΑΣ ΑΣΦΑΛΕΙΑΣ
'371': ΜΕ ΤΗΝ ΑΡΓΕΝΤΙΝΗ
'372': ΚΛΑΔΟΣ ΥΓΕΙΟΝΟΜΙΚΗΣ ΠΕΡΙΘΑΛΨΗΣ Τ.Α.Ε
'373': ΥΠΗΡΕΣΙΑ ΕΚΚΑΘΑΡΙΣΕΩΣ ΝΑΡΚΟΠΕΔΙΩΝ
'374': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ Τ.Α.Υ.Α.Π
'375': ΠΡΟΣΤΑΣΙΑ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ
'376': ΒΙΒΛΙΑ ΕΝΔΙΚΩΝ ΜΕΣΩΝ
'377': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΙΚΡΟΜΕΣΑΙΩΝ ΜΕΤΑΠΟΙΗΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ
ΧΕΙΡΟΤΕΧΝΙΑΣ
'378': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΧΑΡΤΗΣ
'379': ΦΟΡΟΣ ΓΑΜΙΚΩΝ ΣΥΜΦΩΝΩΝ ΙΣΡΑΗΛΙΤΩΝ
'380': ΥΠΟΤΡΟΦΙΑΙ ΚΤΗΝΙΑΤΡΙΚΗΣ
'381': ΑΠΟΔΟΧΕΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ
'382': ΕΠΙΒΑΤΗΓΑ ΑΚΤΟΠΛΟΙΚΑ ΠΛΟΙΑ
'383': ΠΑΛΑΙΟΙ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΙ ΝΟΜΟΙ
'384': ΚΩΔΙΚΑΣ ΠΕΡΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ
'385': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ
'386': ΚΤΗΜΑΤΟΓΡΑΦΗΣΗ ΔΑΣΩΝ
'387': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ
'388': ΠΕΡΙΟΡΙΣΜΟΣ ΧΡΗΣΗΣ ΟΡΙΣΜΕΝΩΝ ΣΥΜΒΑΤΙΚΩΝ ΟΠΛΩΝ
'389': ΑΓΙΟΝ ΟΡΟΣ
'390': ΚΥΡΩΣΕΙΣ ΦΟΡΟΛΟΓΙΚΩΝ ΠΑΡΑΒΑΣΕΩΝ
'391': ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α
'392': ΕΠΑΝΑΠΑΤΡΙΣΜΟΣ ΚΕΦΑΛΑΙΩΝ
'393': ΜΑΘΗΤΕΣ ΤΕΧΝΙΤΕΣ
'394': ΔΙΑΒΙΒΑΣΕΙΣ
'395': ΕΜΜΙΣΘΟΙ ΚΑΙ ΠΟΙΝΙΚΟΙ ΔΙΚ. ΕΠΙΜΕΛΗΤΕΣ
'396': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΣΥΝΔΡΟΜΗΣ
'397': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΕΤΡΕΛΑΙΟΥ
'398': ΕΛΛΗΝΙΚΗ ΤΡΑΠΕΖΑ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΕΩΣ ΑΝΩΝΥΜΟΣ ΕΤΑΙΡΕΙΑ (Ε.Τ.Β.Α. Α.Ε.)
'399': ΕΙΔΙΚΟΤΗΤΕΣ ΚΑΙ ΤΡΟΠΟΣ ΕΙΣΟΔΟΥ ΣΤΕΛΕΧΩΝ
'400': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΗΜΕΔΑΠΗ - ΣΩΜΑ ΕΠΙΘΕΩΡΗΣΗΣ ΕΡΓΑΣΙΑΣ
'401': ΙΝΣΤΙΤΟΥΤΟ ΩΚΕΑΝΟΓΡΑΦΙΚΩΝ ΚΑΙ ΑΛΙΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ
'402': ΕΛΕΓΧΟΣ ΑΠΟΛΥΣΕΩΝ ΜΙΣΘΩΤΩΝ
'403': ΠΑΝΕΛΛΗΝΙΑ ΕΚΘΕΣΗ ΛΑΜΙΑΣ
'404': ΚΥΡΙΑΚΗ ΑΡΓΙΑ ΚΑΙ ΑΛΛΕΣ ΥΠΟΧΡΕΩΤΙΚΕΣ ΑΡΓΙΕΣ
'405': ΚΛΑΔΟΣ ΥΓΕΙΑΣ Ο.Α.Ε.Ε
'406': ΟΡΚΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ
'407': ΕΜΠΟΡΙΚΑ ΒΙΒΛΙΑ
'408': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'409': ΑΓΙΟΣ ΒΙΚΕΝΤΙΟΣ-ΓΡΕΝΑΔΙΝΟΙ, ΑΓΙΟΣ ΜΑΡΙΝΟΣ Κ.ΛΠ
'410': ΑΠΟΖΗΜΙΩΣΗ ΔΙΑΤΕΛΕΣΑΝΤΩΝ ΠΡΩΘΥΠΟΥΡΓΩΝ
'411': ΑΣΦΑΛΙΣΗ ΛΟΓΟΤΕΧΝΩΝ ΚΑΙ ΚΑΛΛΙΤΕΧΝΩΝ
'412': ΠΕΙΘΑΡΧΙΚΑ ΣΥΜΒΟΥΛΙΑ
'413': ΕΤΑΙΡΙΕΣ ΧΡΗΜΑΤΟΔΟΤΙΚΗΣ ΜΙΣΘΩΣΗΣ
'414': ΚΟΙΝΩΝΙΚΗ ΥΠΗΡΕΣΙΑ ΦΥΛΑΚΩΝ
'415': ΚΑΝΟΝΙΣΜΟΣ ΥΠΗΡΕΣΙΩΝ ΑΓΡΟΦΥΛΑΚΗΣ
'416': ΑΣΦΑΛΙΣΗ ΣΤΟ ΙΚΑ
'417': ΕΜΠΟΡΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΚΑΙ ΑΚΟΛΟΥΘΟΙ
'418': ΕΠΙΚΟΥΡΟΙ ΠΑΡΑΤΗΡΗΤΕΣ
'419': ΥΠΟΤΡΟΦΙΕΣ
'420': ΚΕΝΤΡΟ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ
'421': ΠΡΩΤΕΣ ΥΛΕΣ ΣΟΚΟΛΑΤΟΠΟΙΙΑΣ
'422': ΕΠΙΤΡΟΠΗ ΚΗΠΩΝ ΚΑΙ ΔΕΝΔΡΟΣΤΟΙΧΙΩΝ
'423': ΚΙΝΗΤΟ ΕΠΙΣΗΜΑ
'424': ΣΥΝΔΙΚΑΛΙΣΜΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'425': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ Π.Ν
'426': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΚΑΙ ΔΑΝΕΙΩΝ
'427': ΑΔΕΙΕΣ ΗΝΙΟΧΙΑΣ
'428': ΥΠΗΡΕΣΙΑ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΥ ΚΑΙ ΜΕΛΕΤΩΝ
'429': ΚΡΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ
'430': ΑΤΟΜΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ
'431': ΠΟΛΥΤΕΚΝΟΙ
'432': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΜΑΚΕΔΟΝΙΑΣ
'433': ΑΣΦΑΛΙΣΗ ΑΥΤΟΚΙΝΗΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ
'434': ΔΑΝΕΙΑ ΕΣΩΤΕΡΙΚΑ
'435': ΕΚΚΛΗΣΙΑ ΚΡΗΤΗΣ
'436': ΦΟΡΟΛΟΓΙΑ ΣΤΑΦΙΔΑΣ
'437': ΕΚΠΑΙΔΕΥΤΙΚΕΣ ΑΔΕΙΕΣ
'438': ΑΕΡΟΔΙΚΕΙΑ
'439': ΕΠΙΔΟΜΑ ΑΣΘΕΝΕΙΑΣ
'440': ΘΕΣΕΙΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ
'441': ΑΓΟΡΑ ΣΥΝΑΛΛΑΓΜΑΤΟΣ
'442': ΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ ΤΟΥ ΚΡΑΤΟΥΣ (Ν.Σ.Κ.)
'443': ΦΟΡΟΛΟΓΙΑ ΜΕΤΑΒΙΒΑΣΗΣ
'444': ΣΥΜΒΟΥΛΙΑ - ΕΠΙΤΡΟΠΕΣ - ΙΝΣΤΙΤΟΥΤΑ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ
'445': ΤΕΛΗ ΕΙΣΙΤΗΡΙΩΝ ΚΑΙ ΚΟΜΙΣΤΡΩΝ
'446': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΥΓΕΙΟΝΟΜΙΚΟΥ ΣΩΜΑΤΟΣ
'447': ΠΡΟΣΩΠΙΚΟ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ ΜΕ ΣΧΕΣΗ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ
'448': ΑΡΤΕΡΓΑΤΕΣ
'449': ΕΥΚΟΛΙΕΣ ΣΕ ΦΟΙΤΗΤΕΣ
'450': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΚΟΙΝΗΣ ΧΟΡΤΟΝΟΜΗΣ ΚΑΙ ΣΥΝΙΔΙΟΚΤΗΣΙΑΣ
'451': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΠΕΡΙΦΕΡΕΙΑΚΟΥ ΓΕΝΙΚΟΥ ΝΟΣΟΚΟΜΕΙΟΥ Ο
ΕΥΑΓΓΕΛΙΣΜΟΣ
'452': ΠΡΟΣΚΟΠΙΣΜΟΣ
'453': ΣΥΜΒΟΥΛΙΑ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΚΑΙ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ
'454': ΚΡΑΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΗΧΑΝΗΜΑΤΩΝ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ
'455': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΝΘΥΠΑΣΠΙΣΤΩΝ-ΥΠΑΞΙΩΜΑΤΙΚΩΝ
'456': ΔΙΑΦΟΡΕΣ ΣΧΟΛΕΣ
'457': ΒΙΒΛΙΑ ΔΗΜΟΣΙΕΥΣΕΩΣ ΔΙΑΘΗΚΩΝ
'458': ΚΑΝΟΝΙΣΜΟΙ ΠΡΟΣΩΠΙΚΟΥ ΣΥΓΚΟΙΝΩΝΙΑΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'459': ΤΟΥΡΙΣΤΙΚΟΙ ΤΟΠΟΙ
'460': ΙΝΣΤΙΤΟΥΤΟ ΞΕΝΩΝ ΓΛΩΣΣΩΝ ΚΑΙ ΦΙΛΟΛΟΓΙΩΝ
'461': ΚΑΠΝΟΠΩΛΕΣ
'462': ΑΓΩΓΕΣ ΓΙΑΤΡΩΝ
'463': ΣΥΣΤΑΣΗ ΚΑΙ ΑΠΟΔΟΣΗ ΠΑΡΑΚΑΤΑΘΗΚΩΝ ΑΠΟ Τ.Π. ΚΑΙ Δ
'464': ΑΔΙΚΗΜΑΤΑ ΔΙΑΠΡΑΤΤΟΜΕΝΑ ΣΤΑ ΚΡΑΤΗ-ΜΕΛΗ
'465': ΑΝΑΣΤΟΛΕΣ ΤΟΥ ΣΥΝΤΑΓΜΑΤΟΣ - ΚΑΤΑΣΤΑΣΗ ΠΟΛΙΟΡΚΙΑΣ
'466': ΣΥΜΒΑΣΕΙΣ ΠΑΡΟΧΗΣ ΑΣΦΑΛΕΙΑΣ (ΕΝΕΧΥΡΟ, ΥΠΟΘΗΚΗ Κ.ΛΠ.)
'467': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣΝΑΥΤΙΚΩΝ ΠΡΑΚΤΟΡΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ (Τ.Α.Ν.Π.Υ.)
'468': ΑΝΩΤΑΤΟ ΣΥΓΚΟΙΝΩΝΙΑΚΟ ΣΥΜΒΟΥΛΙΟ
'469': ΠΡΕΒΕΝΤΟΡΙΑ
'470': ΑΝΑΒΟΛΗ ΣΤΡΑΤΕΥΣΕΩΣ
'471': ΕΙΔΙΚΑ ΛΗΞΙΑΡΧΕΙΑ
'472': ΓΕΩΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ
'473': ΥΓΕΙΟΝΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ
'474': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ
'475': ΚΑΖΑΚΣΤΑΝ – ΚΑΜΕΡΟΥΝ – ΚΑΝΑΔΑΣ Κ.ΛΠ
'476': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΤΟΝ ΑΜΑΧΟ ΠΛΗΘΥΣΜΟ
'477': ΦΙΛΟΣΟΦΙΚΗ ΣΧΟΛΗ
'478': ΕΚΤΕΛΩΝΙΣΜΟΣ ΤΑΧΥΔΡΟΜΙΚΩΝ ΔΕΜΑΤΩΝ
'479': ΥΔΡΕΥΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ
'480': ΣΥΜΦΩΝΙΕΣ ΠΕΡΙ ΠΛΩΤΩΝ ΟΔΩΝ
'481': ΑΝΑΚΗΡΥΞΗ ΤΗΣ ΑΝΕΞΑΡΤΗΣΙΑΣ
'482': ΕΠΙΤΡΟΠΗ ΟΛΥΜΠΙΑΚΩΝ ΑΓΩΝΩΝ
'483': ΟΙΝΟΠΑΡΑΓΩΓΗ ΑΤΤΙΚΟΒΟΙΩΤΙΑΣ
'484': ΕΚΠΤΩΣΕΙΣ ΥΠΕΡ ΕΞΑΓΩΓΕΩΝ
'485': ΦΟΡΟΛΟΓΙΑ ΚΛΗΡΟΝΟΜΙΩΝ, ΔΩΡΕΩΝ, ΓΟΝΙΚΩΝ ΠΑΡΟΧΩΝ
'486': ΟΡΦΑΝΟΤΡΟΦΕΙΑ ΚΑΙ ΟΙΚΟΤΡΟΦΕΙΑ
'487': ΜΕ ΤΗΝ ΟΥΡΑΓΟΥΑΗ
'488': ΜΕ ΤΗΝ ΑΥΣΤΡΙΑΚΗ
'489': ΔΙΑΦΟΡΟΙ ΦΟΡΟΙ ΚΑΤΑΝΑΛΩΣΕΩΣ
'490': ΔΙΕΥΘΥΝΣΗ ΕΦΕΔΡΩΝ - ΠΟΛΕΜΙΣΤΩΝ - ΑΓΩΝΙΣΤΩΝ
'491': ΑΓΡΟΤΙΚΕΣ ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ
'492': ΞΥΛΕΙΑ
'493': ΒΙΒΛΙΑΡΙΑ ΥΓΕΙΑΣ ΕΡΓΑΤΩΝ
'494': ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΥΠΗΡΕΣΙΩΝ
'495': ΝΟΜΑΡΧΙΑΚΕΣ ΚΑΙ ΔΗΜΟΤΙΚΕΣ ΕΚΛΟΓΕΣ
'496': ΕΓΓΥΗΣΕΙΣ ΚΑΙ ΔΑΝΕΙΑ ΤΟΥ ΔΗΜΟΣΙΟΥ
'497': ΥΠΟΥΡΓΕΙΟ ΑΝΑΠΤΥΞΗΣ
'498': ΤΑΚΤΙΚΑ ΔΙΟΙΚΗΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'499': ΤΡΟΦΟΔΟΣΙΑ ΠΛΗΡΩΜΑΤΩΝ ΠΛΟΙΩΝ
'500': ΔΙΑΦΟΡΟΙ ΛΙΜΕΝΕΣ ΚΑΙ ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ
'501': ΗΛΕΚΤΡΙΚΕΣ ΕΚΜΕΤΑΛΛΕΥΣΕΙΣ
'502': ΠΡΟΥΠΟΘΕΣΕΙΣ ΑΣΚΗΣΗΣ ΔΙΑΦΟΡΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ
'503': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΑΕΡΟΣΚΑΦΩΝ
'504': ΕΠΙΤΡΟΠΗ ΔΑΣΜΟΛΟΓΙΟΥ
'505': ΝΑΥΠΗΓΕΙΑ Π. ΝΑΥΤΙΚΟΥ
'506': ΒΙΟΜΗΧΑΝΙΚΕΣ ΚΑΙ ΕΠΙΧΕΙΡΗΜΑΤΙΚΕΣ ΠΕΡΙΟΧΕΣ
'507': ΙΑΤΡΟΔΙΚΑΣΤΕΣ
'508': ΑΘΛΗΤΙΣΜΟΣ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'509': ΟΡΓΑΝΙΣΜΟΣ ΣΥΚΩΝ
'510': ΚΑΝΟΝΙΣΜΟΣ ΑΣΘΕΝΕΙΑΣ ΤΑΜΕΙΟΥ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ
ΠΡΑΚΤΟΡΕΙΩΝ (Τ.Σ.Ε.Υ.Π.)
'511': ΑΔΕΙΕΣ ΜΙΣΘΩΤΩΝ
'512': ΠΡΟΣΤΑΣΙΑ ΚΕΦΑΛΑΙΩΝ ΕΞΩΤΕΡΙΚΟΥ
'513': ΑΠΟΔΕΙΚΤΙΚΑ ΦΟΡΟΛΟΓΙΚΗΣ ΕΝΗΜΕΡΟΤΗΤΑΣ
'514': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΩΝ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ
ΚΑΙ ΤΑΧΥΔΡΟΜΕΙΩΝ (Ε.Ε.Τ.Τ.)
'515': ΠΡΟΣΩΠΙΚΟ Ο.Τ.Ε
'516': ΒΑΣΙΛΙΚΑ ΙΔΡΥΜΑΤΑ
'517': ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΛΗΓΕΝΤΩΝ ΑΠΟ ΕΚΡΗΞΗ ΠΛΟΙΟΥ ΣΤΗΝ ΚΡΗΤΗ
'518': ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΥΝΑΜΕΩΣ ΡΕΟΝΤΩΝ ΥΔΑΤΩΝ
'519': ΚΑΚΟΥΡΓΙΟΔΙΚΕΙΑ
'520': ΚΕΝΤΡΙΚΕΣ ΑΓΟΡΕΣ ΑΛΛΩΝ ΠΟΛΕΩΝ
'521': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ Π.Ν
'522': ΕΚΛΟΓΙΚΟΙ ΚΑΤΑΛΟΓΟΙ ΚΑΙ ΒΙΒΛΙΑΡΙΑ
'523': ΥΠΗΡΕΣΙΑ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ
'524': ΤΟΥΡΙΣΤΙΚΗ ΑΝΑΠΤΥΞΗ
'525': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ
'526': ΕΛΕΓΧΟΣ ΕΚΡΗΚΤΙΚΩΝ ΥΛΩΝ
'527': ΜΑΚΕΔΟΝΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ
'528': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΣΕ ΔΗΜΟΣΙΟΥΣ ΥΠΑΛΛΗΛΟΥΣ
'529': ΣΤΡΑΤΙΩΤΙΚΕΣ ΥΠΟΧΡΕΩΣΕΙΣ ΕΠΑΝΑΠΑΤΡΙΖΟΜΕΝΩΝ
'530': ΔΙΑΚΡΙΣΗ ΕΜΠΟΡΙΚΩΝ ΠΡΑΞΕΩΝ
'531': ΟΡΓΑΝΙΣΜΟΣ ΕΛΛΗΝΙΚΩΝ ΓΕΩΡΓΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ (Ε.Λ.Γ.Α.)
'532': ΕΞΩΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ
'533': ΔΡΑΧΜΟΠΟΙΗΣΗ
'534': ΜΕ ΤΗ ΒΡΑΖΙΛΙΑ
'535': ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΑΚΑΔΗΜΙΑ
'536': ΑΝΤΑΛΛΑΓΗ ΘΕΡΑΠΕΥΤΙΚΩΝ ΟΥΣΙΩΝ
'537': ΓΑΛΛΙΑ, ΓΕΡΜΑΝΙΑ Κ.ΛΠ
'538': ΝΟΜΟΠΑΡΑΣΚΕΥΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ
'539': ΚΥΒΕΡΝΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ
'540': ΣΤΡΑΤΙΩΤΙΚΟΙ ΑΚΟΛΟΥΘΟΙ
'541': ΔΙΑΘΕΣΗ ΑΠΟΣΤΡΑΓΓΙΖΟΜΕΝΩΝ ΓΑΙΩΝ
'542': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΡΑΔΙΟΦΩΝΙΑ – ΤΗΛΕΟΡΑΣΗ
'543': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΦΑΡΜΑΚΩΝ
'544': ΣΥΜΒΑΣΕΙΣ ΔΙΑΦΟΡΕΣ
'545': ΠΡΑΞΕΙΣ ΚΑΤΑ ΤΗΣ ΑΣΦΑΛΕΙΑΣ ΤΗΣ ΑΕΡΟΠΟΡΙΑΣ
'546': ΙΑΤΡΟΙ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ
'547': ΚΕΝΤΡΙΚΟ ΣΥΜΒΟΥΛΙΟ ΥΓΕΙΑΣ (ΚΕ.Σ.Υ.)
'548': ΑΝΩΤΑΤΟ ΣΥΜΒΟΥΛΙΟ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'549': ΥΠΟΥΡΓΕΙΟ ΕΝΕΡΓΕΙΑΣ ΚΑΙ ΦΥΣΙΚΩΝ ΠΟΡΩΝ
'550': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΕΛΑΦΡΩΝ ΑΕΡΟΠΛΑΝΩΝ Δ.Χ
'551': ΠΟΛΥΕΘΝΕΙΣ ΜΟΡΦΩΤΙΚΕΣ ΣΥΜΦΩΝΙΕΣ
'552': ΕΚΠΑΙΔΕΥΣΗ Λ.Σ
'553': ΠΡΟΣΤΑΣΙΑ ΕΛΕΥΘΕΡΟΥ ΑΝΤΑΓΩΝΙΣΜΟΥ
'554': ΕΘΝΙΚΗ ΕΠΙΤΡΟΠΗ ΔΙΕΘΝΟΥΣ ΕΜΠΟΡΙΚΟΥ ΕΠΙΜΕΛΗΤΗΡΙΟΥ
'555': ΟΡΓΑΝΙΣΜΟΣ
'556': ΤΕΛΩΝΕΙΑΚΕΣ ΠΑΡΑΚΑΤΑΘΗΚΕΣ
'557': ΕΛΕΓΧΟΣ ΟΡΓΑΝΙΣΜΩΝ ΚΟΙΝΩΝΙΚΗΣ ΠΟΛΙΤΙΚΗΣ
'558': ΕΝΩΣΕΙΣ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'559': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'560': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΛΟΓΙΚΩΝ ΚΑΙ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΕΥΝΩΝ
'561': ΛΑΟΓΡΑΦΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ ΜΑΚΕΔΟΝΙΑΣ - ΘΡΑΚΗΣ
'562': ΠΡΩΤΕΣ ΥΛΕΣ ΤΑΠΗΤΟΥΡΓΙΑΣ
'563': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΡΗΤΗΣ
'564': ΚΩΔΙΚΑΣ ΟΔΙΚΗΣ ΚΥΚΛΟΦΟΡΙΑΣ
'565': ΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ
'566': ΜΕΛΕΤΕΣ ΠΡΟΓΡΑΜΜΑΤΟΣ ΔΗΜΟΣΙΩΝ ΕΠΕΝΔΥΣΕΩΝ
'567': ΕΠΙΔΟΣΗ ΔΙΑ ΤΟΥ ΤΑΧΥΔΡΟΜΕΙΟΥ
'568': ΠΑΝΕΠΙΣΤΗΜΙΟ ΘΡΑΚΗΣ
'569': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ
'570': ΔΗΜΟΣΙΑ ΚΤΗΜΑΤΑ ΣΤΗ ΔΩΔΕΚΑΝΗΣΟ
'571': ΣΥΜΒΑΣΕΙΣ ΔΙΚΑΣΤΙΚΗΣ ΑΝΤΙΛΗΨΕΩΣ
'572': ΠΕΡΙΟΡΙΣΜΟΙ ΑΛΙΕΙΑΣ
'573': ΠΥΡΗΝΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ
'574': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΚΙΝΗΤΩΝ
'575': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΑΝΑΛΥΤΙΚΑ ΠΡΟΓΡΑΜΜΑΤΑ
'576': ΔΙΚΑΙΩΜΑΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΕΡΓΑΣΙΩΝ
'577': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΥΤΟΚΙΝΗΤΙΣΤΩΝ (Τ.Σ.Α.)
'578': ΤΗΛΕΦΩΝΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'579': ΦΟΡΟΛΟΓΙΑ ΑΣΦΑΛΙΣΤΡΩΝ
'580': ΔΙΕΘΝΗΣ ΥΔΡΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ
'581': ΕΠΑΡΧΙΕΣ
'582': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ
'583': ΓΕΝΙΚΑ ΓΙΑ ΤΑ ΘΕΑΤΡΑ
'584': ΣΥΜΒΑΣΕΙΣ ΔΙΩΞΕΩΣ ΛΑΘΡΕΜΠΟΡΙΟΥ
'585': ΜΗΧΑΝΕΣ ΠΡΟΠΛΗΡΩΜΗΣ ΤΕΛΩΝ
'586': ΟΡΓΑΝΙΣΜΟΣ ΚΡΑΤΙΚΩΝ ΘΕΑΤΡΩΝ
'587': ΚΕΝΤΡΟ ΗΛΕΚΤΡΟΝΙΚΟΥ ΥΠΟΛΟΓΙΣΤΟΥ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ
'588': ΦΟΡΟΣ ΠΡΟΣΤΙΘΕΜΕΝΗΣ ΑΞΙΑΣ
'589': ΤΑΜΕΙΑ ΑΡΩΓΗΣ ΤΤΤ. ΥΠΑΛΛΗΛΩΝ
'590': ΣΩΜΑ ΟΡΚΩΤΩΝ ΕΛΕΓΚΤΩΝ ΛΟΓΙΣΤΩΝ (Σ.Ο.Ε.Λ.), ΕΠΙΤΡΟΠΗ ΛΟΓΙΣΤΙΚΗΣ ΤΥΠΟΠΟΙΗΣΗΣ
ΚΑΙ ΕΛΕΓΧΩΝ (Ε.Λ.Τ.Ε.)
'591': ΑΓΡΟΤΙΚΑ ΝΗΠΙΟΤΡΟΦΕΙΑ
'592': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΑΘΗΝΩΝ ΠΕΙΡΑΙΩΣ
'593': ΜΙΣΘΩΣΕΙΣ ΑΚΙΝΗΤΩΝ Ο.Δ.Ε.Π
'594': ΕΛΕΓΧΟΣ ΣΠΟΡΟΠΑΡΑΓΩΓΗΣ
'595': ΑΜΥΝΤΙΚΕΣ ΠΕΡΙΟΧΕΣ ΚΑΙ Ν. ΟΧΥΡΑ
'596': ΟΔΟΙΠΟΡΙΚΑ
'597': ΠΟΡΟΙ ΟΡΓΑΝΙΣΜΩΝ ΤΟΥΡΙΣΜΟΥ
'598': ΔΙΕΘΝΕΣ ΔΙΚΑΣΤΗΡΙΟ
'599': ΟΙΚΟΝΟΜΙΚΗ ΜΕΡΙΜΝΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'600': ΓΕΝΙΚΟ ΝΟΣΟΚΟΜΕΙΟ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ
'601': ΝΟΜΙΚΗ ΒΟΗΘΕΙΑ ΣΕ ΠΟΛΙΤΕΣ ΧΑΜΗΛΟΥ ΕΙΣΟΔΗΜΑΤΟΣ
'602': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΟΙ ΣΥΛΛΟΓΟΙ
'603': ΥΠΟΥΡΓΕΙΟ ΣΤΡΑΤΙΩΤΙΚΩΝ
'604': ΠΡΟΣΩΠΙΚΟ Ε.Μ.Π
'605': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ
'606': ΑΓΟΝΕΣ ΓΡΑΜΜΕΣ
'607': ΜΟΝΟΠΩΛΙΟ ΠΕΤΡΕΛΑΙΟΥ
'608': ΠΡΟΛΗΨΗ ΡΥΠΑΝΣΗΣ ΤΗΣ ΘΑΛΑΣΣΑΣ
'609': ΧΩΡΙΚΗ ΔΙΚΑΙΟΔΟΣΙΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ
'610': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ
'611': ΥΠΗΡΕΣΙΑ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ
'612': ΑΞΙΟΠΟΙΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΠΕΡΙΟΥΣΙΑΣ
'613': ΕΜΠΟΡΙΚΟΙ ΑΝΤΙΠΡΟΣΩΠΟΙ
'614': ΕΝΩΣΕΙΣ ΕΦΕΔΡΩΝ ΑΞΙΩΜΑΤΙΚΩΝ
'615': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΒΙΟΜΗΧΑΝΙΑΣ
'616': ΛΟΓΙΣΤΙΚΟ ΕΙΔΙΚΩΝ ΤΑΜΕΙΩΝ Ν.Π.Δ.Δ
'617': ΣΥΜΒΑΣΗ ΓΙΑ ΔΕΙΓΜΑΤΑ ΚΛΠ
'618': ΕΡΓΟΛΗΠΤΕΣ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ
'619': ΕΠΑΝΕΠΟΙΚΙΣΜΟΣ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ
'620': ΦΑΡΙΚΑ ΤΕΛΗ
'621': ΛΑΤΟΜΕΙΑ ΜΑΡΜΑΡΩΝ
'622': ΠΟΣΟΣΤΟ ΣΥΜΜΕΤΟΧΗΣ ΑΣΦΑΛΙΣΜΕΝΩΝ
'623': ΑΣΦΑΛΕΙΑ ΑΝΘΡΩΠΙΝΗΣ ΖΩΗΣ ΣΤΗ ΘΑΛΑΣΣΑ
'624': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΦΥΛΑΚΩΝ
'625': ΛΑΘΡΕΜΠΟΡΙΑ
'626': ΑΣΦΑΛΙΣΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΕΝΙΚΑ
'627': ΕΙΣΑΓΩΓΗ ΧΛΩΡΙΚΟΥ ΚΑΛΙΟΥ
'628': ΙΝΣΤΙΤΟΥΤΟ ΓΕΩΠΟΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'629': ΕΠΙΔΟΜΑ ΠΑΣΧΑ - ΧΡΙΣΤΟΥΓΕΝΝΩΝ
'630': ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΑΛΛΗΛΑΣΦΑΛΕΙΑΣ
'631': ΟΡΓΑΝΙΣΜΟΣ ΦΟΡΟΛΟΓΙΚΩΝ ΔΙΚΑΣΤΗΡΙΩΝ
'632': ΕΠΙΔΟΣΗ
'633': ΙΔΡΥΜΑ ΚΡΑΤΙΚΩΝ ΥΠΟΤΡΟΦΙΩΝ
'634': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΑΕΡΟΥΓΕΙΟΝΟΜΕΙΩΝ
'635': ΟΦΕΙΛΕΣ ΠΡΟΣ ΤΟ ΔΗΜΟΣΙΟ
'636': ΠΡΑΚΤΟΡΕΙΑ ΕΙΔΗΣΕΩΝ
'637': ΕΛΕΓΧΟΣ ΚΑΙ ΕΠΟΠΤΕΙΑ ΞΕΝΟΔΟΧΕΙΩΝ ΚΛΠ
'638': ΚΟΙΝΑ ΤΑΜΕΙΑ ΕΚΜΕΤΑΛΛΕΥΣΕΩΣ ΛΕΩΦΟΡΕΙΩΝ (Κ.Τ.Ε.Λ.)
'639': ΚΑΤΩΤΑΤΑ ΟΡΙΑ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ
'640': ΣΥΝΤΗΡΗΤΙΚΗ ΚΑΤΑΣΧΕΣΗ ΠΛΟΙΩΝ
'641': ΥΠΗΡΕΣΙΑ ΠΡΟΣΤΑΣΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ ΣΤΗΝ ΑΛΛΟΔΑΠΗ
'642': ΕΥΡΩΠΑΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΠΥΡΗΝΙΚΩΝ ΕΡΕΥΝΩΝ
'643': ΒΙΒΛΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ
'644': ΠΟΛΙΤΙΚΕΣ ΚΑΙ ΣΤΡΑΤΙΩΤΙΚΕΣ ΣΥΝΤΑΞΕΙΣ
'645': ΜΕΤΑΤΡΟΠΗ ΜΕΤΟΧΩΝ ΣΕ ΟΝΟΜΑΣΤΙΚΕΣ
'646': ΕΙΔΙΚΟΙ ΦΡΟΥΡΟΙ
'647': ΥΠΗΡΕΣΙΑ ΕΘΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ
'648': ΡΥΘΜΙΣΤΙΚΟΣ ΦΟΡΟΣ
'649': ΛΙΜΑΝΙ ΗΡΑΚΛΕΙΟΥ ΚΡΗΤΗΣ ΚΑΙ
'650': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΥΠΟΤΡΟΦΙΕΣ
'651': ΦΟΡΟΛΟΓΙΑ ΟΙΝΟΥ
'652': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΗ ΣΥΜΒΑΣΗ ΑΕΡΟΝΑΥΤΙΛΙΑΣ
'653': ΤΑΜΕΙΟ ΑΡΩΓΗΣ ΥΠΑΛΛΗΛΩΝ
'654': ΚΟΙΝΩΝΙΚΗ ΑΣΦΑΛΙΣΗ ΑΓΡΟΤΩΝ
'655': ΚΥΡΟΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΩΝ ΠΡΑΞΕΩΝ
'656': ΦΟΡΟΛΟΓΙΑ ΥΠΕΡΑΞΙΑΣ ΑΚΙΝΗΤΩΝ
'657': ΝΗΠΙΑΓΩΓΕΙΑ
'658': ΕΚΘΕΜΑΤΑ ΚΑΙ ΔΕΙΓΜΑΤΑ
'659': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ ΑΕΡΟΠΟΡΙΑΣ
'660': ΠΛΗΡΩΜΗ ΜΙΣΘΩΝ ΚΑΙ ΗΜΕΡΟΜΙΣΘΙΩΝ
'661': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΠΝΟΥ
'662': ΟΡΙΑ
'663': ΔΙΚΑΙΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΑΘΩΝ, ΠΥΡΟΠΑΘΩΝ, ΠΡΟΣΦΥΓΩΝ ΚΛΠ
'664': ΧΡΕΗ ΚΛΗΡΟΝΟΜΙΩΝ
'665': ΠΡΟΣΩΠΙΚΟΝ ΙΔΡΥΜΑΤΩΝ ΠΑΙΔΙΚΗΣ ΠΡΟΣΤΑΣΙΑΣ
'666': ΜΙΣΘΩΣΕΙΣ ΚΑΙ ΑΓΟΡΕΣ
'667': ΠΑΛΑΙΟΤΕΡΑΙ ΕΚΚΑΘΑΡΙΣΕΙΣ
'668': ΟΙΚΟΝΟΜΙΚΗ ΑΠΟΚΑΤΑΣΤΑΣΗ ΑΓΡΟΤΩΝ
'669': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΔΗΜΟΤΙΚΑ ΚΑΙ ΚΟΙΝΟΤΙΚΑ ΕΡΓΑ
'670': ΜΗΤΡΩΟ ΑΓΡΟΤΩΝ
'671': ΚΑΝΟΝΙΣΜΟΣ ΔΙΕΥΚΟΛΥΝΣΕΩΝ
'672': ΚΡΑΤΙΚΟ ΕΡΓΟΣΤΑΣΙΟ ΑΕΡΟΠΛΑΝΩΝ
'673': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΕΝΔΕΙΚΤΙΚΑ
'674': ΑΥΘΑΙΡΕΤΕΣ ΚΑΤΑΣΚΕΥΕΣ
'675': ΕΓΚΑΤΑΛΕΛΕΙΜΜΕΝΕΣ ΕΚΤΑΣΕΙΣ
'676': ΥΠΟΥΡΓΕΙΟ ΔΗΜΟΣΙΩΝ ΄ΕΡΓΩΝ
'677': ΠΡΟΝΟΙΑ Β. ΕΛΛΑΔΟΣ
'678': ΔΙΚΑΣΤΙΚΟ ΕΝΣΗΜΟ - ΑΓΩΓΟΣΗΜΟ
'679': ΤΑΧΥΔΡΟΜΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ
'680': ΕΣΩΤΕΡΙΚΗ ΝΟΜΟΘΕΣΙΑ
'681': ΦΟΡΟΛΟΓΙΑ ΤΣΙΓΑΡΟΧΑΡΤΟΥ
'682': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ
'683': ΜΑΙΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ
'684': ΑΔΕΙΕΣ ΣΤΡΑΤΙΩΤΙΚΩΝ
'685': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'686': ΠΟΙΝΙΚΟΣ ΚΑΙ ΠΕΙΘΑΡΧΙΚΟΣ ΚΩΔΙΚΑΣ
'687': ΑΝΥΠΟΤΑΚΤΟΙ
'688': ΔΙΕΥΘΥΝΣΗ ΤΕΛΩΝΕΙΩΝ ΘΕΣΣΑΛΟΝΙΚΗΣ
'689': ΠΕΡΙΦΕΡΕΙΕΣ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ
'690': ΑΣΦΑΛΙΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ Τ.Ε.Β.Ε
'691': ΣΙΔΗΡΟΣ
'692': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΕΜΠΟΡΙΟΥ
'693': ΔΙΑΧΕΙΡΙΣΗ ΙΣΡΑΗΛΙΤΙΚΩΝ ΠΕΡΟΥΣΙΩΝ
'694': ΛΙΠΟΤΑΞΙΑ
'695': ΒΑΡΕΑ ΚΑΙ ΑΝΘΥΓΙΕΙΝΑ ΕΠΑΓΓΕΛΜΑΤΑ
'696': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΜΗΧΑΝΗΜΑΤΩΝ
'697': ΛΕΩΦΟΡΕΙΑ ΠΕΡΙΟΧΗΣ ΠΡΩΤΕΥΟΥΣΑΣ
'698': ΑΝΑΜΟΡΦΩΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ
'699': ΥΓΕΙΟΝΟΜΙΚΟ ΣΩΜΑ
'700': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΕΡΓΑΣΙΑΣ
'701': ΔΙΩΡΥΓΑ ΚΟΡΙΝΘΟΥ
'702': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΑΣΦΑΛΙΣΜΕΝΩΝ
'703': ΚΟΙΝΩΝΙΚΟΣ ΕΛΕΓΧΟΣ ΔΙΟΙΚΗΣΗΣ - ΑΝΤΙΓΡΑΦΕΙΟΚΡΑΤΙΚΑ ΜΕΤΡΑ -ΕΚΚΑΘΑΡΙΣΗ
ΑΡΧΕΙΩΝ
'704': ΒΙΒΛΙΑ ΥΠΟΘΕΣΕΩΝ ΕΚΟΥΣΙΑΣ ΔΙΚΑΙΟΔΟΣΙΑΣ
'705': ΖΑΧΑΡΗ
'706': ΒΟΡΕΙΟΑΤΛΑΝΤΙΚΗ ΑΜΥΝΤΙΚΗ ΟΡΓΑΝΩΣΗ (Ν.Α.Τ.Ο)
'707': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΓΕΝΙΚΩΝ ΑΠΟΘΗΚΩΝ
'708': ΝΟΜΙΚΗ ΚΑΤΑΣΤΑΣΗ ΠΡΟΣΦΥΓΩΝ
'709': ΔΙΚΑΣΤΗΡΙΟ ΛΕΙΩΝ
'710': ΔΙΕΘΝΗΣ ΟΡΓΑΝΩΣΗ ΕΡΓΑΣΙΑΣ
'711': ΠΡΟΜΗΘΕΙΕΣ–ΜΙΣΘΩΣΕΙΣ–ΕΡΓΑ Ο.Γ.Α
'712': ΠΕΡΙΘΑΛΨΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α
'713': ΧΟΡΗΓΗΣΗ ΔΑΝΕΙΩΝ ΑΠΟ Τ.Π. ΚΑΙ ΔΑΝΕΙΩΝ
'714': ΤΕΛΟΣ ΕΠΙΤΗΔΕΥΜΑΤΟΣ
'715': ΕΛΕΥΘΕΡΑ ΤΕΛΩΝΕΙΑΚΑ ΣΥΓΚΡΟΤΗΜΑΤΑ
'716': ΦΟΡΟΛΟΓΙΚΑ ΚΙΝΗΤΡΑ ΣΥΓΧΩΝΕΥΣΕΩΣ Η ΜΕΤΑΤΡΟΠΗΣ ΕΠΙΧΕΙΡΗΣΕΩΝ
'717': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ T.E.B.E
'718': ΝΑΥΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ
'719': ΠΡΟΣΩΠΙΚΟ Υ.Ε.Ν
'720': ΛΕΙΤΟΥΡΓΟΙ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'721': ΚΟΙΝΟΠΡΑΞΙΑ ΓΕΩΡΓΙΚΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ
'722': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΠΙΧΕΙΡΗΜΑΤΙΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΟΥ
'723': ΒΟΣΚΟΤΟΠΟΙ
'724': ΕΠΙΤΟΚΙΑ ΤΡΑΠΕΖΩΝ
'725': ΚΑΠΝΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ
'726': ΣΤΑΘΜΟΙ ΑΥΤΟΚΙΝΗΤΩΝ
'727': ΕΥΛΟΓΙΑ
'728': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΒΙΟΜΗΧΑΝΙΑΣ
'729': ΤΑΜΕΙΟ ΑΕΡΟΠΟΡΙΚΗΣ ΑΜΥΝΑΣ
'730': ΟΡΓΑΝΙΣΜΟΣ ΚΕΝΤΡΙΚΗΣ ΥΠΗΡΕΣΙΑΣ
'731': ΤΑΜΕΙΟ ΕΡΓΑΣΙΑΣ ΗΘΟΠΟΙΩΝ
'732': ΤΕΛΩΝΙΣΜΟΣ ΕΙΔΩΝ ΑΤΟΜΙΚΗΣ ΧΡΗΣΕΩΣ
'733': ΦΟΡΟΛΟΓΙΑ ΠΡΟΣΟΔΟΥ ΑΠΟ ΠΛΟΙΑ
'734': ΔΙΟΙΚΗΤΙΚΗ ΔΙΑΙΡΕΣΗΣ
'735': ΟΡΓΑΝΙΣΜΟΣ ΑΥΤΟΚΙΝΗΤΟΔΡΟΜΙΩΝ ΕΛΛΑΔΟΣ (Ο.Α.Ε.)
'736': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΑΜΕΣΗΣ ΒΟΗΘΕΙΑΣ (Ε.Κ.Α.Β.)
'737': ΓΝΩΜΟΔΟΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ
'738': ΔΙΑΘΗΚΗ
'739': ΑΓΩΓΕΣ ΔΙΑΤΡΟΦΗΣ
'740': ΦΑΡΜΑΚΕΥΤΙΚΟΙ ΣΥΛΛΟΓΟΙ
'741': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΓΕΩΡΓΙΚΩΝ
ΣΥΝΕΤΑΙΡΙΣΤΙΚΩΝ ΟΡΓΑΝΩΣΕΩΝ (Τ.Σ.Ε.Α.Π.Γ.Σ.Ο)
'742': ΕΠΙΔΟΜΑΤΑ ΔΙΑΦΟΡΑ
'743': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ
'744': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΧΗΜΙΚΩΝ (Τ.Ε.Α.Χ)
'745': ΠΡΟΑΓΩΓΕΣ ΚΑΙ ΠΡΟΣΟΝΤΑ ΠΥΡΟΣΒΕΣΤΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ
'746': ΟΔΟΙΠΟΡΙΚΑ ΕΞΟΔΑ ΠΡΟΣΩΠΙΚΟΥ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ
'747': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΤ’ ΙΔΙΑΝ
'748': ΠΡΟΣΤΑΣΙΑ ΚΑΤΑ ΤΗΣ ΦΥΛΛΟΞΗΡΑΣ
'749': ΟΡΓΑΝΙΣΜΟΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ
'750': ΠΡΑΤΗΡΙΑ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ
'751': ΘΡΗΣΚΕΥΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'752': ΔΙΑΔΙΚΑΣΙΑ ΑΝΑΓΚΑΣΤΙΚΩΝ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ ΑΚΙΝΗΤΩΝ
'753': ΔΙΕΡΜΗΝΕΙΣ
'754': ΣΧΕΔΙΑ ΑΛΛΩΝ ΠΟΛΕΩΝ
'755': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΑΕΡΟΠΟΡΙΑΣ
'756': ΗΜΕΡΟΛΟΓΙΟ ΜΗΧΑΝΗΣ
'757': ΚΕΝΤΡΟ ΕΛΛΗΝΙΚΗΣ ΓΛΩΣΣΑΣ
'758': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΣΕ ΑΡΤΟΠΟΙΕΙΑ
'759': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ
'760': ΜΕΤΑΦΡΑΣΤΙΚΑ ΓΡΑΦΕΙΑ
'761': ΠΡΟΔΙΑΓΡΑΦΕΣ ΜΕΛΕΤΩΝ
'762': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΕΘΝΙΚΗΣ
'763': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΣΥΜΒΟΛΑΙΟΓΡΑΦΩΝ
'764': ΙΑΤΡΟΔΙΚΑΣΤΙΚΗ ΑΜΟΙΒΗ
'765': ΕΦΟΡΙΕΣ ΚΑΠΝΟΥ – ΚΑΠΝΕΡΓΟΣΤΑΣΙΑ
'766': ΠΟΙΜΝΙΟΣΤΑΣΙΑ
'767': ΚΕΝΤΡΑ ΕΡΕΥΝΑΣ - ΕΡΕΥΝΗΤΙΚΑ ΙΝΣΤΙΤΟΥΤΑ
'768': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΔΙΚΗΓΟΡΩΝ
'769': ΟΙΝΟΠΑΡΑΓΩΓΗ ΣΑΜΟΥ
'770': ΙΜΑΤΙΣΜΟΣ Π. ΝΑΥΤΙΚΟΥ
'771': ΜΗΧΑΝΙΚΟΙ,ΑΡΧΙΤΕΚΤΟΝΕΣ,ΤΟΠΟΓΡΑΦΟΙ
'772': ΠΑΝΤΕΙΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΟΙΝΩΝΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'773': ΝΕΟΙ ΧΡΗΜΑΤΟΠΙΣΤΩΤΙΚΟΙ ΘΕΣΜΟΙ
'774': ΥΠΗΡΕΣΙΑ ΠΟΛΙΤΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'775': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΘΗΚΟΦΥΛΑΚΕΙΩΝ
'776': ΑΤΥΧΗΜΑΤΑ ΣΕ ΔΗΜΟΣΙΑ ΕΡΓΑ
'777': ΑΡΕΙΟΣ ΠΑΓΟΣ
'778': ΥΠΑΓΩΓΗ ΣΕ ΑΣΦΑΛΙΣΗ ΚΑΙ
'779': ΔΙΕΘΝΕΙΣ ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣΔΙΕΥΡΩΠΑΙΚΟ ΣΙΔΗΡΟΔΡΟΜΙΚΟ ΣΥΣΤΗΜΑ
'780': ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΘΕΩΡΗΣΗ Π. ΝΑΥΤΙΚΟΥ
'781': ΑΝΑΠΤΥΞΙΑΚΗ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΗ ΠΟΛΙΤΙΚΗ
'782': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ
'783': ΝΑΥΤΙΚΟ ΧΗΜΕΙΟ
'784': ΛΑΧΕΙΑ
'785': ΤΡΟΧΙΟΔΡΟΜΟΙ ΑΘΗΝΩΝ – ΠΕΙΡΑΙΩΣ
'786': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΛΙΠΑΣΜΑΤΩΝ ΤΑ.Π.Π.Ε.Λ
'787': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΑΝΟΙΚΟΔΟΜΗΣΗ
'788': ΑΓΟΡΑΠΩΛΗΣΙΑ ΚΑΠΝΟΥ
'789': ΠΕΡΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΔΙΕΘΝΩΝ ΜΕΤΑΦΟΡΩΝ
'790': ΑΛΙΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ
'791': ΣΥΜΒΟΥΛΙΑ ΚΑΙ ΕΠΙΤΡΟΠΕΣ
'792': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ
'793': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΑΣΕΜΝΩΝ ΔΗΜΟΣΙΕΥΜΑΤΩΝ
'794': ΓΕΩΡΓΙΚΟΙ ΣΤΑΘΜΟΙ
'795': ΝΑΞΙΩΤΙΚΗ ΣΜΥΡΙΔΑ
'796': ΑΝΑΣΤΟΛΗ ΠΡΟΣΕΛΕΥΣΕΩΣ ΕΦΕΔΡΩΝ
'797': ΕΚΠΑΙΔΕΥΣΗ ΧΩΡΟΦΥΛΑΚΗΣ
'798': ΑΣΦΑΛΙΣΗ ΕΞΑΓΩΓΙΚΩΝ ΠΙΣΤΩΣΕΩΝ
'799': ΘΕΡΑΠΑΙΝΙΔΕΣ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ
'800': ΕΠΙΤΡΟΠΗ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
'801': ΚΑΝΟΝΙΣΜΟΣ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ
'802': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'803': ΕΠΙΘΕΩΡΗΣΕΙΣ ΚΤΗΝΙΑΤΡΙΚΗΣ
'804': ΜΕΡΙΚΗ ΑΠΑΣΧΟΛΗΣΗ - ΦΑΣΟΝ - ΤΗΛΕΡΓΑΣΙΑ ΚΑΤ’ ΟΙΚΟΝ ΑΠΑΣΧΟΛΗΣΗ
'805': ΗΛΕΚΤΡΙΚΗ ΕΤΑΙΡΕΙΑ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ
'806': ΠΡΟΚΑΤΑΣΚΕΥΑΣΜΕΝΑΙ ΟΙΚΙΑΙ
'807': ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ
'808': ΣΥΜΦΩΝΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΤΟΥ ΠΕΡΙΒΑΛΛΟΝΤΟΣ
'809': ΛΙΓΝΙΤΗΣ
'810': ΤΑΜΕΙΟ ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΤΑ
'811': ΜΕΛΕΤΕΣ ΤΕΧΝΙΚΩΝ ΕΡΓΩΝ
'812': ΠΛΗΡΩΜΑΤΑ ΑΕΡΟΣΚΑΦΩΝ
'813': ΕΞΑΓΩΓΗ ΣΤΑΦΙΔΑΣ
'814': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'815': ΔΙΑΧΕΙΡΙΣΗ ΠΕΡΙΟΥΣΙΑΣ
'816': ΟΡΓΑΝΙΚΟΙ ΝΟΜΟΙ
'817': ΥΠΗΡΕΣΙΕΣ ΑΙΜΟΔΟΣΙΑΣ
'818': ΣΩΜΑΤΕΙΑ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'819': ΠΕΖΟΔΡΟΜΙΑ
'820': ΔΙΑΘΕΣΗ ΑΠΟΡΡΙΜΜΑΤΩΝ
'821': ΤΡΟΧΙΟΔΡΟΜΟΙ ΘΕΣΣΑΛΟΝΙΚΗΣ
'822': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΟΥ ΛΟΓΙΣΤΙΚΟΥ
'823': ΡΥΜΟΥΛΚΑ - ΛΑΝΤΖΕΣ
'824': ΠΕΤΡΕΛΑΙΟΕΙΔΗ
'825': ΓΕΝΙΚΑ ΑΡΧΕΙΑ ΤΟΥ ΚΡΑΤΟΥΣ
'826': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ Ο.Τ.Ε. - ΣΧΕΣΕΙΣ Ο.Τ.Ε. ΜΕ ΑΛΛΟΥΣ ΠΑΡΟΧΟΥΣ
'827': ΥΠΗΡΕΣΙΑ ΑΥΤΟΚΙΝΗΤΩΝ
'828': ΑΚΑΔΗΜΙΑ ΑΘΗΝΩΝ
'829': ΜΟΝΟΠΩΛΙΟ ΖΑΧΑΡΙΝΗΣ
'830': ΟΙΚΙΣΤΙΚΕΣ ΠΕΡΙΟΧΕΣ
'831': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΑΛΙΕΙΑΣ
'832': ΔΙΑΦΟΡΕΣ ΕΚΤΑΚΤΕΣ ΦΟΡΟΛΟΓΙΕΣ
'833': ΒΙΒΛΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'834': ΕΡΓΑΤΙΚΑ ΑΤΥΧΗΜΑΤΑ
'835': ΝΟΣΗΛΕΥΤΕΣ
'836': ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΕΛΕΥΘΕΡΙΕΣ
'837': ΕΘΝΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ
'838': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΥΑΛΟΥΡΓΩΝ
'839': ΑΓΩΓΕΣ ΑΣΦΑΛΙΣΤΡΩΝ
'840': ΣΩΜΑΤΕΜΠΟΡΙΑ ΓΥΝΑΙΚΩΝ
'841': ΑΤΕΛΕΙΕΣ ΕΡΓΩΝ ΑΜΥΝΤΙΚΟΥ ΠΡΟΓΡΑΜΜΑΤΟΣ
'842': ΤΕΧΝΙΚΗ ΕΚΠΑΙΔΕΥΣΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΕ ΑΝΩΤΑΤΕΣ ΣΧΟΛΕΣ
'843': ΔΙΚΑΙΩΜΑΤΑ ΚΗΡΥΚΩΝ ΚΛΠ
'844': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ
'845': ΝΑΥΤΕΣ ΚΑΙ ΛΙΜΕΝΟΦΥΛΑΚΕΣ
'846': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΣΧΟΛΗ ΑΓΡΙΝΙΟΥ
'847': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ
'848': ΜΕΙΩΣΗ ΕΙΣΦΟΡΩΝ
'849': ΚΕΝΤΡΑ ΛΗΨΕΩΣ ΤΙΜΩΝ ΣΦΑΓΕΙΩΝ
'850': ΑΠΟΔΗΜΙΑ ΣΤΡΑΤΕΥΣΙΜΩΝ
'851': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΚΟΙΝΗΣ ΔΙΑΝΟΜΗΣ ΠΩΛΗΤΩΝ
ΒΕΝΖΙΝΗΣ ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ
'852': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ ΠΕΡΙΘΑΛΨΗ
'853': ΝΟΣΗΛΕΥΤΙΚΑ ΙΔΡΥΜΑΤΑ
'854': ΓΕΝΙΚΑ ΠΕΡΙ ΜΟΥΣΕΙΩΝ
'855': ΑΣΦΑΛΕΙΑ ΟΧΥΡΩΝ ΘΕΣΕΩΝ
'856': ΓΕΩΡΓΙΚΑ ΜΗΧΑΝΗΜΑΤΑ
'857': ΤΑΜΕΙΑ ΣΥΝΕΡΓΑΣΙΑΣ
'858': ΙΔΙΩΤΙΚΕΣ ΚΛΙΝΙΚΕΣ ΚΑΙ ΕΡΓΑΣΤΗΡΙΑ
'859': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΙΠΤΑΜΕΝΩΝ
'860': ΔΙΑΦΟΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΧΟΛΕΣ
'861': ΓΥΝΑΙΚΕΣ ΝΟΣΟΚΟΜΟΙ
'862': ΦΟΙΤΗΣΗ, ΒΑΘΜΟΛΟΓΙΑ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ. Α.Σ.Κ.Τ
'863': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΔΙΑΦΟΡΟΙ
'864': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ
'865': ΚΩΔΙΚΟΠΟΙΗΣΗ ΤΗΣ ΝΟΜΟΘΕΣΙΑΣ
'866': ΜΕΤΑ ΤΗΣ ΓΑΛΛΙΑΣ
'867': ΓΕΩΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ
'868': ΕΙΔΗ ΠΑΡΑΔΙΔΟΜΕΝΑ ΣΤΗΝ ΕΛΕΥΘΕΡΗ ΧΡΗΣΗ
'869': ΜΟΝΟΠΩΛΙΟ ΣΠΙΡΤΩΝ
'870': ΚΑΤΑΣΤΑΤΙΚΟΝ Τ.Α.Κ.Ε
'871': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΥΠΑΛΛΗΛΩΝ ΑΣΤΥΝΟΜΙΑΣ ΠΟΛΕΩΝ (Ε.Τ.Υ.Α.Π.)
'872': ΜΙΣΘΟΔΟΣΙΑ ΙΕΡΕΩΝ – ΕΝΟΡΙΑΚΗ ΕΙΣΦΟΡΑ
'873': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'874': ΝΟΜΟΣ ΠΕΡΙ ΚΤΗΜΑΤΙΚΩΝ ΤΡΑΠΕΖΩΝ
'875': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΥΔΡΑΥΛΙΚΩΝ ΔΥΝΑΜΕΩΝ
'876': ΑΝΑΠΗΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΟΠΛΙΤΕΣ ΕΙΡΗΝΙΚΗΣ ΠΕΡΙΟΔΟΥ
'877': ΠΟΙΝΙΚΗ ΚΑΙ ΠΕΙΘΑΡΧΙΚΗ ΔΩΣΙΔΙΚΙΑ Λ.Σ
'878': ΔΑΣΙΚΟ ΠΡΟΣΩΠΙΚΟ
'879': ΑΟΠΛΗ ΘΗΤΕΙΑ-ΑΝΤΙΡΡΗΣΙΕΣ ΣΥΝΕΙΔΗΣΗΣ
'880': ΝΕΟΙ ΠΡΟΣΦΥΓΕΣ
'881': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ ΣΤΡΑΤΟΥ
'882': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΠΟΛΙΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'883': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΟΥ ΔΙΚΑΙΟΥ
'884': ΚΩΔΙΚΑΣ ΑΓΡΟΤΙΚΗΣ ΑΣΦΑΛΕΙΑΣ
'885': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΑΠΟΣΤΟΛΙΚΗΣ ΔΙΑΚΟΝΙΑΣ
'886': ΥΠΟΥΡΓΕΙΟ ΑΙΓΑΙΟΥ
'887': ΓΑΜΟΙ ΔΩΔΕΚΑΝΗΣΟΥ
'888': ΩΡΕΣ ΕΡΓΑΣΙΑΣ ΚΡΕΟΠΩΛΕΙΩΝ
'889': ΚΩΔΙΚΑΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ
'890': ΔΕΛΤΙΟ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ
'891': ΑΡΜΟΔΙΟΤΗΤΑ ΝΟΜΑΡΧΗ ΣΕ ΕΡΓΑΤΙΚΑ ΖΗΤΗΜΑΤΑ
'892': ΤΡΟΦΟΔΟΣΙΑ Π. ΝΑΥΤΙΚΟΥ
'893': ΣΥΜΦΩΝΙΑ ΠΕΡΙ ΔΙΠΛΩΜΑΤΙΚΩΝ ΣΧΕΣΕΩΝ
'894': ΕΦΕΔΡΟΙ ΚΑΙ ΕΠΙΚΟΥΡΟΙ ΑΞΙΩΜΑΤΙΚΟΙ Π.Ν
'895': ΤΟΥΡΙΣΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ
'896': ΔΙΕΘΝΕΣ ΠΟΙΝΙΚΟ ΔΙΚΑΣΤΗΡΙΟ
'897': ΔΙΟΙΚΗΤΙΚΕΣ ΠΡΑΞΕΙΣ
'898': ΝΟΣΟΚΟΜΕΙΑ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'899': ΣΥΜΒΟΥΛΙΟ ΧΑΛΥΒΑ
'900': ΤΕΜΑΧΙΣΜΟΣ ΚΡΕΑΤΩΝ
'901': ΕΛΕΓΧΟΣ ΚΑΤΟΧΗΣ ΟΠΛΩΝ
'902': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΤΗΣ ΔΡΑΧΜΗΣ
'903': ΕΦΟΔΙΑΣΜΟΣ ΠΛΟΙΩΝ
'904': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΙΟΝΙΩΝ ΝΗΣΩΝ
'905': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΚΙΝΗΤΩΝ ΑΞΙΩΝ ΑΝΩΝΥΜΗ ΕΤΑΙΡΕΙΑ (Δ.Ε.Κ.Α. Α.Ε.)
'906': ΕΤΑΙΡΕΙΑ – ΕΥΡΩΠΑΙΚΟΣ ΟΜΙΛΟΣ
'907': ΔΙΕΥΘΥΝΣΗ ΑΛΙΕΙΑΣ
'908': ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΟΥΡΙΣΤΙΚΩΝ ΚΑΤΑΣΤΗΜΑΤΩΝ
'909': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΕΛΑΙΟΛΑΔΟΥ
'910': ΠΤΗΤΙΚΗ ΙΚΑΝΟΤΗΤΑ
'911': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΣΧΟΛΕΣ
'912': ΔΙΑΤΙΜΗΣΗ ΙΑΤΡΙΚΩΝ ΠΡΑΞΕΩΝ
'913': ΑΔΙΚΗΜΑΤΑ ΤΥΠΟΥ
'914': ΕΞΑΝΘΗΜΑΤΙΚΟΣ ΤΥΦΟΣ
'915': ΟΙΚΟΣ ΝΑΥΤΟΥ
'916': ΜΑΣΤΙΧΑ
'917': ΣΥΛΛΟΓΟΙ ΚΑΙ ΟΜΟΣΠΟΝΔΙΑ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ
'918': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ
'919': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΑΝΩΤΑΤΩΝ ΕΚΠΑΙΔΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ
'920': ΥΓΕΙΟΝΟΜΙΚΗ ΑΠΟΘΗΚΗ
'921': ΓΕΝ. ΔΙΕΥΘΥΝΣΗ ΠΟΙΝΙΚΗΣ ΔΙΚΑΙΟΣΥΝΗΣ
'922': ΑΕΡΟΠΟΡΙΚΟ ΔΙΚΑΙΟ
'923': ΜΕΛΕΤΗ ΚΑΙ ΕΠΙΒΛΕΨΗ ΜΗΧΑΝΟΛΟΓΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ
'924': ΑΘΕΜΙΤΟΣ ΑΝΤΑΓΩΝΙΣΜΟΣ
'925': ΠΟΛΕΜΙΚΗ ΔΙΑΘΕΣΙΜΟΤΗΤΑ
'926': ΛΕΣΧΕΣ ΚΑΙ ΠΡΑΤΗΡΙΑ ΕΛ.ΑΣ
'927': ΚΑΥΣΙΜΑ
'928': ΥΓΕΙΟΝΟΜΙΚΑ ΜΕΤΡΑ
'929': ΚΑΤΑΣΤΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ
'930': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ
'931': ΔΙΟΙΚΗΤΙΚΗ ΡΥΘΜΙΣΗ ΑΠΟΔΟΧΩΝ ΚΑΙ ΟΡΩΝ ΕΡΓΑΣΙΑΣ
'932': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΑΧΥΔΡΟΜΕΙΩΝ
'933': ΟΡΓΑΝΙΣΜΟΣ ΛΙΜΕΝΟΣ ΘΕΣΣΑΛΟΝΙΚΗΣ ΑΝΩΝΥΜΗ ΕΤΑΙΡΙΑ (Ο.Λ.Θ. Α.Ε.)
'934': ΣΧΟΛΗ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ
'935': ΚΑΘΟΛΙΚΟΙ
'936': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΜΟΥΣΕΙΑ
'937': ΔΙΕΘΝΗΣ ΕΚΘΕΣΗ ΘΕΣΣΑΛΟΝΙΚΗΣ Α.Ε. – XELEXPO Α.Ε
'938': ΕΥΕΡΓΕΤΙΚΟΣ ΥΠΟΛΟΓΙΣΜΟΣ ΗΜΕΡΩΝ ΕΡΓΑΣΙΑΣ
'939': ΕΙΣΦΟΡΑ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΚΙΝΔΥΝΟΥ
'940': ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ ΓΙΑ ΤΟΥΡΙΣΤΙΚΟΥΣ ΣΚΟΠΟΥΣ
'941': ΑΠΟΛΥΜΑΝΤΗΡΙΑ
'942': ΕΚΠΟΙΗΣΗ ΠΛΟΙΩΝ ΔΗΜΟΣΙΟΥ
'943': ΔΙΑΚΟΝΟΙ
'944': ΥΔΡΕΥΣΗ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ
'945': ΠΡΩΤΕΣ ΥΛΕΣ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ
'946': ΨΕΥΔΗΣ ΒΕΒΑΙΩΣΗ ΕΝΩΠΙΟΝ ΑΡΧΗΣ
'947': ΑΠΩΛΕΣΘΕΙΣΕΣ ΚΑΙ ΠΑΡΑΓΡΑΦΕΙΣΕΣ ΑΞΙΕΣ
'948': ΦΟΙΤΗΤΙΚΗ ΛΕΣΧΗ
'949': ΤΑΜΕΙΟ ΥΓΕΙΑΣ ΤΑΧΥΔΡΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ
'950': ΕΛΕΓΧΟΣ ΔΕΝΔΡΩΔΩΝ ΚΑΛΛΙΕΡΓΕΙΩΝ
'951': ΚΑΤΑΠΟΛΕΜΗΣΗ ΑΝΑΛΦΑΒΗΤΙΣΜΟΥΛΑΙΚΗ ΕΠΙΜΟΡΦΩΣΗ
'952': ΕΠΙΚΟΥΡΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΜΕΤΑΦΟΡΩΝ
'953': ΦΟΙΤΗΤΙΚΕΣ ΛΕΣΧΕΣ
'954': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΓΥΝΑΙΚΩΝ
'955': ΛΗΣΤΕΙΑ
'956': ΑΓΩΓΕΣ ΑΠΟ ΣΥΝΑΛΛΑΓΜΑΤΙΚΕΣ ΚΑΙ ΓΡΑΜΜΑΤΙΑ
'957': ΕΚΜΙΣΘΩΣΗ ΔΗΜΟΣΙΩΝ ΜΕΤΑΛΛΕΙΩΝ
'958': ΚΟΛΥΜΒΗΤΙΚΕΣ ΔΕΞΑΜΕΝΕΣ
'959': ΕΡΑΝΟΙ ΚΑΙ ΛΑΧΕΙΟΦΟΡΟΙ Η ΦΙΛΑΝΘΡΩΠΙΚΕΣ ΑΓΟΡΕΣ
'960': ΠΡΟΣΤΑΣΙΑ ΕΠΙΒΑΤΗΓΟΥ ΝΑΥΤΙΛΙΑΣ
'961': ΓΕΝΙΚΟΙ ΝΟΜΟΙ ΠΕΡΙ ΞΕΝΟΔΟΧΕΙΩΝ-ΕΠΙΠΛ. ΔΩΜΑΤΙΩΝ ΚΛΠ
'962': ΙΕΡΑΡΧΙΑ ΚΑΙ ΠΡΟΑΓΩΓΕΣ ΑΞΙΩΜΑΤΙΚΩΝ
'963': ΣΥΝΕΡΓΑΤΕΣ (ΓΡΑΜΜΑΤΕΙΣ) ΒΟΥΛΕΥΤΩΝ-ΕΥΡΩΒΟΥΛΕΥΤΩΝ
'964': ΣΧΟΛΗ ΙΚΑΡΩΝ
'965': ΟΡΓΑΝΙΣΜΟΣ ΣΙΔΗΡΟΔΡΟΜΩΝ ΕΛΛΑΔΟΣ (Ο.Σ.Ε.)ΣΙΔΗΡΟΔΡΟΜΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ
'966': ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ ΚΑΤΑ ΘΑΛΑΣΣΑΝ ΚΑΙ ΚΑΤΑ ΞΗΡΑΝ
'967': ΚΑΝΟΝΙΣΜΟΣ ΜΕΤΑΛΛΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ
'968': ΑΠΟΦΥΓΗ ΣΥΓΚΡΟΥΣΕΩΝ
'969': ΤΟΜΑΤΟΠΑΡΑΓΩΓΗ
'970': ΔΙΑΦΟΡΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ
'971': ΚΑΤΑΤΑΞΗ ΓΥΝΑΙΚΩΝ ΣΤΟ Λ.Σ
'972': ΕΤΑΙΡΕΙΕΣ ΔΙΟΙΚΟΥΜΕΝΕΣ ΑΠΟ ΤΟΥΣ ΠΙΣΤΩΤΕΣ
'973': ΒΑΛΚΑΝΙΚΕΣ ΣΥΜΦΩΝΙΕΣ
'974': ΜΕΤΑΦΟΡΑ ΣΥΝΤΕΛΕΣΤΗ ΔΟΜΗΣΗΣ
'975': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Π.Ν
'976': ΠΡΟΣΩΠΙΚΟ ΦΑΡΜΑΚΕΙΩΝ
'977': ΔΙΔΑΣΚΟΜΕΝΑ ΜΑΘΗΜΑΤΑ
'978': ΕΚΛΟΓΗ ΒΟΥΛΕΥΤΩΝ - ΕΥΡΩΒΟΥΛΕΥΤΩΝ
'979': ΦΑΡΜΑΚΟΠΟΙΟΙ
'980': ΣΤΡΑΤΙΩΤΙΚΑ ΠΡΑΤΗΡΙΑ
'981': ΚΑΡΚΙΝΟΣ
'982': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Α.Ε. ΟΙΝΟΠΟΙΙΑΣ, ΖΥΘΟΠΟΙΙΑΣ
ΚΑΙ ΟΙΝΟΠΝΕΥΜΑΤΟΠΟΙΙΑΣ
'983': ΧΕΙΡΙΣΤΕΣ ΑΣΥΡΜΑΤΟΥ
'984': ΠΟΛΙΤΙΚΗ ΕΠΙΣΤΡΑΤΕΥΣΗ-ΠΑΛΛΑΙΚΗ ΑΜΥΝΑ
'985': ΟΡΓΑΝΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ
'986': ΟΜΟΓΕΝΕΙΣ ΠΑΛΛΙΝΟΣΤΟΥΝΤΕΣ
'987': ΕΥΡΩΠΑΙΚΟΣ ΚΟΙΝΩΝΙΚΟΣ ΧΑΡΤΗΣ
'988': ΟΡΓΑΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'989': ΕΞΑΙΡΕΣΗ ΔΙΚΑΣΤΩΝ
'990': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ – ΔΙΕΥΘΥΝΣΕΙΣ ΣΤΟΙΧΕΙΩΔΟΥΣ ΕΚΠΑΙΔΕΥΣΗΣ
'991': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΘΕΩΡΗΣΕΩΣ ΚΑΙ ΑΣΦΑΛΕΙΑΣ
'992': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΑΥΤΟΝΟΜΟΥ ΣΤΑΦΙΔΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ (Τ.Α.Π.Α.Σ.Ο)
'993': ΤΑΜΕΙΟΝ ΠΡΟΝΟΙΑΣ ΟΡΘΟΔΟΞΟΥ ΕΦΗΜΕΡΙΑΚΟΥ
'994': ΣΧΟΛΙΚΗ ΣΩΜΑΤΙΚΗ ΑΓΩΓΗ
'995': ΚΕΝΤΡΟ ΠΑΡΑΓΩΓΙΚΟΤΗΤΑΣ
'996': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΙΔΙΟΚΤΗΤΩΝ
'997': ΒΟΣΚΗ ΕΝΤΟΣ ΔΑΣΩΝ
'998': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΓΕΩΡΓΙΚΩΝ ΠΡΟΙΟΝΤΩΝ
'999': ΠΑΙΔΑΓΩΓΙΚΑ ΤΜΗΜΑΤΑ Α.Ε.Ι
'1000': ΥΠΟΤΡΟΦΙΕΣ ΚΛΗΡΟΔΟΤΗΜΑΤΟΣ Π. ΒΑΣΣΑΝΗ
'1001': ΑΤΥΧΗΜΑ ΑΠΟ ΔΟΛΟ ΤΟΥ ΕΡΓΟΔΟΤΗ
'1002': ΒΥΖΑΝΤΙΝΟ ΚΑΙ ΧΡΙΣΤΙΑΝΙΚΟ ΜΟΥΣΕΙΟ
'1003': ΕΙΡΗΝΕΥΤΙΚΕΣ ΑΠΟΣΤΟΛΕΣ
'1004': ΥΓΕΙΟΝΟΜΙΚΟΣ ΄ΕΛΕΓΧΟΣ ΕΙΣΕΡΧΟΜΕΝΩΝ
'1005': ΟΡΚΟΣ ΤΟΥ ΠΟΛΙΤΗ
'1006': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΣΠΟΥΔΑΣΤΩΝ
'1007': ΠΑΡΑΧΑΡΑΞΗ ΚΑΙ ΚΙΒΔΗΛΙΑ
'1008': ΔΙΑΜΕΡΙΣΜΑΤΑ ΠΛΟΙΑΡΧΩΝ ΚΑΙ ΠΛΗΡΩΜΑΤΩΝ
'1009': ΚΛΑΔΟΣ ΑΡΩΓΗΣ Τ.Α.Κ.Ε
'1010': ΟΡΓΑΝΙΣΜΟΣ ΒΑΜΒΑΚΟΣ
'1011': ΝΟΣΗΛΕΙΑ ΣΤΡΑΤΙΩΤΙΚΩΝ
'1012': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ
'1013': ΠΟΛΥΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1014': ΝΑΥΤΙΚΟ ΑΠΟΜΑΧΙΚΟ ΤΑΜΕΙΟ
'1015': ΥΓΙΕΙΝΗ ΑΡΤΟΠΟΙΕΙΩΝ
'1016': ΝΟΜΑΡΧΙΑΚΑ ΣΥΜΒΟΥΛΙΑ
'1017': ΛΕΣΧΗ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'1018': ΚΑΤΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ
'1019': ΓΕΝΙΚΑ ΠΕΡΙ ΚΥΚΛΟΦΟΡΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝ
'1020': ΤΑΜΕΙΟ ΝΟΣΗΛΕΙΑΣ ΣΠΟΥΔΑΣΤΩΝ
'1021': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΚΑΙ ΒΙΟΤΕΧΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ
'1022': ΑΚΤΟΠΛΟΙΑ
'1023': ΠΡΟΣΤΑΣΙΑ ΑΛΙΕΙΑΣ
'1024': ΜΕ ΤΗ ΝΟΡΒΗΓΙΑ
'1025': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΠΡΟΣΩΠΙΚΟΥ (΄ΕΝΟΠΛΟΥ-ΠΟΛΙΤΙΚΟΥ) ΥΠΟΥΡΓΕΙΟΥ ΔΗΜΟΣΙΑΣ
ΤΑΞΗΣ
'1026': ΛΕΩΦΟΡΕΙΑ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΕΩΣ
'1027': ΕΡΓΑΤΙΚΕΣ ΔΙΑΦΟΡΕΣ
'1028': ΡΑΔΙΟΗΛΕΚΤΡΟΛΟΓΟΙ-ΡΑΔΙΟΤΕΧΝΙΤΕΣ
'1029': ΠΡΟΓΝΩΣΤΙΚΑ ΠΟΔΟΣΦΑΙΡΟΥ
'1030': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΗΣ ΑΓΡΟΤΙΚΗΣ ΤΡΑΠΕΖΑΣ
ΤΗΣ ΕΛΛΑΔΑΣ (Τ.Σ.Π. – Α.Τ.Ε.)
'1031': ΥΔΡΕΥΣΗ ΛΕΚΑΝΟΠΕΔΙΟΥ ΑΘΗΝΩΝ
'1032': ΤΡΑΠΕΖΑ ΟΦΘΑΛΜΩΝ
'1033': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΧΑΡΤΩΝ ΚΑΙ ΧΑΡΤΟΓΡΑΦΙΚΗΣ ΚΛΗΡΟΝΟΜΙΑΣ - ΕΘΝΙΚΗ ΧΑΡΤΟΘΗΚΗ
'1034': ΚΑΝΟΝΙΣΜΟΙ ΑΠΟΦΥΓΗΣ ΣΥΓΚΡΟΥΣΕΩΝ
'1035': ΓΡΑΦΕΙΟ ΕΓΚΛΗΜΑΤΙΩΝ ΠΟΛΕΜΟΥ
'1036': ΑΓΡΟΤΙΚΕΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ
'1037': ΤΑΥΤΟΤΗΤΕΣ
'1038': ΔΑΣΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ
'1039': ΣΥΜΒΟΛΑΙΟΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ
'1040': ΙΔΙΟΚΤΗΣΙΑ ΚΑΤ’ ΟΡΟΦΟ
'1041': ΣΧΟΛΙΚΑ ΤΑΜΕΙΑ
'1042': ΑΡΧΕΙΟΦΥΛΑΚΕΙΑ ΔΙΑΦΟΡΑ
'1043': ΑΠΟΖΗΜΙΩΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ
'1044': ΣΧΟΛΙΚΑ ΚΤΙΡΙΑ
'1045': ΦΟΡΟΛΟΓΙΑ ΟΙΚΟΔΟΜΩΝ
'1046': ΠΡΟΤΥΠΑ ΔΗΜΟΤΙΚΑ
'1047': ΠΡΩΤΕΣ ΥΛΕΣ ΒΥΡΣΟΔΕΨΙΑΣ - ΔΕΡΜΑΤΑ
'1048': ΣΥΜΒΙΒΑΣΜΟΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑ
'1049': ΚΑΤΑΣΤΑΣΗ ΔΗΜΟΤΙΚΩΝ ΚΑΙ ΚΟΙΝΟΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'1050': ΕΣΟΔΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'1051': ΣΤΑΔΙΑ ΚΑΙ ΓΥΜΝΑΣΤΗΡΙΑ
'1052': ΚΟΙΝΗ ΑΓΡΟΤΙΚΗ ΠΟΛΙΤΙΚΗ
'1053': ΑΤΟΜΑ ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ - ΥΠΕΡΗΛΙΚΕΣ - ΧΡΟΝΙΑ ΠΑΣΧΟΝΤΕΣ
'1054': ΕΚΚΛΗΣΙΑΣΤΙΚΑ ΔΙΚΑΣΤΗΡΙΑ
'1055': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΑΠΟΦΥΓΗ ΔΙΠΛΗΣ ΦΟΡΟΛΟΓΙΑΣ
'1056': ΠΡΟΣΤΑΣΙΑ ΒΑΜΒΑΚΟΠΑΡΑΓΩΓΗΣ
'1057': ΝΑΥΤΙΚΗ ΣΤΡΑΤΟΛΟΓΙΑ
'1058': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ ΑΣΦΑΛΙΣΜΕΝΩΝ Ο.Γ.Α
'1059': ΦΥΣΙΚΑ ΟΡΓΑΝΙΚΑ ΛΙΠΑΣΜΑΤΑ
'1060': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΕΣΤΙΑΤΟΡΙΩΝ, ΖΑΧΑΡΟΠΛΑΣΤΕΙΩΝ,
ΚΑΦΕΝΕΙΩΝ Κ.ΛΠ. (Τ.Ε.Α.Μ.Ε.Ζ.)
'1061': ΤΕΧΝΙΚΑΙ ΥΠΗΡΕΣΙΑΙ
'1062': ΣΥΓΚΕΝΤΡΩΣΗ ΠΡΟΙΟΝΤΩΝ
'1063': ΥΔΡΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ
'1064': ΥΠΗΡΕΣΙΑ ΕΛΕΓΧΟΥ ΚΑΤΑΣΚΕΥΗΣ ΑΞΙΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ
'1065': ΕΠΙΣΚΟΠΙΚΑ ΓΡΑΦΕΙΑ
'1066': ΒΕΛΓΙΟ, ΒΕΝΕΖΟΥΕΛΑ Κ.ΛΠ
'1067': ΔΗΜΟΤΙΚΟΣ ΚΑΙ ΚΟΙΝΟΤΙΚΟΣ ΚΩΔΙΚΑΣ
'1068': ΠΡΟΔΟΣΙΑ
'1069': ΜΙΣΘΟΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'1070': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΝΑΥΤΙΚΟΥ
'1071': ΑΝΑΖΗΤΗΣΗ ΚΑΙ ΔΙΑΦΥΛΑΞΗ ΑΡΧΑΙΟΤΗΤΩΝ
'1072': ΑΔΕΙΕΣ ΛΙΑΝΙΚΗΣ ΠΩΛΗΣΗΣ ΤΣΙΓΑΡΩΝ ΚΑΙ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ
'1073': ΕΠΟΠΤΙΚΑ ΜΕΣΑ ΔΙΔΑΣΚΑΛΙΑΣ
'1074': ΕΚΛΟΓΟΔΙΚΕΙΑ
'1075': Ο.Γ.Α ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'1076': ΙΝΣΤΙΤΟΥΤΟ ΥΓΕΙΑΣ ΤΟΥ ΠΑΙΔΙΟΥ
'1077': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ
'1078': ΕΣΠΕΡΙΔΟΕΙΔΗ-ΟΠΩΡΟΚΗΠΕΥΤΙΚΑ
'1079': ΕΠΙΔΟΜΑΤΑ ΣΤΡΑΤΕΥΟΜΕΝΩΝ
'1080': ΠΡΟΛΗΨΗ ΕΡΓΑΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ ΤΩΝ ΝΑΥΤΙΚΩΝ
'1081': ΥΠΗΡΕΣΙΑ ΑΠΟΜΑΓΝΗΤΙΣΕΩΣ ΠΛΟΙΩΝ
'1082': ΔΙΑΦΟΡΕΣ ΕΙΔΙΚΕΣ ΔΙΑΔΙΚΑΣΙΕΣ
'1083': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ
'1084': ΕΘΝΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΗΡΟΦΟΡΙΩΝ (Ε.Υ.Π.)
'1085': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (T.E.A.M)
'1086': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΝΕΡΓΙΑΣ - ΟΡΓΑΝΙΣΜΟΣ ΑΠΑΣΧΟΛΗΣΗΣ ΕΡΓΑΤΙΚΟΥ ΔΥΝΑΜΙΚΟΥ
'1087': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΠΡΟΣΩΠΙΚΟΥ ΣΤΡΑΤΕΥΜΑΤΟΣ
'1088': ΟΙΚΟΝΟΜΙΚΗ ΥΠΗΡΕΣΙΑ Π. ΝΑΥΤΙΚΟΥ
'1089': ΔΑΣΙΚΗ ΦΟΡΟΛΟΓΙΑ
'1090': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ, ΜΕΛΙΣΣΟΚΟΜΙΑΣ Κ.Λ.Π
'1091': ΠΟΛΙΤΙΚΑ ΔΙΚΑΙΩΜΑΤΑ ΤΩΝ ΓΥΝΑΙΚΩΝ
'1092': ΜΕΤΑΘΕΣΕΙΣ ΕΚΠΑΙΔΕΥΤΙΚΩΝ
'1093': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΥΠΟΛΟΓΙΣΜΟΥ
'1094': ΔΙΑΧΕΙΡΙΣΗ ΔΑΣΩΝ
'1095': ΔΟΥΛΕΙΑ
'1096': ΜΕ ΤΗ ΠΟΛΩΝΙΑ
'1097': ΑΝΑΔΙΑΝΟΜΗ ΚΤΗΜΑΤΩΝ
'1098': ΥΠΟΑΠΑΣΧΟΛΟΥΜΕΝΟΙ ΜΙΣΘΩΤΟΙ
'1099': ΟΡΓΑΝΙΣΜΟΙ ΠΡΩΗΝ Υ.Β.Ε.Τ. - Γ.Γ.Β. - Γ.Γ.Ε.Τ
'1100': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ ΑΘΗΝΩΝ
'1101': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤ.ΕΤΑΙΡΕΙΑΣ Η ΕΘΝΙΚΗ (Τ.Α.Π.Α.Ε.
Η ΕΘΝΙΚΗ)
'1102': ΤΕΛΗ ΣΧΟΛΑΖΟΥΣΩΝ ΚΛΗΡΟΝΟΜΙΩΝ
'1103': ΞΕΝΕΣ ΓΛΩΣΣΕΣ
'1104': ΚΑΤΑΣΚΗΝΩΣΕΙΣ - ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ
'1105': ΔΙΚΑΣΤΗΡΙΑ ΑΝΗΛΙΚΩΝ
'1106': ΣΥΜΒΑΣΕΙΣ ΕΚΤΕΛΕΣΕΩΣ ΑΛΛΟΔΑΠΩΝ ΑΠΟΦΑΣΕΩΝ
'1107': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ
'1108': ΘΕΩΡΗΤΙΚΑ ΚΑΙ ΙΣΤΟΡΙΚΑ ΜΑΘΗΜΑΤΑ
'1109': ΑΦΡΟΔΙΣΙΑ
'1110': ΦΑΡΟΙ
'1111': ΔΗΜΟΣΙΟΓΡΑΦΙΚΟ ΕΠΑΓΓΕΛΜΑ
'1112': ΚΑΤΑΣΤΑΤΙΚΟΣ ΝΟΜΟΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ
'1113': ΕΛΕΓΧΟΣ ΣΚΟΠΙΜΟΤΗΤΑΣ ΙΔΡΥΣΕΩΣ ΒΙΟΜΗΧΑΝΙΩΝ
'1114': ΓΥΜΝΑΣΙΑ ΚΑΙ ΛΥΚΕΙΑ
'1115': ΑΕΡΟΝΑΥΤΙΚΕΣ ΠΛΗΡΟΦΟΡΙΕΣ
'1116': ΚΑΤΑΣΤΑΣΗ ΥΠΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'1117': ΥΠΟΥΡΓΕΙΟ ΧΩΡΟΤΑΞΙΑΣ
'1118': ΕΚΤΕΛΕΣΗ ΄ΕΡΓΩΝ
'1119': ΜΙΣΘΟΔΟΣΙΑ ΥΠΑΛΛΗΛΩΝ ΣΕ ΕΠΙΣΤΡΑΤΕΥΣΗ
'1120': ΚΟΙΜΗΤΗΡΙΑ
'1121': ΑΣΦΑΛΙΣΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΚΙΝΔΥΝΩΝ ΠΟΛΕΜΟΥ
'1122': ΣΥΜΦΩΝΙΑ ΓΙΑ ΑΝΙΘΑΓΕΝΕΙΣ
'1123': ΝΟΜΑΡΧΙΑΚΗ ΑΥΤΟΔΙΟΙΚΗΣΗ
'1124': ΣΧΟΛΗ ΤΟΥΡΙΣΤΙΚΩΝ ΕΠΑΓΓΕΛΜΑΤΩΝ
'1125': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΠΑΡΑΓΩΓΗΣ ΚΑΙ ΕΜΠΟΡΙΑΣ ΟΠΩΡΟΚΗΠΕΥΤΙΚΩΝ
'1126': ΑΠΟΛΥΜΑΝΣΗ ΥΔΑΤΩΝ
'1127': ΠΟΛΕΟΔΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ
'1128': ΟΡΓΑΝΙΣΜΟΣ ΕΚΔΟΣΕΩΣ ΣΧΟΛΙΚΩΝ ΒΙΒΛΙΩΝ
'1129': ΥΠΑΛΛΗΛΟΙ ΝΟΜ. ΠΡΟΣΩΠΩΝ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ
'1130': ΑΝΤΙΣΤΑΘΜΙΣΤΙΚΗ ΕΙΣΦΟΡΑ
'1131': ΠΡΟΣΩΠΙΚΟ ΙΔΙΩΤΙΚΩΝ ΕΚΠΑΙΔΕΥΤΗΡΙΩΝ
'1132': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΑ ΑΥΤΟΚΙΝΗΤΑ
'1133': ΕΞΩΣΧΟΛΙΚΗ ΑΓΩΓΗ
'1134': ΑΣΦΑΛΙΣΤΙΚΗ ΑΡΜΟΔΙΟΤΗΤΑ
'1135': ΕΛΙΕΣ ΚΑΙ ΕΛΑΙΑ
'1136': ΓΑΜΟΙ ΙΣΡΑΗΛΙΤΩΝ
'1137': ΤΑΜΕΙΟ ΑΡΤΟΥ
'1138': ΚΑΝΟΝΙΣΜΟΣ ΕΠΙΤΡΟΠΩΝ
'1139': ΣΥΜΒΑΣΗ ΚΑΤΑ ΔΑΓΚΕΙΟΥ
'1140': ΕΘΝΙΚΟΙ ΔΡΥΜΟΙ
'1141': ΑΠΑΛΛΑΓΕΣ ΤΕΛΩΝ ΧΑΡΤΟΣΗΜΟΥ
'1142': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΝΑΠΤΥΞΕΩΣ
'1143': ΚΑΝΟΝΙΣΜΟΣ ΕΡΓΑΣΙΑΣ ΕΠΙ ΦΟΡΤΗΓΩΝ ΠΛΟΙΩΝ
'1144': ΛΥΣΣΑ
'1145': ΑΓΡΟΚΤΗΜΑ
'1146': ΚΑΘΗΓΗΤΕΣ ΚΑΙ ΥΦΗΓΗΤΕΣ
'1147': ΠΑΙΔΙΚΟΙ - ΒΡΕΦΟΝΗΠΙΑΚΟΙ ΣΤΑΘΜΟΙ
'1148': ΚΕΝΤΡΟ ΒΥΖΑΝΤΙΝΩΝ ΕΡΕΥΝΩΝ
'1149': ΙΔΡΥΣΗ ΕΛΕΥΘΕΡΗΣ ΖΩΝΗΣ ΣΕ ΔΙΑΦΟΡΑ ΛΙΜΑΝΙΑ ΤΗΣ ΧΩΡΑΣ
'1150': ΣΧΟΛΙΚΑ ΛΕΩΦΟΡΕΙΑ
'1151': ΣΦΑΓΕΙΑ
'1152': ΕΠΙΚΥΡΩΣΗ ΝΟΜΟΘΕΤΗΜΑΤΩΝ
'1153': ΕΓΓΡΑΦΑ ΤΑΥΤΟΤΗΤΑΣ ΝΑΥΤΙΚΩΝ
'1154': ΑΤΟΜΙΚΑ ΔΙΚΑΙΩΜΑΤΑ - ΔΕΔΟΜΕΝΑ ΠΡΟΣΩΠΙΚΟΥ ΧΑΡΑΚΤΗΡΑ
'1155': ΙΑΤΡΟΦΑΡΜΑΚΕΥΤΙΚΗ - ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ - ΕΞΟΔΑ ΚΗΔΕΙΑΣ
'1156': ΥΠΗΡΕΣΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ
'1157': ΣΤΟΛΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ
'1158': ΠΕΡΙΦΡΑΞΗ ΟΙΚΟΠΕΔΩΝ
'1159': ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΤΤΙΚΗΣ
'1160': ΤΡΑΧΩΜΑΤΑ
'1161': ΝΑΥΑΓΙΑ-ΝΑΥΑΓΙΑΙΡΕΣΗ
'1162': ΥΠΟΜΗΧΑΝΙΚΟΙ
'1163': ΤΑΙΝΙΟΘΗΚΗ ΤΗΣ ΕΛΛΑΔΟΣ
'1164': ΚΑΝΟΝΙΣΜΟΣ ΤΗΛΕΓΡΑΦΙΚΗΣ ΥΠΗΡΕΣΙΑΣ
'1165': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΤΡΟΜΟΚΡΑΤΙΑΣ
'1166': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΙΜΑΧΟΥ ΠΡΟΣΤΑΣΙΑΣ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ
'1167': ΑΤΟΜΙΚΑ ΒΙΒΛΙΑΡΙΑ
'1168': ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΒΙΒΛΙΑΡΙΑ ΑΡΤΕΡΓΑΤΩΝ ΚΛΠ
'1169': ΦΟΡΟΛΟΓΙΑ ΑΜΥΛΟΣΙΡΟΠΙΟΥ, ΣΤΑΦΙΔΙΝΗΣ ΚΛΠ
'1170': ΜΟΥΣΕΙΟ ΕΛΛΗΝΙΚΩΝ ΛΑΙΚΩΝ ΟΡΓΑΝΩΝ
'1171': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΠΕΡΙΘΑΛΨΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΗΛΕΚΤΡ.
ΕΤΑΙΡΙΑΣ (Ε.Η.Ε.)
'1172': ΤΑΜΕΙΑ ΜΟΝΙΜΩΝ ΟΔΟΣΤΡΩΜΑΤΩΝ
'1173': ΟΡΓΑΝΙΚΕΣ ΘΕΣΕΙΣ ΑΞΙΩΜΑΤΙΚΩΝ Π.Ν
'1174': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΑΣ ΑΘΗΝΩΝ
'1175': ΠΟΛΙΟΜΥΕΛΙΤΙΔΑ
'1176': ΠΡΟΑΓΩΓΑΙ ΑΞΙΩΜΑΤΙΚΩΝ ΧΩΡΟΦΥΛΑΚΗΣ
'1177': ΕΠΙΔΟΜΑ ΑΔΕΙΑΣ
'1178': ΕΞΕΤΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΛΗΨΗ ΠΡΟΣΩΠΙΚΟΥ
'1179': ΕΛΕΓΧΟΣ ΕΞΑΓΩΓΙΚΟΥ ΕΜΠΟΡΙΟΥ
'1180': ΡΑΔΙΟΦΩΝΙΚΟΙ ΣΤΑΘΜΟΙ
'1181': ΚΑΝΟΝΙΣΜΟΣ ΔΙΟΙΚΗΤΙΚΗΣ ΟΡΓΑΝΩΣΕΩΣ Τ.Σ.Α.Υ
'1182': Φ.Κ.Π. ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ
'1183': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΟΡΓΑΝΙΣΜΟΙ
'1184': ΧΟΛΕΡΑ
'1185': EΝΙΑΙΟΣ ΔΗΜΟΣΙΟΓΡΑΦΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ
'1186': ΑΤΕΛΕΙΕΣ ΔΗΜΟΣΙΩΝ ΥΠΗΡΕΣΙΩΝ
'1187': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΜΗΧΑΝΟΔΗΓΩΝ ΟΔΟΣΤΡΩΤΗΡΩΝ ΚΛΠ
'1188': ΝΟΣΟΚΟΜΟΙ
'1189': ΝΟΣΟΚΟΜΕΙΑ ΦΥΛΑΚΩΝ
'1190': ΑΠΟΚΑΤΑΣΤΑΣΗ ΚΤΗΝΟΤΡΟΦΩΝ
'1191': ΤΕΛΗ ΚΑΙ ΕΙΣΦΟΡΕΣ
'1192': ΑΚΑΤΑΣΧΕΤΑ
'1193': ΞΕΝΟΔΟΧΕΙΑΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ ΤΗΣ ΕΛΛΑΔΑΣ
'1194': ΔΗΜΟΤΟΛΟΓΙΑ
'1195': ΣΤΑΤΙΣΤΙΚΗ ΥΠΗΡΕΣΙΑ
'1196': ΚΡΑΤΙΚΟ ΕΡΓΑΣΤΗΡΙΟ ΕΛΕΓΧΟΥ ΦΑΡΜΑΚΩΝ
'1197': ΑΕΡΟΠΟΡΙΚΗ ΑΣΤΥΝΟΜΙΑ
'1198': ΕΚΤΑΚΤΕΣ ΕΙΣΦΟΡΕΣ
'1199': ΣΥΝΤΑΞΕΙΣ ΥΠΑΛΛΗΛΩΝ Τ.Τ.Τ
'1200': ΜΕΤΡΑ ΚΑΤΑ ΤΗΣ ΦΟΡΟΔΙΑΦΥΓΗΣ
'1201': ΕΔΑΦΙΚΗ ΕΠΕΚΤΑΣΗ ΝΟΜΟΘΕΣΙΑΣ
'1202': ΜΙΚΡΟΔΙΑΦΟΡΕΣ
'1203': ΤΑΤΖΙΚΙΣΤΑΝ – ΤΑΥΛΑΝΔΗ – ΤΟΥΡΚΙΑ Κ.ΛΠ
'1204': ΣΥΜΒΑΣΗ ΔΙΕΘΝΟΥΣ ΜΕΤΑΦΟΡΑΣ ΕΜΠΟΡΕΥΜΑΤΩΝ ΟΔΙΚΩΣ
'1205': ΚΩΔΙΚΑΣ ΙΔΙΩΤΙΚΟΥ ΝΑΥΤΙΚΟΥ ΔΙΚΑΙΟΥ
'1206': ΚΕΝΤΡΑ ΓΕΩΡΓΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ-Ο.Γ.Ε.Ε.Κ.Α
'1207': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΔΡΥΜΑΤΩΝ ΕΜΠΟΡΙΚΟΥ ΝΑΥΤΙΚΟΥ
'1208': ΓΡΑΦΕΙΟ ΔΙΑΡΚΗ ΚΩΔΙΚΑ ΝΟΜΟΘΕΣΙΑΣ
'1209': ΕΡΕΥΝΑ ΙΔΙΩΤΙΚΩΝ ΜΕΤΑΛΛΕΙΩΝ
'1210': ΔΙΕΥΘΥΝΣΗ ΔΗΜΟΣΙΩΝ ΕΡΓΩΝ ΑΕΡΟΠΟΡΙΑΣ
'1211': ΠΕΡΙ ΝΟΜΑΡΧΩΝ
'1212': ΣΥΝΤΑΞΕΙΣ ΘΥΜΑΤΩΝ ΑΠΟ ΕΣΩΤΕΡΙΚΕΣ ΔΙΑΜΑΧΕΣ
'1213': ΔΙΑΧΕΙΡΙΣΗ ΕΦΟΔΙΩΝ ΕΞΩΤΕΡΙΚΟΥ
'1214': ΟΡΓΑΝΩΣΗ ΥΠΗΡΕΣΙΩΝ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'1215': ΦΟΡΤΗΓΑ ΠΛΟΙΑ ΑΝΩ ΤΩΝ 4.500 ΤΟΝΝΩΝ
'1216': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ ΠΛΟΙΩΝ
'1217': ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ
'1218': ΔΙΑΦΟΡΕΣ ΒΙΟΜΗΧΑΝΙΕΣ
'1219': ΣΥΝΤΗΡΗΣΗ ΑΕΡΟΣΚΑΦΩΝ
'1220': ΟΛΥΜΠΙΑΚΗ ΑΕΡΟΠΟΡΙΑ
'1221': ΟΡΓΑΝΙΣΜΟΣ ΧΩΡΟΦΥΛΑΚΗΣ
'1222': ΠΕΡΙΘΑΛΨΗ ΦΥΜΑΤΙΚΩΝ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'1223': ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΗΣ ΟΙΚΟΝΟΜΙΚΗΣ ΑΝΑΠΤΥΞΗΣ
'1224': ΠΡΩΤΕΣ ΥΛΕΣ ΞΥΛΙΝΩΝ ΒΑΡΕΛΙΩΝ
'1225': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΑΘΗΝΩΝ (Τ.Α.Τ.Τ.Α.)
'1226': ΠΡΟΠΑΡΑΣΚΕΥΑΣΤΙΚΗ ΣΧΟΛΗ ΚΑΛΩΝ ΤΕΧΝΩΝ ΤΗΝΟΥ
'1227': ΟΙΚΟΝΟΜΙΚΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ ΕΞΩΤΕΡΙΚΟΥ
'1228': ΚΑΛΛΙΤΕΧΝΙΚΟΙ ΣΤΑΘΜΟΙ
'1229': ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗ ΒΙΑ ΤΩΝ
'1230': ΠΡΟΣΤΑΣΙΑ ΑΜΠΕΛΟΥΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ
'1231': ΔΙΑΦΟΡΑ ΑΔΙΚΗΜΑΤΑ
'1232': ΑΣΤΥΝΟΜΙΑ ΚΑΙ ΑΣΦΑΛΕΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ
'1233': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΒΑΣΙΛΙΚΗΣ ΑΕΡΟΠΟΡΙΑΣ
'1234': ΥΠΟΘΗΚΗ ΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ
'1235': ΕΥΘΥΝΗ ΑΠΟ Τ’ΑΥΤΟΚΙΝΗΤΑ
'1236': ΠΡΟΣΤΑΣΙΑ ΜΗΤΡΟΤΗΤΟΣ ΚΑΙ ΒΡΕΦΩΝ
'1237': ΜΕ ΤΗ ΦΙΛΑΝΔΙΑ
'1238': ΕΠΑΡΧΙΑΚΟΣ ΤΥΠΟΣ
'1239': ΕΠΙΘΕΩΡΗΣΗ ΤΕΛΩΝΕΙΩΝ
'1240': ΕΠΙΤΡΟΠΕΙΕΣ ΤΟΠΩΝΥΜΙΩΝ
'1241': ΜΕΤΑΝΑΣΤΕΥΣΗ ΚΑΙ ΑΠΟΔΗΜΙΑ
'1242': ΔΙΚΗΓΟΡΙΚΟΙ ΣΥΛΛΟΓΟΙ
'1243': ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΓΕΩΡΓΙΑΣ
'1244': ΤΜΗΜΑ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΠΑΤΡΩΝ
'1245': ΜΑΛΑΚΤΕΣ
'1246': ΕΛΑΙΑ
'1247': ΑΤΟΜΙΚΑ ΕΓΓΡΑΦΑ ΑΞΙΩΜΑΤΙΚΩΝ
'1248': ΑΓΡΟΤΙΚΗ ΤΡΑΠΕΖΑ ΤΗΣ ΕΛΛΑΔΟΣ
'1249': ΟΠΤΙΚΟΙ - ΚΑΤΑΣΤΗΜΑΤΑ ΟΠΤΙΚΩΝ ΕΙΔΩΝ
'1250': ΔΗΜΟΣΙΕΣ ΕΠΕΝΔΥΣΕΙΣ
'1251': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ
'1252': ΝΗΟΛΟΓΙΑ-ΥΠΟΘΗΚΟΛΟΓΙΑ-ΣΗΜΑΤΟΛΟΓΗΣΗ
'1253': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΑΣ ΔΙΑΧΕΙΡΙΣΕΩΣ ΕΙΔΩΝ ΜΟΝΟΠΩΛΙΟΥ
(Τ.Α.Π.-Ε.Δ.Ε.Μ.Ε.)
'1254': ΕΙΣΠΡΑΞΗ ΑΞΙΩΝ
'1255': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΤΡΟΦΙΜΩΝ-ΠΟΤΩΝ-ΝΕΡΩΝ
'1256': ΛΟΓΙΣΤΕΣ - ΦΟΡΟΤΕΧΝΙΚΟΙ
'1257': ΕΙΔΙΚΕΣ ΔΙΚΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΓΙΑ ΤΟ ΔΗΜΟΣΙΟ
'1258': ΣΧΟΛΕΣ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ
'1259': ΤΑΜΕΙΟΝ ΚΟΙΝΩΦΕΛΩΝ ΕΡΓΩΝ ΛΕΥΚΑΔΟΣ
'1260': ΕΙΔΙΚΗ ΑΓΩΓΗ, ΕΙΔΙΚΗ ΕΠΑΓΓΕΛΜΑΤΙΚΗ
'1261': ΥΠΗΡΕΣΙΑ ΚΡΑΤΙΚΩΝ ΠΡΟΜΗΘΕΙΩΝ
'1262': ΟΙΝΟΛΟΓΙΚΑ ΙΔΡΥΜΑΤΑ
'1263': ΣΥΝΘΗΚΕΣ ΕΚΔΟΣΕΩΣ
'1264': ΑΞΙΩΜΑΤΙΚΟΙ ΚΑΙ ΥΠΑΞΙΩΜΑΤΙΚΟΙ Λ.Σ
'1265': ΥΓΕΙΟΝΟΜΙΚΗ ΕΞΕΤΑΣΗ ΠΡΟΣΩΠΙΚΟΥ
'1266': ΞΕΝΑ ΣΧΟΛΕΙΑ ΗΜΕΔΑΠΗΣ
'1267': Ε.Σ.Υ.-ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'1268': ΤΑΜΕΙΑ ΕΦΑΡΜΟΓΗΣ ΣΧΕΔΙΩΝ ΠΟΛΕΩΝ
'1269': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΙΔΩΝ
'1270': ΣΥΝΘΗΚΗ ΠΕΡΙ ΔΙΑΣΤΗΜΑΤΟΣ
'1271': ΔΙΑΧΕΙΡΙΣΗ ΑΝΤΑΛΛΑΞΙΜΩΝ ΚΤΗΜΑΤΩΝ
'1272': ΠΡΟΣΩΠΙΚΟΝ ΔΙΟΙΚΗΣΕΩΣ
'1273': ΣΧΟΛΗ ΕΚΠΤΙΚΩΝ ΛΕΙΤΟΥΡΓΩΝ
'1274': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΞΕΝΟΔΟΧΟΥΠΑΛΛΗΛΩΝ (Τ.Α.Ξ.Υ.)
'1275': ΣΩΜΑΤΙΚΗ ΙΚΑΝΟΤΗΤΑ ΑΞΙΩΜΑΤΙΚΩΝ
'1276': ΒΕΒΑΙΩΣΗ ΕΣΟΔΩΝ ΔΗΜΟΣΙΟΥ ΑΠΟ ΜΕΤΑΛΛΕΙΑ ΚΑΙ ΛΑΤΟΜΕΙΑ
'1277': ΔΙΑΦΟΡΟΙ ΕΠΟΙΚΙΣΤΙΚΟΙ ΝΟΜΟΙ
'1278': ΕΠΙΚΟΥΡΙΚΟ ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΚΡΕΟΠΩΛΩΝ ΚΑΙ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΚΡΕΑΤΟΣ
(Ε.Τ.Α.Κ.Ε.Κ)
'1279': ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ
'1280': ΓΕΝΙΚΕΣ ΑΠΟΘΗΚΕΣ
'1281': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ
'1282': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ
'1283': ΤΟΜΕΑΣ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΤΕΑΜ)ΕΙΔΙΚΟΣ ΤΟΜΕΑΣ
ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ (ΙΚΑ-ΕΤΕΑΜ)
'1284': ΒΑΡΒΑΚΕΙΟ ΛΥΚΕΙΟ
'1285': ΚΩΔΙΚΑΣ ΔΙΚΩΝ ΤΟΥ ΔΗΜΟΣΙΟΥ
'1286': ΔΙΕΘΝΕΣ ΤΑΜΕΙΟΝ ΠΕΡΙΘΑΛΨΕΩΣ ΤΟΥ ΠΑΙΔΙΟΥ
'1287': ΣΙΔΗΡΟΔΡΟΜΟΙ ΕΛΛΗΝΙΚΟΥ ΚΡΑΤΟΥΣ
'1288': ΑΡΔΕΥΣΕΙΣ
'1289': ΤΑΜΕΙΟ ΑΡΧΑΙΟΛΟΓΙΚΩΝ ΠΟΡΩΝ ΚΑΙ ΑΠΑΛΛΟΤΡΙΩΣΕΩΝ
'1290': ΙΔΡΥΜΑ ΒΥΖΑΝΤΙΝΗΣ ΜΟΥΣΙΚΟΛΟΓΙΑΣ
'1291': ΚΥΒΕΡΝΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ ΕΛΕΓΧΟΥ ΤΙΜΩΝ
'1292': ΕΙΔΙΚΟ ΤΑΜΕΙΟ ΕΠΟΙΚΙΣΜΟΥ
'1293': ΚΤΗΜΑΤΟΛΟΓΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'1294': ΚΑΤΑΣΚΕΥΗ ΣΤΑΦΙΔΙΝΗΣ
'1295': ΔΙΕΘΝΗΣ ΥΓΕΙΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'1296': ΕΠΕΤΗΡΙΔΑ
'1297': ΠΑΓΚΟΣΜΙΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ
'1298': ΕΝΙΣΧΥΣΗ ΑΠΡΟΣΤΑΤΕΥΤΩΝ ΠΑΙΔΙΩΝ
'1299': ΔΙΑΦΟΡΟΙ ΕΠΙΣΙΤΙΣΤΙΚΟΙ ΝΟΜΟΙ
'1300': ΔΙΠΛΩΜΑΤΙΚΕΣ ΑΤΕΛΕΙΕΣ
'1301': ΜΕΤΑ ΤΟΥ ΒΕΛΓΙΟΥ
'1302': ΚΑΝΝΑΒΙΣ
'1303': ΕΚΤΕΛΕΣΗ
'1304': ΤΟΥΡΙΣΤΙΚΕΣ ΕΓΚΑΤΑΣΤΑΣΕΙΣ ΡΟΔΟΥ
'1305': ΠΟΙΝΙΚΟ ΜΗΤΡΩΟ
'1306': ΑΝΩΜΑΛΕΣ ΔΙΚΑΙΟΠΡΑΞΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ
'1307': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ
'1308': ΣΥΝΤΟΝΙΣΜΟΣ ΠΡΟΓΡΑΜΜΑΤΩΝ ΚΑΙ ΕΡΓΑΣΙΩΝ ΟΔΩΝ ΚΑΙ ΕΡΓΩΝ ΚΟΙΝΗΣ ΩΦΕΛΕΙΑΣ
'1309': ΠΡΟΣΩΠΙΚΟ ΞΕΝΟΔΟΧΕΙΩΝ
'1310': ΙΝΣΤΙΤΟΥΤΟ ΦΥΣΙΚΗΣ ΤΟΥ ΣΤΕΡΕΟΥ ΦΛΟΙΟΥ ΤΗΣ ΓΗΣ
'1311': ΕΠΙΚΙΝΔΥΝΕΣ ΟΙΚΟΔΟΜΕΣ
'1312': ΑΡΧΕΙΑ ΔΙΚΑΣΤΗΡΙΩΝ
'1313': ΣΚΟΠΟΒΟΛΗ
'1314': ΑΠΟΝΟΜΗ ΣΥΝΤΑΞΕΩΝ ΤΑΜΕΙΟΥ ΝΟΜΙΚΩΝ
'1315': ΣΗΡΟΤΡΟΦΙΑ
'1316': ΕΣΩΤΕΡΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'1317': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΚΤΗΝΟΤΡΟΦΙΑΣ
'1318': ΧΑΡΤΗΣ
'1319': ΥΠΗΡΕΣΙΑ ΕΓΚΛΗΜΑΤΟΛΟΓΙΚΩΝ ΑΝΑΖΗΤΗΣΕΩΝ
'1320': ΥΓΕΙΟΝΟΜΙΚΗ ΠΕΡΙΘΑΛΨΗ ΒΟΥΛΕΥΤΩΝ
'1321': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΟΥ 1940
'1322': ΧΗΜΕΙΟ ΣΤΡΑΤΟΥ
'1323': ΕΠΑΡΧΙΑΚΕΣ ΓΕΝΙΚΕΣ ΣΥΝΕΛΕΥΣΕΙΣ
'1324': ΛΟΓΑΡΙΑΣΜΟΣ ΑΡΩΓΗΣ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'1325': ΚΑΤ’ ΙΔΙΑΝ ΝΑΟΙ
'1326': ΠΛΗΡΩΜΗ ΜΕ ΕΠΙΤΑΓΕΣ
'1327': ΕΘΝΙΚΕΣ ΣΥΛΛΟΓΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1328': ΣΩΜΑ ΣΤΡΑΤΟΛΟΓΙΑΣ
'1329': ΟΔΟΝΤΙΑΤΡΟΙ
'1330': ΤΑΜΕΙΟ ΕΘΝΙΚΟΥ ΣΤΟΛΟΥ
'1331': ΣΥΜΠΛΗΡΩΜΑΤΙΚΕΣ ΠΑΡΟΧΕΣ ΜΗΤΡΟΤΗΤΑΣ
'1332': ΜΕΤΑΤΡΕΨΙΜΟΤΗΤΑ ΚΑΤΑΘΕΣΕΩΝ
'1333': ΠΤΗΝΟΤΡΟΦΙΑ
'1334': ΠΤΥΧΙΟΥΧΟΙ ΑΛΛΟΔΑΠΩΝ ΠΑΝΕΠΙΣΤΗΜΙΩΝ - ΔΙΑΠΑΝΕΠΙΣΤΗΜΙΑΚΟ ΚΕΝΤΡΟ ΑΝΑΓΝΩΡΙΣΕΩΣ
'1335': ΦΟΡΤΗΓΑ ΑΥΤΟΚΙΝΗΤΑ
'1336': ΥΠΗΡΕΣΙΑ ΜΗΧΑΝΙΚΗΣ ΚΑΛΛΙΕΡΓΕΙΑΣ
'1337': ΕΛΕΓΧΟΣ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ
'1338': ΔΗΜΟΣΙΟΓΡΑΦΙΚΕΣ ΟΡΓΑΝΩΣΕΙΣ
'1339': ΝΑΥΤΙΛΙΑΚΕΣ ΤΡΑΠΕΖΕΣ
'1340': ΛΕΙΤΟΥΡΓΙΑ ΥΔΡΟΘΕΡΑΠΕΥΤΗΡΙΩΝ
'1341': ΣΥΜΒΟΥΛΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ
'1342': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ ΚΑΠΝΟΥ
'1343': ΤΕΛΟΣ ΑΔΕΙΩΝ ΟΙΚΟΔΟΜΩΝ
'1344': ΕΘΝΙΚΟΤΗΤΑ ΠΛΟΙΩΝ
'1345': ΠΟΛΙΤΙΚΑ ΚΟΜΜΑΤΑ
'1346': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'1347': ΝΗΟΓΝΩΜΟΝΕΣ
'1348': ΔΙΑΦΟΡΟΙ ΠΟΙΝΙΚΟΙ ΝΟΜΟΙ
'1349': ΠΡΟΣΩΡΙΝΗ ΑΠΟΛΥΣΗ
'1350': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΣΤΡΑΤΟΥ ΞΗΡΑΣ
'1351': ΥΠΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ
'1352': ΦΟΡΟΛΟΓΙΑ ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΩΝ ΣΥΜΒΑΣΕΩΝ
'1353': ΠΤΥΧΙΑ ΙΠΤΑΜΕΝΟΥ ΠΡΟΣΩΠΙΚΟΥ
'1354': ΚΡΕΑΤΑ ΣΕ ΠΑΚΕΤΑ
'1355': ΕΛΕΓΧΟΣ ΟΠΛΟΦΟΡΙΑΣ
'1356': ΑΝΑΣΤΟΛΕΣ ΔΗΜΟΣΙΟΥ ΧΡΕΟΥΣ
'1357': ΗΛΕΚΤΡΙΚΟΙ ΣΙΔΗΡΟΔΡΟΜΟΙ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ (Η.Σ.Α.Π)
'1358': ΔΙΑΘΕΣΗ ΛΥΜΑΤΩΝ ΚΑΙ ΑΠΟΒΛΗΤΩΝ
'1359': ΕΠΙΘΕΩΡΗΣΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'1360': ΤΕΛΗ ΑΔΕΙΩΝ ΕΞΑΓΩΓΗΣ
'1361': ΠΡΟΙΟΝΤΑ ΓΑΛΑΚΤΟΣ
'1362': ΓΕΩΡΓΙΚΑ ΕΠΙΜΕΛΗΤΗΡΙΑ
'1363': ΙΕΡΑΡΧΙΚΟΣ ΄ΕΛΕΓΧΟΣ
'1364': ΣΤΡΑΤΙΩΤΙΚΕΣ ΦΥΛΑΚΕΣ
'1365': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΑΠΝΕΜΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'1366': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΠΠΟΔΡΟΜΙΩΝ
(Τ.Α.Π.Ε.Α.Π.Ι.)
'1367': ΑΠΟΧΩΡΗΤΗΡΙΑ
'1368': ΦΟΡΟΣ ΕΙΣΟΔΗΜΑΤΟΣ ΦΥΣΙΚΩΝ ΚΑΙ ΝΟΜΙΚΩΝ ΠΡΟΣΩΠΩΝ
'1369': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΑΡΟΧΩΝ
'1370': ΑΤΤΙΚΟ ΜΕΤΡΟ
'1371': ΒΟΥΣΤΑΣΙΑ
'1372': ΑΠΟΣΤΡΑΤΕΙΕΣ - ΕΠΑΝΑΦΟΡΕΣ
'1373': ΤΡΑΠΕΖΙΤΙΚΑ ΔΑΝΕΙΑ ΣΕ ΧΡΥΣΟ ΚΛΠ
'1374': ΔΙΚΑΙΟΣΤΑΣΙΟ ΠΟΛΕΜΩΝ
'1375': ΕΘΝΙΚΟ ΑΣΤΕΡΟΣΚΟΠΕΙΟ
'1376': ΙΔΙΩΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΑΡΟΧΗΣ ΥΠΗΡΕΣΙΩΝ ΑΣΦΑΛΕΙΑΣ
'1377': ΔΑΝΕΙΑ ΕΞΩΤΕΡΙΚΑ
'1378': ΠΝΕΥΜΑΤΙΚΟ ΚΕΝΤΡΟ ΑΘΗΝΩΝ
'1379': ΑΠΟΣΒΕΣΕΙΣ
'1380': ΔΙΑΦΟΡΟΙ ΟΙΝΙΚΟΙ ΚΑΙ ΣΤΑΦΙΔΙΚΟΙ ΝΟΜΟΙ
'1381': ΑΚΑΔΗΜΙΑ ΣΩΜΑΤΙΚΗΣ ΑΓΩΓΗΣ
'1382': ΑΜΜΟΛΗΨΙΑ
'1383': ΠΡΟΣΩΠΙΚΟ ΠΛΟΗΓΙΚΗΣ ΥΠΗΡΕΣΙΑΣ
'1384': ΗΘΙΚΕΣ ΑΜΟΙΒΕΣ ΑΕΡΟΠΟΡΙΑΣ
'1385': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΟΙΝΟΠΝΕΥΜΑΤΟΣ
'1386': ΛΙΜΕΝΙΚΑ ΤΑΜΕΙΑ – ΛΙΜΕΝΙΚΑ ΕΡΓΑ
'1387': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΕΘΝΙΚΟΥ ΟΡΓΑΝΙΣΜΟΥ ΚΑΠΝΟΥ (Τ.Ε.Α.ΥΕ.Ο.Κ)
'1388': ΕΛΕΓΧΟΣ ΤΗΣ ΠΙΣΤΕΩΣ
'1389': ΣΤΡΑΤΙΩΤΙΚΗ ΣΧΟΛΗ ΑΞΙΩΜΑΤΙΚΩΝ ΣΩΜΑΤΩΝ
'1390': ΒΟΗΘΗΤΙΚΑ ΠΡΟΣΩΠΑ ΤΗΣ ΔΙΚΗΣ
'1391': ΟΡΓΑΝΙΣΜΟΣ ΣΧΟΛΙΚΩΝ ΚΤΙΡΙΩΝ
'1392': ΒΙΟΜΗΧΑΝΙΕΣ ΔΩΔΕΚΑΝΗΣΟΥ
'1393': ΥΓΙΕΙΝΗ ΚΑΙ ΑΣΦΑΛΕΙΑ ΧΩΡΩΝ ΕΡΓΑΣΙΑΣ ΚΑΙ ΕΡΓΑΖΟΜΕΝΩΝ
'1394': ΜΕΤΑΤΡΟΠΗ ΤΗΣ ΠΟΙΝΗΣ
'1395': ΑΥΤΟΝΟΜΟΣ ΟΙΚΟΔΟΜΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΑΞΙΩΜΑΤΙΚΩΝ
'1396': ΟΔΙΚΕΣ ΜΕΤΑΦΟΡΕΣ-ΜΕΤΑΦΟΡΕΙΣ
'1397': ΑΡΜΑ ΘΕΣΠΙΔΟΣ
'1398': ΔΗΜΟΤΙΚΑ & ΚΟΙΝΟΤΙΚΑ
'1399': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ
'1400': ΣΧΟΛΗ ΑΝΘΡΩΠΙΣΤΙΚΩΝ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'1401': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΦΟΙΤΗΤΑΙ
'1402': ΓΕΝΙΚΑ
'1403': ΚΑΤΑΠΟΛΕΜΗΣΗ ΕΠΙΖΩΟΤΙΩΝ
'1404': ΟΡΓΑΝΙΣΜΟΣ ΔΙΟΙΚΗΣΕΩΣ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΚΑΙ ΜΟΝΑΣΤΗΡΙΑΚΗΣ ΠΕΡΙΟΥΣΙΑΣ
'1405': ΑΠΑΓΟΡΕΥΣΗ ΧΡΗΣΗΣ ΕΠΙΒΛΑΒΩΝ ΟΥΣΙΩΝ
'1406': ΨΥΧΟΛΟΓΟΙ
'1407': ΠΥΡΑΣΦΑΛΕΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΚΑΙ ΑΠΟΘΗΚΩΝ
'1408': ΑΠΟΚΑΤΑΣΤΑΣΙΣ ΑΠΟΡΩΝ ΚΟΡΑΣΙΔΩΝ
'1409': ΜΕ ΤΗ ΒΕΝΕΖΟΥΕΛΑ
'1410': ΔΙΚΑΙΟ ΤΩΝ ΣΥΝΘΗΚΩΝ
'1411': ΚΤΗΝΙΑΤΡΙΚΑ ΜΙΚΡΟΒΙΟΛΟΓΙΚΑ ΕΡΓΑΣΤΗΡΙΑ
'1412': ΕΡΓΑΣΤΗΡΙΑ
'1413': ΚΑΝΟΝΙΣΜΟΙ TELEX ΚΑΙ TELEFAX
'1414': ΟΠΛΑ ΚΑΙ ΣΩΜΑΤΑ ΣΤΡΑΤΟΥ ΞΗΡΑΣ
'1415': ΕΚΠΑΙΔΕΥΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'1416': ΤΙΜΟΛΟΓΙΑ ΠΑΡΟΧΩΝ
'1417': ΜΟΥΣΟΥΛΜΑΝΙΚΕΣ ΚΟΙΝΟΤΗΤΕΣ
'1418': ΣΤΡΑΤΙΩΤΙΚΑ ΕΡΓΑ ΕΝ ΓΕΝΕΙ
'1419': ΣΤΡΑΤΙΩΤΙΚΑ ΝΟΣΟΚΟΜΕΙΑ
'1420': ΔΙΟΙΚΗΣΗ ΔΗΜΟΣΙΩΝ ΚΤΗΜΑΤΩΝ –
'1421': ΕΙΔΙΚΕΣ ΤΙΜΕΣ ΚΑΥΣΙΜΩΝ ΚΑΙ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
'1422': ΕΓΓΡΑΦΗ ΣΠΟΥΔΑΣΤΩΝ
'1423': ΔΗΜΟΤΙΚΑ-ΚΟΙΝΟΤΙΚΑ ΔΑΣΗ ΚΑΙ ΚΗΠΟΙ
'1424': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΠΟΛΕΟΔΟΜΙΑΣ ΚΑΙ ΣΤΕΓΑΣΕΩΣ
'1425': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α
'1426': ΕΞΕΤΑΣΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΒΟΥΛΗΣ
'1427': ΜΕΤΡΑ ΚΑΤΑ ΤΩΝ ΠΥΡΚΑΙΩΝ ΔΑΣΩΝ
'1428': ΥΠΟΥΡΓΕΙΟ ΕΘΝΙΚΗΣ ΟΙΚΟΝΟΜΙΑΣ
'1429': ΣΥΓΚΕΝΤΡΩΣΗ ΠΕΡΙΟΥΣΙΑΣ ΤΟΥ ΔΗΜΟΣΙΟΥ
'1430': ΚΑΤΑΣΚΕΥΗ ΚΑΙ ΣΥΝΤΗΡΗΣΗ ΟΔΩΝ
'1431': ΤΕΛΩΝΕΙΑΚΑ ΚΤΙΡΙΑ
'1432': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΕΚΤΕΛΩΝΙΣΤΩΝ (Τ.Σ.Ε.)
'1433': ΚΑΘΗΓΗΤΙΚΕΣ ΕΔΡΕΣ
'1434': ΝΑΥΤΙΚΗ ΕΡΓΑΣΙΑ ΝΕΩΝ
'1435': ΕΚΤΕΛΕΣΗ ΘΑΝΑΤΙΚΗΣ ΠΟΙΝΗΣ
'1436': ΕΠΙΘΕΩΡΗΣΗ ΠΛΟΙΩΝ
'1437': ΔΙΠΛΩΜΑΤΑ ΚΑΙ ΑΔΕΙΕΣ ΝΑΥΤΙΚΗΣ ΙΚΑΝΟΤΗΤΑΣ
'1438': ΙΣΤΟΡΙΚΟ ΚΑΙ ΕΘΝΟΛΟΓΙΚΟ ΜΟΥΣΕΙΟ
'1439': ΠΡΟΣΤΑΣΙΑ ΕΡΓΑΖΟΜΕΝΗΣ ΝΕΑΣ
'1440': ΥΠΗΡΕΣΙΑ ΕΠΙΜΕΛΗΤΩΝ ΑΝΗΛΙΚΩΝ
'1441': ΑΣΤΙΚΗ ΕΥΘΥΝΗ ΑΠΟ ΠΥΡΗΝΙΚΗ ΕΝΕΡΓΕΙΑ
'1442': ΚΩΔΙΚΑΣ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ
'1443': ΕΠΙΘΕΩΡΗΣΗ Υ.Ε.Ν
'1444': ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ ΣΥΝΔΙΚΑΛΙΣΤΙΚΩΝ ΣΤΕΛΕΧΩΝ
'1445': ΥΓΕΙΟΝΟΜΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'1446': ΔΙΔΑΣΚΑΛΕΙΟ ΜΕΣΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'1447': ΥΠΟΒΡΥΧΙΑ
'1448': ΥΠΗΡΕΣΙΑ ΑΠΩΛΕΙΩΝ, ΝΕΚΡΟΤΑΦΕΙΩΝ ΚΛΠ
'1449': ΑΓΡΟΤ. ΑΠΟΚΑΤΑΣΤΑΣΗ ΣΤΑ ΔΩΔΕΚΑΝΗΣΑ
'1450': ΕΙΔΙΚΕΣ ΑΠΑΛΛΟΤΡΙΩΣΕΙΣ
'1451': ΣΤΕΓΑΣΗ ΤΑΧΥΔΡΟΜΙΚΩΝ ΥΠΗΡΕΣΙΩΝ
'1452': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΝΑΡΚΩΤΙΚΩΝ
'1453': ΜΕΤΑΜΟΣΧΕΥΣΗ ΒΙΟΛΟΓΙΚΩΝ ΟΥΣΙΩΝ
'1454': ΒΡΑΒΕΙΑ ΚΑΙ ΧΟΡΗΓΙΕΣ
'1455': ΕΥΡΩΠΑΙΚΗ ΜΟΡΦΩΤΙΚΗ ΣΥΜΒΑΣΗ
'1456': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝ. ΕΡΥΘΡΟΥ ΣΤΑΥΡΟΥ
(Τ.Ε.Α.Π.Ε.Ε.Σ.)
'1457': ΑΤΕΛΕΙΕΣ ΕΙΔΩΝ ΒΟΗΘΕΙΑΣ
'1458': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΟΧΥΡΩΣΗΣ
'1459': ΡΟΥΑΝΤΑ – ΡΟΥΜΑΝΙΑ Κ.ΛΠ
'1460': ΜΟΝΙΜΕΣ ΑΝΤΙΠΡΟΣΩΠΕΙΕΣ
'1461': ΠΡΟΣΤΑΣΙΑ ΕΦΕΔΡΩΝ ΙΠΤΑΜΕΝΩΝ
'1462': ΤΡΑΠΕΖΕΣ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ
'1463': ΙΑΤΡΙΚΟΝ ΠΡΟΣΩΠΙΚΟΝ ΔΗΜΟΣΙΟΥ ΚΑΙ Ν.Π.Δ.Δ
'1464': ΔΙΑΦΟΡΑ ΜΟΝΑΣΤΗΡΙΑ
'1465': ΕΤΑΙΡΕΙΕΣ ΕΠΕΝΔΥΣΕΩΝ - ΧΑΡΤΟΦΥΛΑΚΙΟΥ ΚΑΙ ΑΜΟΙΒΑΙΩΝ ΚΕΦΑΛΑΙΩΝ
'1466': ΑΝΑΓΝΩΡΙΣΗ ΤΗΣ ΕΛΛΗΝΙΚΗΣ ΠΟΛΙΤΕΙΑΣ
'1467': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ
'1468': ΛΙΜΕΝΑΡΧΕΙΑ
'1469': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΘΕΣΣΑΛΙΑΣ
'1470': ΣΤΡΑΤΕΥΣΗ ΓΥΝΑΙΚΩΝ
'1471': ΣΤΡΑΤΙΩΤΙΚΗ ΥΠΗΡΕΣΙΑ ΚΑΤΑΣΚΕΥΗΣ ΕΡΓΩΝ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ
'1472': ΠΡΟΣΤΑΣΙΑ ΤΗΣ ΤΙΜΗΣ ΤΟΥ ΠΟΛΙΤΙΚΟΥ ΚΟΣΜΟΥ
'1473': ΕΠΙΜΟΡΦΩΣΗ ΛΕΙΤΟΥΡΓΩΝ Μ.Ε
'1474': ΕΝΙΣΧΥΣΗ ΕΞΑΓΩΓΗΣ
'1475': ΗΛΕΚΤΡΟΦΩΤΙΣΜΟΣ ΔΙΑΦΟΡΩΝ ΠΟΛΕΩΝ
'1476': ΜΕ ΤΙΣ ΚΑΤΩ ΧΩΡΕΣ
'1477': ΝΑΥΠΗΓΟΥΜΕΝΑ ΠΛΟΙΑ-ΝΑΥΠΗΓΟΕΠΙΣΚΕΥΑΣΤΙΚΕΣ
'1478': ΕΛΕΓΧΟΣ ΠΩΛΗΣΕΩΝ ΕΠΙ ΠΙΣΤΩΣΕΙ
'1479': ΕΛΕΓΧΟΣ ΒΙΟΜΗΧΑΝΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ
'1480': ΔΙΕΘΝΗΣ ΟΙΚΟΝΟΜΙΚΗ ΕΠΙΤΡΟΠΗ
'1481': ΓΡΑΦΕΙΑ ΕΥΡΕΣΗΣ ΕΡΓΑΣΙΑΣ - ΣΥΜΒΟΥΛΟΙ ΕΡΓΑΣΙΑΣ
'1482': ΜΟΝΟΠΩΛΙΟ ΝΑΡΚΩΤΙΚΩΝ
'1483': ΑΠΑΛΛΑΓΕΣ ΦΟΡΟΛΟΓΙΑΣ ΚΛΗΡΟΝΟΜΙΩΝ
'1484': ΠΑΓΚΟΣΜΙΑ ΟΡΓΑΝΩΣΗ ΥΓΕΙΑΣ
'1485': ΕΘΝΙΚΟ ΙΔΡΥΜΑ ΕΡΕΥΝΩΝ
'1486': ΝΟΜΟΘΕΣΙΑ ΠΕΡΙ ΣΥΛΛΟΓΙΚΗΣ ΣΥΜΒΑΣΕΩΣ
'1487': ΕΘΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΦΑΡΜΑΚΩΝ
'1488': ΔΙΑΦΟΡΑ ΓΥΜΝΑΣΙΑ & ΛΥΚΕΙΑ
'1489': ΞΕΝΕΣ ΣΧΟΛΕΣ ΓΕΩΠΟΝΙΑΣ ΚΑΙ ΔΑΣΟΛΟΓΙΑΣ
'1490': ΠΡΟΣΤΑΣΙΑ ΑΝΕΡΓΩΝ
'1491': ΦΙΛΑΝΘΡΩΠΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ ΚΕΦΑΛΛΗΝΙΑΣ
'1492': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ Τ.Ε.Β.Ε
'1493': ΩΔΕΙΑ ΚΛΠ. ΜΟΥΣΙΚΑ ΙΔΡΥΜΑΤΑ
'1494': ΠΡΟΣΚΥΝΗΜΑΤΙΚΑ ΙΔΡΥΜΑΤΑ
'1495': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΝΩΝ. ΥΔΡΟΗΛΕΚΤΡ. ΕΤ. ΓΛΑΥΚΟΣ
'1496': ΠΡΕΣΒΕΙΕΣ ΚΑΙ ΠΡΟΞΕΝΕΙΑ
'1497': ΥΠΟΥΡΓΕΙΑ ΤΥΠΟΥ ΚΑΙ ΤΟΥΡΙΣΜΟΥ
'1498': ΖΩΝΕΣ ΕΝΕΡΓΟΥ ΠΟΛΕΟΔΟΜΙΑΣ
'1499': ΕΚΚΛΗΣΙΑ ΙΟΝΙΩΝ ΝΗΣΩΝ
'1500': ΕΠΙΤΡΟΠΑΙ ΑΣΦΑΛΕΙΑΣ
'1501': ΥΠΟΥΡΓΟΙ
'1502': ΠΟΙΝΙΚΗ ΔΙΑΤΙΜΗΣΗ
'1503': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΩΝ ΚΕΡΑΜΟΠΟΙΩΝ
'1504': ΠΡΩΤΕΣ ΥΛΕΣ ΠΑΙΓΝΙΟΧΑΡΤΩΝ
'1505': ΚΡΥΠΤΟΓΡΑΦΙΚΗ ΥΠΗΡΕΣΙΑ
'1506': ΔΙΕΘΝΗΣ ΕΠΙΤΡΟΠΗ ΠΡΟΣΩΠΙΚΗΣ ΚΑΤΑΣΤΑΣΕΩΣ
'1507': ΕΛΕΓΧΟΣ ΗΛΕΚΤΡΙΚΩΝ ΕΓΚΑΤΑΣΤΑΣΕΩΝ
'1508': ΔΙΑΧΕΙΡΙΣΗ ΙΔΡΥΜΑΤΩΝ ΚΑΙ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ
'1509': ΤΕΛΩΝΕΙΑΚΗ ΣΤΑΤΙΣΤΙΚΗ
'1510': ΙΔΙΩΤΙΚΕΣ ΝΑΥΤΙΚΕΣ ΣΧΟΛΕΣ
'1511': ΑΕΡΟΠΟΡΙΚΑ ΑΤΥΧΗΜΑΤΑ
'1512': ΑΝΩΤΕΡΟ ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ
'1513': ΔΙΑΦΟΡΟΙ ΔΙΟΙΚΗΤΙΚΟΙ ΕΡΓΑΤΙΚΟΙ ΝΟΜΟΙ
'1514': ΣΥΜΒΟΥΛΙΟ ΓΕΩΓΡΑΦΙΚΩΝ ΥΠΗΡΕΣΙΩΝ
'1515': ΕΚΚΛΗΣΙΑΣΤΙΚΕΣ ΒΙΒΛΙΟΘΗΚΕΣ
'1516': ΤΜΗΜΑ ΕΠΙΣΤΗΜΗΣ ΦΥΣΙΚΗΣ ΑΓΩΓΗΣ ΚΑΙ ΑΘΛΗΤΙΣΜΟΥ
'1517': ΠΕΡΙΟΡΙΣΜΟΣ ΣΥΝΘΕΣΕΩΣ ΥΠΗΡΕΣΙΩΝ
'1518': ΤΑΜΕΙΑ ΕΠΑΡΧΙΑΚΗΣ ΟΔΟΠΟΙΙΑΣ
'1519': ΤΙΜΟΛΟΓΙΑ Ο.Τ.Ε - ΚΟΣΤΟΛΟΓΗΣΗ ΥΠΗΡΕΣΙΩΝ Ο.Τ.Ε
'1520': ΕΘΝΙΚΗ ΒΙΒΛΙΟΘΗΚΗ
'1521': ΔΗΜΟΣΙΕΣ ΣΧΟΛΕΣ ΥΠΟΜΗΧΑΝΙΚΩΝ
'1522': ΑΝΑΦΟΡΕΣ ΠΡΟΣ ΤΙΣ ΑΡΧΕΣ
'1523': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΛΕΩΦΟΡΕΙΑΚΩΝ ΓΡΑΜΜΩΝ
'1524': ΔΙΑΦΟΡΑ ΕΠΙΔΟΜΑΤΑ
'1525': ΙΔΙΩΤΙΚΗ ΑΕΡΟΠΟΡΙΑ – ΑΕΡΟΛΕΣΧΕΣ
'1526': ΤΜΗΜΑ ΔΙΟΙΚΗΤΙΚΗΣ ΤΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'1527': ΔΙΕΘΝΕΙΣ ΑΕΡΟΠΟΡΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1528': ΠΡΟΙΚΟΔΟΤΗΣΕΙΣ ΕΞ ΕΘΝΙΚΩΝ ΓΑΙΩΝ
'1529': ΔΙΟΡΘΩΣΗ ΑΣΥΜΦΩΝΙΩΝ
'1530': ΕΠΙΤΡΟΠΗ ΔΙΟΙΚΗΣΕΩΣ
'1531': ΜΕΤΑ ΤΗΣ ΓΕΡΜΑΝΙΑΣ
'1532': ΟΙΚΟΔΟΜΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ
'1533': ΚΑΤΑΣΤΑΤΙΚΟΙ ΝΟΜΟΙ
'1534': ΑΞΙΩΜΑΤΙΚΟΙ ΓΡΑΦΕΙΟΥ
'1535': ΚΑΝΟΝΙΣΜΟΣ ΕΝΑΕΡΙΟΥ ΚΥΚΛΟΦΟΡΙΑΣ
'1536': ΔΙΑΧΕΙΡΙΣΗ ΚΑΥΣΙΜΩΝ
'1537': ΟΜΟΛΟΓΙΑΚΑ ΔΑΝΕΙΑ
'1538': ΕΡΓΑ
'1539': ΣΧΟΛΗ ΝΑΥΤΙΚΩΝ ΔΟΚΙΜΩΝ
'1540': ΠΩΛΗΣΗ ΦΑΡΜΑΚΩΝ ΑΠΟ ΙΑΤΡΟΥΣ
'1541': ΣΗΜΑΤΑ ΕΘΝΙΚΟΤΗΤΑΣ ΚΑΙ ΝΗΟΛΟΓΗΣΕΩΣ
'1542': ΛΕΙΤΟΥΡΓΟΙ ΣΤΟΙΧΕΙΩΔΟΥΣ
'1543': ΕΦΕΤΕΙΑ ΚΑΙ ΠΡΩΤΟΔΙΚΕΙΑ
'1544': ΥΠΟΥΡΓΕΙΟ ΠΡΟΕΔΡΙΑΣ ΚΥΒΕΡΝΗΣΕΩΣ
'1545': ΜΟΡΦΩΤΙΚΟΣ – ΚΙΝΗΜΑΤΟΓΡΑΦΟΣ
'1546': ΚΑΤΑΜΕΤΡΗΣΗ ΧΩΡΗΤΙΚΟΤΗΤΑΣ
'1547': ΦΩΤΑΕΡΙΟ
'1548': ΠΑΘΗΤΙΚΗ ΑΕΡΑΜΥΝΑ
'1549': ΠΡΟΣΩΠΙΚΟ ΝΟΣΗΛΕΥΤΙΚΩΝ ΙΔΡΥΜΑΤΩΝ
'1550': ΜΕ ΤΗΝ ΚΥΠΡΟ
'1551': ΚΟΛΛΗΓΟΙ (ΕΠΙΜΟΡΤΟΙ ΚΑΛΛΙΕΡΓΗΤΕΣ)
'1552': ΤΑΜΕΙΟ ΑΡΩΓΗΣ Λ.Σ
'1553': ΙΧΘΥΟΣΚΑΛΕΣ
'1554': ΣΧΗΜΑ ΚΑΙ ΤΙΜΗ ΠΩΛΗΣΗΣ ΕΦΗΜΕΡΙΔΩΝ
'1555': ΥΙΟΘΕΣΙΑ
'1556': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ ΑΡΜΟΔΙΟΤΗΤΑΣ ΕΚΚΛΗΣΙΑΣ
'1557': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ
'1558': ΔΙΑΦΟΡΕΣ ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ
'1559': ΕΓΓΕΙΟΣ ΦΟΡΟΛΟΓΙΑ
'1560': ΠΑΙΔΑΓΩΓΙΚΕΣ ΑΚΑΔΗΜΙΕΣ
'1561': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΕΡΓΑΤΟΥΠΑΛΛΗΛΩΝ ΜΕΤΑΛΛΟΥ (ΤΑ.Π.Ε.Μ.)
'1562': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΑΕΡΟΣΚΑΦΩΝ
'1563': ΕΝΩΣΗ ΑΠΟΣΤΡΑΤΩΝ ΑΞΙΩΜΑΤΙΚΩΝ Β.Α
'1564': ΑΣΦΑΛΙΣΗ ΕΡΓΑΤΩΝ ΓΕΩΡΓΙΑΣ
'1565': ΟΡΓΑΝΩΣΗ ΚΑΛΛΙΤΕΧΝΙΚΩΝ ΕΚΔΗΛΩΣΕΩΝ-ΦΕΣΤΙΒΑΛ
'1566': ΠΕΡΙΟΥΣΙΑΚΕΣ ΣΥΝΕΠΕΙΕΣ ΤΗΣ ΠΟΙΝΗΣ
'1567': ΤΗΛΕΓΡΑΦΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ
'1568': ΕΠΙΘΕΩΡΗΣΗ ΔΗΜΟΣΙΩΝ ΥΠΟΛΟΓΩΝ
'1569': ΜΕ ΤΟΝ ΚΑΝΑΔΑ
'1570': ΑΛΛΗΛΟΓΡΑΦΙΑ Υ.Ε.Ν
'1571': ΤΕΧΝΙΚΟ ΠΡΟΣΩΠΙΚΟ ΑΕΡΟΠΟΡΙΑΣ
'1572': ΚΛΑΔΟΣ ΑΥΤΟΤΕΛΩΣ ΑΠΑΣΧΟΛΟΥΜΕΝΩΝ, ΕΛΕΥΘΕΡΩΝ ΚΑΙ ΑΝΕΞΑΡΤΗΤΩΝ
'1573': ΣΧΟΛΕΙΑ ΒΑΡΥΚΟΩΝ Η ΚΩΦΩΝ
'1574': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΚΑΤΩΤΕΡΩΝ ΠΛΗΡΩΜΑΤΩΝ Ε.Ν
'1575': ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ - ΣΚΑΦΗ ΑΝΑΨΥΧΗΣ - ΤΟΥΡΙΣΤΙΚΟΙ ΛΙΜΕΝΕΣ (ΜΑΡΙΝΕΣ)
'1576': ΕΠΙΔΟΜΑΤΑ ΕΟΡΤΩΝ ΧΡΙΣΤΟΥΓΕΝΝΩΝ ΚΑΙ ΠΑΣΧΑ
'1577': ΕΠΙΜΕΛΗΤΗΡΙΑ - ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ
'1578': ΥΠΟΥΡΓΕΙΟ ΕΡΕΥΝΑΣ ΚΑΙ ΤΕΧΝΟΛΟΓΙΑΣ
'1579': ΣΤΕΓΑΣΗ ΑΞΙΩΜΑΤΙΚΩΝ
'1580': ΠΑΡΑΡΤΗΜΑΤΑ ΓΕΝΙΚΟΥ ΧΗΜΕΙΟΥ
'1581': ΚΑΘΑΡΙΣΤΡΙΕΣ
'1582': ΚΑΝΟΝΙΣΜΟΣ ΝΑΥΤΟΔΙΚΕΙΟΥ
'1583': ΑΜΟΙΒΕΣ ΜΗΧΑΝΙΚΩΝ
'1584': ΕΠΙΜΟΡΦΩΣΗ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ
'1585': ΚΑΝΟΝΙΣΜΟΙ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ
'1586': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΙΑΣ ΕΛΛ. ΚΑΛΥΚΟΠΟΙΕΙΟΥ-ΠΥΡΙΤΙΔΟΠΟΙΕΙΟΥ
'1587': ΠΡΟΣΩΠΙΚΟ ΤΡΑΠΕΖΩΝ
'1588': ΛΥΣΣΙΑΤΡΕΙΑ
'1589': ΣΥΝΟΡΙΑΚΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1590': ΠΟΛΕΜΙΚΟ ΜΟΥΣΕΙΟ
'1591': ΚΑΘΗΚΟΝΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΥΠΑΛΛΗΛΩΝ
'1592': ΕΠΕΚΤΑΣΗ ΤΗΣ ΑΣΦΑΛΙΣΕΩΣ
'1593': ΦΟΡΟΛΟΓΙΚΕΣ ΑΠΑΛΛΑΓΕΣ
'1594': ΕΠΙΔΟΜΑ ΣΤΡΑΤΕΥΣΗΣ
'1595': ΔΙΑΡΚΗ ΣΤΡΑΤΟΔΙΚΕΙΑ
'1596': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ Ο.Γ.Α
'1597': ΑΣΤΥΝΟΜΙΑ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ
'1598': ΦΡΟΝΤΙΣΤΕΣ ΜΟΝΑΔΩΝ
'1599': ΑΡΑΒΟΣΙΤΟΣ
'1600': ΜΗΤΡΟΠΟΛΕΙΣ
'1601': ΦΙΛΑΝΘΡΩΠΙΚΑ ΣΩΜΑΤΕΙΑ
'1602': ΔΙΑΦΟΡΟΙ ΠΟΛΥΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ
'1603': ΕΞΥΓΙΑΝΤΙΚΑ ΕΡΓΑ
'1604': ΦΥΛΛΑ ΠΟΙΟΤΗΤΑΣ ΝΑΥΤΩΝ
'1605': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΚΑΙ ΣΩΜΑΤΕΙΑ
'1606': ΕΣΤΙΑ ΝΑΥΤΙΚΩΝ
'1607': ΓΛΥΚΑ ΚΑΙ ΚΟΝΣΕΡΒΕΣ
'1608': ΠΡΟΣΤΑΣΙΑ ΥΠΟΒΡΥΧΙΩΝ ΚΑΛΩΔΙΩΝ
'1609': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΙ ΕΜΠΟΡΙΑ ΣΥΚΩΝ
'1610': ΧΑΡΟΚΟΠΕΙΟ
'1611': ΔΙΑΜΕΤΑΚΟΜΙΣΗ ΣΤΗΝ ΑΛΒΑΝΙΑ
'1612': ΕΠΙΘΕΩΡΗΣΗ ΦΥΛΑΚΩΝ
'1613': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ
'1614': ΚΙΝΗΜΑΤΟΓΡΑΦΙΚΗ ΒΙΟΜΗΧΑΝΙΑ
'1615': ΠΙΣΤΟΠΟΙΗΤΙΚΑ ΠΡΟΕΛΕΥΣΕΩΣ
'1616': ΤΟΥΡΙΣΤΙΚΗ ΠΡΟΠΑΓΑΝΔΑ
'1617': ΕΙΣΦΟΡΑ ΕΙΣΑΓΩΓΕΩΝ
'1618': ΚΑΖΙΝΟ
'1619': ΜΕ ΤΗΝ ΕΛΒΕΤΙΑ
'1620': ΔΙΚΑΣΤΙΚΟΙ ΕΠΙΜΕΛΗΤΕΣ
'1621': ΚΩΔΙΚΑΣ ΠΟΙΝΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ
'1622': ΤΟΠΙΚΕΣ ΔΙΟΙΚΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ
'1623': ΕΤΑΙΡΕΙΕΣ ΚΕΦΑΛΑΙΟΠΟΙΗΣΕΩΣ
'1624': ΟΡΥΖΑ
'1625': ΔΙΟΙΚΗΤΙΚΟ ΣΥΜΒΟΥΛΙΟ Ο.Γ.Α
'1626': ΕΚΠΑΙΔΕΥΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΣΧΟΛΩΝ Π.Ν
'1627': ΒΑΣΙΛΕΙΑ ΚΑΙ ΑΝΤΙΒΑΣΙΛΕΙΑ
'1628': ΥΠΗΡΕΣΙΑ ΣΤΙΣ ΕΠΑΡΧΙΕΣ Τ.Π. ΚΑΙ Δ
'1629': ΓΕΩΡΓΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ
'1630': ΒΟΥΛΕΥΤΗΡΙΟ
'1631': ΠΟΡΘΜΕΙΑ
'1632': ΕΚΤΕΛΕΣΗ ΥΔΡΑΥΛΙΚΩΝ ΕΡΓΩΝ
'1633': ΙΝΣΤΙΤΟΥΤΑ ΚΡΗΤΙΚΟΥ ΔΙΚΑΙΟΥ - ΑΙΓΑΙΟΥ ΚΑΙ ΔΙΑΦΟΡΑ ΕΡΕΥΝΗΤΙΚΑ ΚΕΝΤΡΑ
'1634': ΑΤΕΛΕΙΕΣ ΔΙΑΦΟΡΕΣ
'1635': ΚΕΝΤΡΑ ΠΑΡΑΘΕΡΙΣΜΟΥ -
'1636': ΣΧΟΛΕΣ ΑΕΡΟΠΟΡΙΑΣ
'1637': ΛΕΠΡΑ
'1638': ΑΙΣΘΗΤΙΚΟΙ
'1639': ΕΚΚΑΘΑΡΙΣΗ ΠΟΙΝΙΚΩΝ ΕΞΟΔΩΝ
'1640': ΓΕΝ. ΟΙΚΟΔΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'1641': ΕΛΕΓΧΟΣ ΔΑΠΑΝΩΝ ΤΟΥ ΚΡΑΤΟΥΣ
'1642': ΠΕΤΡΕΛΑΙΟΚΙΝΗΤΑ ΚΑΙ ΙΣΤΙΟΦΟΡΑ
'1643': ΚΑΛΛΙΕΡΓΕΙΑ ΚΑΠΝΟΥ
'1644': ΔΙΟΙΚΗΣΗ ΜΟΝΑΣΤΗΡΙΩΝ
'1645': ΚΤΗΝΙΑΤΡΙΚΑ ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ
'1646': ΜΟΝΙΜΟΙ ΚΑΙ ΕΘΕΛΟΝΤΕΣ
'1647': ΦΟΡΟΛΟΓΙΑ ΚΕΡΔΩΝ ΕΙΣΑΓΩΓΕΩΝ
'1648': ΑΓΩΓΕΣ ΕΞΩΣΕΩΣ ΜΙΣΘΩΤΩΝ
'1649': ΟΡΓΑΝΩΣΗ ΕΞΩΤΕΡΙΚΟΥ ΕΜΠΟΡΙΟΥ
'1650': ΑΓΩΓΕΣ ΜΗΧΑΝΙΚΩΝ
'1651': ΝΑΥΤΙΚΗ ΣΧΟΛΗ ΠΟΛΕΜΟΥ
'1652': ΜΕΤΑΦΟΡΑ ΘΕΣΕΩΝ
'1653': ΕΙΣΑΓΩΓΗ ΕΠΑΓΓΕΛΜΑΤΙΚΟΥ ΥΛΙΚΟΥ
'1654': ΣΥΓΚΡΟΤΗΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ
'1655': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
(T.Ε.Α.Π.Α.Ε.)
'1656': ΣΥΛΛΟΓΗ ΚΑΙ ΔΙΑΚΙΝΗΣΗ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ ΕΡΜΑΤΩΝ
'1657': ΚΕΝΤΡΑ ΑΔΥΝΑΤΙΣΜΑΤΟΣ – ΔΙΑΙΤΟΛΟΓΙΑΣ
'1658': ΟΜΑΔΙΚΗ ΚΑΤΑΓΓΕΛΙΑ ΣΥΜΒΑΣΕΩΣ ΕΡΓΑΣΙΑΣ
'1659': ΔΙΑΦΟΡΑ ΜΟΥΣΕΙΑ
'1660': ΒΕΒΑΙΩΣΗ ΚΑΙ ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ
'1661': ΓΡΑΦΕΙΑ ΤΥΠΟΥ
'1662': ΔΙΟΙΚΗΤΙΚΟ ΠΡΟΣΩΠΙΚΟ
'1663': ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΩΝ
'1664': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΠΡΟΝΟΙΑΣ ΚΑΙ ΑΣΘΕΝΕΙΑΣ ΕΡΓΑΖΟΜΕΝΩΝ
ΣΤΑ ΛΙΜΑΝΙΑ (Τ.Ε.Α.Π.Α.Ε.Λ.)
'1665': ΑΣΦΑΛΙΣΗ ΚΑΠΝΕΡΓΑΤΩΝ
'1666': ΑΝΤΙΣΗΚΩΜΑΤΑ (ΕΞΑΓΟΡΑ ΘΗΤΕΙΑΣ)
'1667': ΡΥΜΟΥΛΚΟΥΜΕΝΑ ΟΧΗΜΑΤΑ
'1668': ΝΟΜΟΙ ΑΝΑΦΕΡΟΜΕΝΟΙ ΣΕ ΠΟΛΛΕΣ ΦΟΡΟΛΟΓΙΕΣ
'1669': ΟΙΚΟΣΥΣΤΗΜΑΤΑ–ΒΙΟΤΟΠΟΙ
'1670': ΠΡΟΣΤΑΣΙΑ ΠΡΟΣΩΠΩΝ
'1671': ΕΘΝΙΚΟ ΤΥΠΟΓΡΑΦΕΙΟ
'1672': ΔΙΚΑΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ
'1673': ΠΡΟΣΤΑΣΙΑ ΒΙΒΛΙΟΥ-ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΒΙΒΛΙΟΥ-ΛΟΓΟΤΕΧΝΙΑ
'1674': ΔΑΣΜΟΙ ΑΝΤΙΝΤΑΜΠΙΓΚ
'1675': ΔΑΣΗ ΠΑΡΑΜΕΘΟΡΙΩΝ ΠΕΡΙΟΧΩΝ
'1676': ΘΕΟΛΟΓΙΚΗ ΣΧΟΛΗ
'1677': ΟΡΟΙ - ΠΡΟΔΙΑΓΡΑΦΕΣ ΤΥΠΟΠΟΙΗΣΗΣ
'1678': ΦΟΡΟΛΟΓΙΑ ΒΥΝΗΣ ΚΑΙ ΖΥΘΟΥ
'1679': ΑΠΟΘΗΚΗ ΚΤΗΝΙΑΤΡΙΚΩΝ ΕΦΟΔΙΩΝ
'1680': ΠΑΡΟΧΗ ΤΗΛΕΦΩΝΙΚΩΝ ΣΥΝΔΕΣΕΩΝ
'1681': ΠΑΡΑΧΩΡΗΣΗ ΙΑΜΑΤΙΚΩΝ ΠΗΓΩΝ
'1682': ΜΑΘΗΤΙΚΑ ΣΥΣΣΙΤΙΑ
'1683': ΠΡΟΣΛΗΨΗ ΕΦΕΔΡΩΝ, ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ, ΠΟΛΥΤΕΚΝΩΝ ΚΑΙ ΑΛΛΩΝ ΑΤΟΜΩΝ
ΜΕ ΕΙΔΙΚΕΣ ΑΝΑΓΚΕΣ
'1684': ΕΡΤ – 3
'1685': ΣΧΟΛΗ ΠΟΛΕΜΟΥ ΑΕΡΟΠΟΡΙΑΣ
'1686': ΤΟΠΟΘΕΤΗΣΕΙΣ - ΜΕΤΑΤΑΞΕΙΣ
'1687': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΠΡΟΣΤΑΣΙΑΣ
'1688': ΦΥΣΙΚΟ ΑΕΡΙΟ
'1689': ΤΕΧΝΙΚΑ ΕΡΓΑ
'1690': ΔΙΠΛΩΜΑΤΟΥΧΟΙ ΑΝΩΤΑΤΩΝ
'1691': ΕΘΝΙΚΟ ΝΟΜΙΣΜΑΤΙΚΟ ΜΟΥΣΕΙΟ
'1692': ΟΙΚΟΝΟΜΙΚΗ ΑΣΤΥΝΟΜΙΑ ΣΤΗ ΘΑΛΑΣΣΑ
'1693': ΑΣΦΑΛΕΙΑ, ΛΕΙΤΟΥΡΓΙΑ ΚΑΙ ΕΚΜΕΤΑΛΛΕΥΣΗ
'1694': ΕΙΔΙΚΑ ΠΡΟΝΟΜΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ
'1695': ΓΡΑΜΜΑΤΕΙΑ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ ΚΑΙ ΕΙΣΑΓΓΕΛΙΩΝ
'1696': ΑΛΙΠΑΣΤΑ
'1697': ΕΠΙΔΟΣΗ ΔΙΚΟΓΡΑΦΩΝ
'1698': ΚΕΝΤΡΙΚΟ ΤΑΜΕΙΟ ΓΕΩΡΓΙΑΣ
'1699': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΜΒΟΥΛΙΑ
'1700': ΤΑΜΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΤΕΛΩΝΕΙΩΝ
'1701': ΝΟΣΗΛΕΥΤΙΚΟ ΙΔΡΥΜΑ Μ.Τ.Σ
'1702': ΔΙΚΑΙΟ ΘΑΛΑΣΣΑΣ-ΥΦΑΛΟΚΡΗΠΙΔΑ
'1703': ΕΙΔΙΚΟΣ ΦΟΡΟΣ ΚΑΤΑΝΑΛΩΣΗΣ
'1704': ΜΕΙΟΝΟΤΙΚΑ ΣΧΟΛΕΙΑ
'1705': ΓΡΑΦΕΙΑ ΕΜΠΟΡΙΚΩΝ ΠΛΗΡΟΦΟΡΙΩΝ
'1706': ΣΥΝΤΟΝΙΣΤΙΚΟΝ ΣΥΜΒΟΥΛΙΟΝ ΝΕΩΝ ΠΡΟΣΦΥΓΩΝ
'1707': ΠΕΡΙΘΑΛΨΗ ΑΠΟΡΩΝ ΚΑΙ ΑΝΑΣΦΑΛΙΣΤΩΝ
'1708': ΦΟΡΟΛΟΓΙΑ ΚΕΝΤΡΩΝ ΔΙΑΣΚΕΔΑΣΕΩΣ ΚΑΙ ΠΟΛΥΤΕΛΕΙΑΣ
'1709': ΣΠΟΓΓΑΛΙΕΥΤΙΚΑ – ΔΥΤΕΣ
'1710': ΔΙΕΘΝΕΣ ΝΟΜΙΣΜΑΤΙΚΟ ΤΑΜΕΙΟ
'1711': ΒΙΒΛΙΟ ΔΙΕΚΔΙΚΗΣΕΩΝ
'1712': ΕΓΚΑΤΑΣΤΑΣΗ - ΛΕΙΤΟΥΡΓΙΑ ΚΑΤΑΣΚΕΥΩΝ ΚΕΡΑΙΩΝ
'1713': ΕΝΩΣΗ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ
'1714': ΛΟΓΙΣΤΙΚΟΣ ΚΑΙ ΟΙΚΟΝΟΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'1715': ΚΑΤΩΤΕΡΑ ΟΡΓΑΝΑ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ
'1716': ΥΠΟΥΡΓΕΙΟ ΕΜΠΟΡΙΚΗΣ ΝΑΥΤΙΛΙΑΣ
'1717': ΟΡΓΑΝΙΣΜΟΣ ΕΛΕΓΚΤΙΚΟΥ ΣΥΝΕΔΡΙΟΥ
'1718': ΑΓΟΡΕΣ ΑΓΡΟΤΙΚΩΝ ΠΡΟΙΟΝΤΩΝ
'1719': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΚΛΩΣΤΟΥΦΑΝΤΟΥΡΓΙΑΣ
'1720': ΞΕΝΑΓΟΙ ΚΑΙ ΔΙΕΡΜΗΝΕΙΣ
'1721': ΠΟΛΕΜΙΚΕΣ ΣΥΝΤΑΞΕΙΣ
'1722': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ ΚΑΙ ΠΕΡΙΧΩΡΩΝ-Ο.Α.Σ.Α
'1723': ΚΑΤΑΣΤΑΤΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΤΑΜΕΙΟΥ ΑΣΦΑΛΙΣΕΩΣ ΑΡΤΕΡΓΑΤΩΝ Κ.Λ.Π
'1724': ΑΤΥΧΗΜΑΤΑ ΣΕ ΜΕΤΑΛΛΕΙΑ ΚΛΠ
'1725': ΦΟΡΟΛΟΓΙΑ ΠΟΛΕΜΙΚΩΝ ΚΕΡΔΩΝ
'1726': ΣΧΕΔΙΟ ΠΟΛΕΩΣ ΘΕΣΣΑΛΟΝΙΚΗΣ
'1727': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ ΑΓΡΟΤ. ΑΣΦΑΛΕΙΑΣ
'1728': ΚΡΑΤΙΚΟ ΩΔΕΙΟ ΘΕΣΣΑΛΟΝΙΚΗΣ
'1729': ΚΕΝΤΡΑ ΑΝΩΤΕΡΗΣ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ (Κ.A.Τ.Ε.)
'1730': ΤΗΛΕΦΩΝΙΚΗ ΑΝΤΑΠΟΚΡΙΣΗ
'1731': ΟΙΚΟΝΟΜΙΚΑ ΓΥΜΝΑΣΙΑ
'1732': ΒΙΒΛΙΑ ΚΑΙ ΕΥΡΕΤΗΡΙΑ ΣΥΝΕΤΑΙΡΙΣΜΩΝ
'1733': ΕΠΙΔΟΜΑ ΑΝΕΡΓΙΑΣ
'1734': ΕΓΓΡΑΦΕΣ, ΕΞΕΤΑΣΕΙΣ, ΠΡΟΓΡΑΜΜΑΤΑ ΚΛΠ
'1735': ΣΧΟΛΗ ΜΟΝΙΜΩΝ ΥΠΑΞΙΩΜΑΤΙΚΩΝ
'1736': ΕΚΚΛΗΣΙΑ ΑΜΕΡΙΚΗΣ
'1737': ΜΕΤΟΧΙΚΟ ΤΑΜΕΙΟ ΣΤΡΑΤΟΥ
'1738': ΝΟΣΗΛΕΙΑ
'1739': ΣΧΟΛΗ ΕΥΕΛΠΙΔΩΝ
'1740': ΥΠΟΥΡΓΕΙΟ ΕΡΓΑΣΙΑΣ ΚΑΙ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ
'1741': ΚΑΝΟΝΙΣΜΟΣ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ ΑΞΙΩΝ ΑΘΗΝΩΝ
'1742': ΑΝΤΙΣΕΙΣΜΙΚΟΣ ΚΑΝΟΝΙΣΜΟΣ
'1743': ΦΑΡΜΑΚΕΥΤΙΚΗ ΔΕΟΝΤΟΛΟΓΙΑ
'1744': ΦΟΡΟΛΟΓΙΑ ΕΛΑΙΩΔΩΝ ΠΡΟΙΟΝΤΩΝ
'1745': ΕΙΔΙΚΑ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΑ ΔΙΚΤΥΑ
'1746': ΤΕΧΝΙΚΕΣ ΥΠΗΡΕΣΙΕΣ
'1747': ΑΡΧΕΙΑ ΥΓΙΕΙΝΗΣ
'1748': ΟΔΟΙΠΟΡΙΚΑ ΚΑΙ ΑΠΟΖΗΜΙΩΣΕΙΣ ΑΠΟΣΤΟΛΩΝ ΕΞΩΤΕΡΙΚΟΥ
'1749': ΔΙΑΦΟΡΟΙ ΛΟΓΙΣΤΙΚΟΙ ΝΟΜΟΙ
'1750': ΕΚΚΛΗΣΙΑΣΤΙΚΟΙ ΥΠΑΛΛΗΛΟΙ
'1751': ΝΑΥΤΙΚΑ ΕΠΑΓΓΕΛΜΑΤΙΚΑ ΣΩΜΑΤΕΙΑ ΚΑΙ ΟΜΟΣΠΟΝΔΙΕΣ
'1752': ΤΕΛΗ ΧΡΗΣΗΣ ΑΕΡΟΛΙΜΕΝΩΝ
'1753': ΠΡΟΑΙΡΕΤΙΚΗ ΑΣΦΑΛΙΣΗ
'1754': ΜΕ ΤΗ ΛΙΒΥΗ
'1755': ΠΟΤΑΜΟΠΛΟΙΑ ΦΟΡΤΙΟΥ ΥΓΡΩΝ ΚΑΥΣΙΜΩΝ
'1756': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΩΝ ΗΛΕΚΤΡΙΚΩΝ ΣΙΔΗΡΟΔΡΟΜΩΝ ΑΘΗΝΩΝ-ΠΕΙΡΑΙΩΣ
(Τ.Σ.Π.-Η.Σ.Α.Π)
'1757': ΜΕΣΑΖΟΝΤΕΣ
'1758': ΣΤΡΑΤΙΩΤΙΚΟΣ ΠΟΙΝΙΚΟΣ
'1759': ΔΙΚΑΙΩΜΑΤΑ ΚΑΙ ΚΑΘΗΚΟΝΤΑ ΦΟΙΤΗΤΩΝ
'1760': ΠΡΟΕΔΡΙΑ ΔΗΜΟΚΡΑΤΙΑΣ
'1761': ΚΩΔΙΚΑΣ ΕΜΠΟΡΙΚΟΥ ΝΟΜΟΥ
'1762': ΣΥΝΤΑΞΙΟΔΟΤΗΣΗ Ο.Γ.Α
'1763': ΣΑΝΑΤΟΡΙΑ
'1764': ΕΛΕΓΧΟΣ ΕΜΠΟΡΙΟΥ ΕΙΔΩΝ ΠΡΩΤΗΣ ΑΝΑΓΚΗΣ
'1765': ΒΑΛΑΝΙΔΙΑ
'1766': ΠΟΛΥΤΕΧΝΙΚΗ ΣΧΟΛΗ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΠΑΤΡΩΝ
'1767': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ
'1768': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΧΡΗΜΑΤΟΔΟΤΗΣΕΩΣ
'1769': ΜΕΤΑΦΟΡΑ ΣΤΟ ΕΣΩΤΕΡΙΚΟ
'1770': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΥΔΡΑΣ
'1771': ΕΓΚΑΤΑΣΤΑΣΗ ΚΑΙ ΚΙΝΗΣΗ ΑΛΛΟΔΑΠΩΝ
'1772': ΣΧΟΛΗ ΤΕΧΝΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ ΑΞΙΩΜΑΤΙΚΩΝ
'1773': ΓΑΜΟΣ ΣΤΡΑΤΙΩΤΙΚΩΝ
'1774': ΑΠΑΓΟΡΕΥΣΗ ΕΞΟΔΟΥ ΟΦΕΙΛΕΤΩΝ
'1775': ΠΡΩΤΕΣ ΥΛΕΣ ΨΕΚΑΣΤΗΡΩΝ
'1776': ΦΙΛΕΚΠΑΙΔΕΥΤΙΚΗ ΕΤΑΙΡΕΙΑ
'1777': ΑΔΕΙΕΣ ΟΔΗΓΩΝ ΑΥΤΟΚΙΝΗΤΩΝ
'1778': ΕΘΝΙΚΗ ΠΙΝΑΚΟΘΗΚΗ ΚΑΙ ΜΟΥΣΕΙΟ ΑΛ. ΣΟΥΤΣΟΥ
'1779': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΜΑΤΑ
'1780': ΕΙΣΠΡΑΞΗ ΠΟΡΩΝ
'1781': ΟΡΓΑΝΩΣΗ ΚΑΙ ΛΕΙΤΟΥΡΓΙΑ ΤΕΧΝΙΚΩΝ ΣΧΟΛΩΝ
'1782': ΔΙΑΘΕΣΗ ΓΑΙΩΝ ΣΤΗ ΘΕΣΣΑΛΙΑ
'1783': ΔΙΑΚΡΙΣΗ ΑΣΦΑΛΙΣΜΕΝΩΝ
'1784': ΑΓΑΘΟΕΡΓΑ ΙΔΡΥΜΑΤΑ ΚΕΡΚΥΡΑΣ
'1785': ΥΠΑΙΘΡΙΟ-ΠΛΑΝΟΔΙΟ ΕΜΠΟΡΙΟ ΚΑΙ ΕΜΠΟΡΟΠΑΝΗΓΥΡΕΙΣ
'1786': ΕΞΑΓΩΓΙΚΑ ΤΕΛΗ
'1787': ΥΠΟΥΡΓΙΚΟ ΣΥΜΒΟΥΛΙΟ - ΟΡΓΑΝΩΣΗ ΥΠΟΥΡΓΕΙΩΝ - ΚΥΒΕΡΝΗΤΙΚΕΣ ΕΠΙΤΡΟΠΕΣ
'1788': ΑΥΤΟΚΙΝΗΤΑ ΚΑΙ ΑΜΑΞΙΔΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ
'1789': ΥΠΗΡΕΣΙΕΣ ΠΕΡΙΦΕΡΕΙΑΚΗΣ ΑΝΑΠΤΥΞΗΣ
'1790': ΔΙΑΤΙΜΗΣΗ ΦΑΡΜΑΚΩΝ
'1791': ΦΟΡΟΛΟΓΙΑ ΕΙΔΩΝ ΠΟΛΥΤΕΛΕΙΑΣ
'1792': ΝΑΥΤΙΚΗ ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ
'1793': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΠΕΤΡΕΛΑΙΟΕΙΔΩΝ
'1794': ΔΩΡΟ ΕΟΡΤΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ
'1795': ΔΙΕΥΚΟΛΥΝΣΕΙΣ ΓΙΑ ΤΗΝ ΑΝΟΙΚΟΔΟΜΗΣΗ
'1796': ΕΠΙΣΚΕΥΑΣΤΕΣ - ΣΥΝΕΡΓΕΙΑ ΕΠΙΣΚΕΥΗΣ ΑΥΤΟΚΙΝΗΤΩΝΟΔΙΚΗ ΒΟΗΘΕΙΑ ΟΧΗΜΑΤΩΝ
'1797': ΠΑΡΑΧΩΡΗΣΗ ΔΑΣΩΝ
'1798': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΑΣΘΕΝΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΠΙΣΤΕΩΣ, ΓΕΝΙΚΗΣ
ΚΑΙ ΑΜΕΡΙΚΑΝ ΕΞΠΡΕΣ
'1799': ΠΛΗΤΤΟΜΕΝΑ ΑΠΟ ΤΗΝ ΑΝΕΡΓΙΑ ΕΠΑΓΓΕΛΜΑΤΑ
'1800': ΤΑΜΕΙΑ Κ.Α.Τ.Ε
'1801': ΕΙΔΙΚΟΙ ΣΤΡΑΤΙΩΤΙΚΟΙ ΟΡΓΑΝΙΣΜΟΙ
'1802': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΙΟΝΙΚΗΣ ΚΑΙ ΛΑΙΚΗΣ ΤΡΑΠΕΖΑΣ (Τ.Α.Π.-
Ι.Λ.Τ.)
'1803': ΠΡΟΣΤΑΣΙΑ ΑΠΟ ΑΚΤΙΝΟΒΟΛΙΕΣ
'1804': ΚΡΑΤΙΚΟ ΘΕΑΤΡΟ Β. ΕΛΛΑΔΟΣ
'1805': ΥΓΕΙΟΝΟΜΙΚΟΣ ΕΛΕΓΧΟΣ ΦΟΙΤΗΤΩΝ
'1806': ΔΙΑΦΟΡΑ
'1807': ΤΕΛΩΝΕΙΑΚΗ ΥΠΗΡΕΣΙΑ ΣΙΔΗΡΟΔΡΟΜΩΝ
'1808': ΕΦΕΥΡΕΣΕΙΣ ΑΦΟΡΩΣΑΙ ΕΘΝ. ΑΜΥΝΑ
'1809': ΥΠΟΒΡΥΧΙΟΣ ΤΗΛΕΓΡΑΦΟΣ
'1810': ΑΔΕΙΕΣ ΟΙΚΟΔΟΜΗΣ ΞΕΝΟΔΟΧΕΙΩΝ
'1811': ΙΝΣΤΙΤΟΥΤΟ ΒΥΖΑΝΤΙΝΩΝ ΣΠΟΥΔΩΝ
'1812': ΣΧΟΛΗ ΓΕΩΤΕΧΝΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΘΕΣΝΙΚΗΣ
'1813': ΒΙΒΛΙΟΘΗΚΕΣ
'1814': ΤΑΜΕΙΑ ΑΝΕΓΕΡΣΕΩΣ ΔΙΔΑΚΤΗΡΙΩΝ
'1815': ΕΠΙΔΟΜΑ ΒΙΒΛΙΟΘΗΚΗΣ
'1816': ΚΑΤΑΣΤΗΜΑΤΑ ΑΦΟΡΟΛΟΓΗΤΩΝ ΕΙΔΩΝ
'1817': ΕΠΙΧΕΙΡΗΣΕΙΣ ΠΕΡΙΘΑΛΨΕΩΣ ΗΛΙΚΙΩΜΕΝΩΝ Η ΑΝΑΠΗΡΩΝ
'1818': ΛΙΜΕΝΙΚΟΙ ΣΤΑΘΜΟΙ
'1819': ΝΟΜΟΘΕΤΙΚΕΣ ΕΞΟΥΣΙΟΔΟΤΗΣΕΙΣ
'1820': ΘΑΛΑΜΟΙ ΡΑΔΙΟΙΣΟΤΟΠΩΝ
'1821': ΔΙΟΙΚΗΣΗ ΕΚΚΛΗΣΙΑΣΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΗΣ
'1822': ΑΠΑΓΟΡΕΥΜΕΝΕΣ ΚΑΙ
'1823': ΗΘΟΠΟΙΟΙ
'1824': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΔΙΕΘΝΩΝ ΕΚΘΕΣΕΩΝ
'1825': ΣΦΡΑΓΙΣΤΟΣ ΧΑΡΤΗΣ
'1826': ΕΤΑΙΡΕΙΕΣ ΔΙΑΧΕΙΡΙΖΟΜΕΝΕΣ ΔΗΜΟΣΙΑ ΣΥΜΦΕΡΟΝΤΑ
'1827': ΤΕΛΩΝΕΙΑΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ
'1828': ΔΕΞΑΜΕΝΟΠΛΟΙΑ
'1829': ΚΕΝΤΡΟ ΔΙΕΘΝΟΥΣ ΚΑΙ ΕΥΡΩΠΑΙΚΟΥ
'1830': ΕΠΙΒΑΤΗΓΑ ΜΕΣΟΓΕΙΑΚΑ ΚΑΙ ΤΟΥΡΙΣΤΙΚΑ ΠΛΟΙΑ
'1831': ΕΠΙΘΕΩΡΗΣΗ ΔΙΚΑΣΤΙΚΩΝ ΥΠΑΛΛΗΛΩΝ
'1832': ΚΑΝΟΝΙΣΜΟΣ ΘΕΑΤΡΩΝ ΚΙΝΗΜΑΤΟΓΡΑΦΩΝ ΚΛΠ
'1833': ΜΕΤΑΛΛΕΥΤΙΚΟΣ ΚΩΔΙΚΑΣ
'1834': ΚΑΤΑΣΤΑΤΙΚΟ Τ.Ε.Α.Α.Π.Α.Ε
'1835': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΛΕΣΧΗ
'1836': ΕΜΠΟΡΙΚΑ ΚΑΙ ΒΙΟΜΗΧΑΝΙΚΑ ΣΗΜΑΤΑ - (ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ)
'1837': ΕΠΙΔΟΜΑΤΑ ΑΠΟΛΥΟΜΕΝΩΝ ΟΠΛΙΤΩΝ ΩΣ ΑΝΙΚΑΝΩΝ
'1838': ΣΥΜΒΟΥΛΙΟ ΕΝΕΡΓΕΙΑΣ
'1839': ΣΧΟΛΗ ΝΟΜΙΚΩΝ,ΟΙΚΟΝΟΜΙΚΩΝ ΚΑΙ ΠΟΛΙΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'1840': ΠΡΟΠΛΗΡΩΜΕΣ ΚΑΙ ΠΡΟΚΑΤΑΒΟΛΕΣ
'1841': ΚΛΑΔΟΣ ΑΣΘΕΝΕΙΑΣ Τ.Ε.Β.Ε
'1842': ΔΙΑΝΟΜΗ ΓΑΙΩΝ ΚΩΠΑΙΔΑΣ
'1843': ΠΡΟΣΩΠΙΚΟ ΑΣΦΑΛΕΙΑΣ Ν.Π.Δ.Δ. - ΟΡΓΑΝΙΣΜΩΝ & ΕΠΙΧΕΙΡΗΣΕΩΝ
'1844': ΥΠΟΥΡΓΕΙΟ ΥΠΟΔΟΜΩΝ, ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΔΙΚΤΥΩΝ
'1845': ΑΕΡΟΝΑΥΑΓΟΣΩΣΤΙΚΗ ΜΟΝΑΔΑ
'1846': ΚΟΥΡΕΙΑ, ΚΟΜΜΩΤΗΡΙΑ Κ.Λ.Π
'1847': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΔΙΚΑΣΤΙΚΩΝ ΕΠΙΜΕΛΗΤΩΝ
'1848': ΕΙΔΙΚΑ ΣΥΝΕΡΓΕΙΑ
'1849': ΚΑΤΕΨΥΓΜΕΝΑ ΚΡΕΑΤΑ
'1850': ΜΕΣΟΓΕΙΑΚΑ ΔΡΟΜΟΛΟΓΙΑ ΕΠΙΒΑΤΗΓΩΝ ΠΛΟΙΩΝ
'1851': ΣΥΓΚΡΟΤΗΣΗ ΠΡΟΣΩΠΙΚΟΥ ΑΕΡΟΠΟΡΙΑΣ
'1852': ΥΠΑΛΛΗΛΙΚΟΣ ΚΩΔΙΚΑΣ
'1853': ΓΕΝΙΚΕΣ ΔΙΑΤΑΞΕΙΣ ΠΕΡΙ ΦΑΡΜΑΚΕΙΩΝ
'1854': ΔΙΑΦΟΡΟΙ ΣΤΕΓΑΣΤΙΚΟΙ ΝΟΜΟΙ
'1855': ΥΠΟΥΡΓΕΙΟ ΣΥΝΤΟΝΙΣΜΟΥ
'1856': ΠΡΟΣΛΗΨΕΙΣ ΣΤΟ ΔΗΜΟΣΙΟ
'1857': ΤΑΜΕΙΟ ΕΠΙΚ. ΑΣΦΑΛ. ΠΡΟΣΩΠ. Ο.Ε.Α.Σ. ΚΑΙ ΥΠΑΛΛ. ΓΡΑΦΕΙΩΝ ΚΟΙΝΩΝ
ΤΑΜΕΙΩΝ ΙΔΙΩΤΙΚΩΝ ΛΕΩΦΟΡΕΙΩΝ
'1858': ΣΤΡΑΤΙΩΤΙΚΗ ΑΣΤΥΝΟΜΙΑ
'1859': ΝΟΜΙΣΜΑΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1860': ΑΡΧΗ ΔΙΑΣΦΑΛΙΣΗΣ ΑΠΟΡΡΗΤΟΥ ΕΠΙΚΟΙΝΩΝΙΩΝ (Α.Δ.Α.Ε.)
'1861': ΣΤΡΑΤΙΩΤΙΚΑ ΣΥΝΕΡΓΕΙΑ
'1862': ΠΡΟΣΩΠΙΚΗ ΚΡΑΤΗΣΗ
'1863': ΕΦΗΜΕΡΙΔΑ ΤΗΣ ΚΥΒΕΡΝΗΣΕΩΣ
'1864': ΑΝΩΤΑΤΟ ΥΓΕΙΟΝΟΜΙΚΟ ΣΥΜΒΟΥΛΙΟ
'1865': ΓΡΑΜΜΑΤΕΙΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ
'1866': ΚΑΤΑΣΤΑΣΗ ΔΙΟΠΩΝ, ΝΑΥΤΩΝ ΚΑΙ ΝΑΥΤΟΠΑΙΔΩΝ
'1867': ΠΕΡΙΠΤΩΣΕΙΣ ΑΜΟΙΒΑΙΑΣ ΣΥΝΔΡΟΜΗΣ
'1868': ΥΠΟΝΟΜΟΙ ΠΡΩΤΕΥΟΥΣΑΣ
'1869': ΤΕΛΗ ΔΙΑΔΡΟΜΗΣ ΕΝΑΕΡΙΟΥ ΧΩΡΟΥ
'1870': ΥΓΕΙΟΝΟΜΙΚΑΙ ΕΠΙΤΡΟΠΑΙ
'1871': ΙΑΤΡΙΚΕΣ ΕΙΔΙΚΟΤΗΤΕΣ
'1872': ΕΡΤ – 2
'1873': ΕΚΤΕΛΕΣΗ ΕΡΓΩΝ Ο.Σ.Ε.ΚΑΙ ΣΥΝΔΕΔΕΜΕΝΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'1874': ΓΕΩΡΓΙΚΕΣ ΣΧΟΛΕΣ
'1875': ΣΥΜΜΕΤΟΧΗ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΣΕ ΠΡΟΜΗΘΕΙΕΣ ΔΗΜΟΣΙΟΥ
'1876': ΔΙΚΑΙΩΜΑ ΧΟΡΤΟΝΟΜΗΣ
'1877': ΟΙΚΟΚΥΡΙΚΕΣ ΣΧΟΛΕΣ
'1878': ΚΕΝΤΡΑ ΥΓΕΙΑΣ-ΠΟΛΥΙΑΤΡΕΙΑ
'1879': ΔΙΚΑΣΤΗΡΙΟ ΣΥΝΔΙΑΛΛΑΓΗΣ ΚΑΙ ΔΙΑΙΤΗΣΙΑΣ
'1880': ΕΠΙΘΕΩΡΗΣΗ ΙΧΘΥΩΝ
'1881': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΞΕΥΓΕΝΙΣΜΟΥ ΔΕΝΔΡΩΝ
'1882': ΦΟΙΤΗΤΕΣ
'1883': ΔΟΜΗΣΗ ΕΠΙ ΡΥΜΟΤΟΜΟΥΜΕΝΩΝ ΑΚΙΝΗΤΩΝ
'1884': ΑΠΑΣΧΟΛΗΣΗ - ΕΞΕΙΔΙΚΕΥΣΗ - ΚΑΤΑΡΤΙΣΗ ΑΝΕΡΓΩΝ
'1885': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΦΑΡΜΑΚΕΥΤΙΚΩΝ ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Υ.Φ.Ε.)
'1886': ΝΟΜΙΣΜΑΤΙΚΟ ΣΥΣΤΗΜΑ
'1887': ΑΠΟΓΡΑΦΗ ΝΑΥΤΙΚΩΝ
'1888': ΕΘΝΙΚΟ ΘΕΑΤΡΟ
'1889': ΥΠΗΡΕΣΙΑ ΕΠΙΣΤΗΜΟΝΙΚΗΣ ΄ΕΡΕΥΝΑΣ ΚΑΙ ΑΝΑΠΤΥΞΕΩΣ
'1890': ΠΑΡΟΧΕΣ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'1891': ΣΙΒΙΤΑΝΙΔΕΙΟΣ ΣΧΟΛΗ
'1892': ΣΤΡΑΤΙΩΤΙΚΗ ΙΑΤΡΙΚΗ ΣΧΟΛΗ
'1893': ΥΠΟΥΡΓΕΙΟ ΚΟΙΝΩΝΙΚΩΝ ΥΠΗΡΕΣΙΩΝ
'1894': ΑΠΑΓΟΡΕΥΣΗ ΑΠΑΛΛΟΤΡΙΩΣΗΣ ΠΛΟΙΩΝ
'1895': ΠΑΝΕΠΙΣΤΗΜΙΑΚΑ ΣΥΓΓΡΑΜΜΑΤΑ
'1896': ΜΟΥΣΟΥΛΜΑΝΟΙ
'1897': ΔΙΚΑΣΤΙΚΟΙ ΣΥΜΒΟΥΛΟΙ ΠΟΛΕΜΙΚΟΥ ΝΑΥΤΙΚΟΥ
'1898': ΑΕΡΟΠΟΡΙΚΑ ΕΡΓΑ ΚΑΙ ΠΡΟΜΗΘΕΙΕΣ
'1899': ΤΟΠΙΚΑ ΕΓΓΕΙΟΒΕΛΤΙΩΤΙΚΑ ΕΡΓΑ
'1900': ΦΟΡΟΛΟΓΙΑ ΖΩΩΝ
'1901': ΣΥΝΤΑΓΜΑ
'1902': ΝΟΜΟΙ ΠΕΡΙ ΧΡΗΜΑΤΙΣΤΗΡΙΟΥ - ΕΠΙΤΡΟΠΗ ΚΕΦΑΛΑΙΑΓΟΡΑΣ - ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΗ
ΑΓΟΡΑ ΠΑΡΑΓΩΓΩΝ
'1903': ΓΕΩΤΡΗΣΕΙΣ
'1904': ΤΑΜΕΙΑ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΜΠΟΡΙΚΗΣ
ΤΡΑΠΕΖΑΣ ΕΛΛΑΔΑΣ (Τ.Ε.Α.Π.Ε.Τ.Ε ΚΑΙ Τ.Α.Π.Ε.Τ.Ε.)
'1905': ΕΦΕΔΡΟΙ ΑΕΡΟΠΟΡΙΑΣ
'1906': ΚΑΤ’ ΙΔΙΑΝ ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ
'1907': ΣΧΟΛΗ ΝΟΜΙΚΩΝ ΚΑΙ ΟΙΚΟΝΟΜΙΚΩΝ ΕΠΙΣΤΗΜΩΝ
'1908': ΚΑΤΑΒΟΛΗ ΕΙΣΦΟΡΩΝ ΜΕ ΔΟΣΕΙΣ
'1909': ΠΑΛΑΙΟΤΕΡΕΣ ΑΕΡΟΠΟΡΙΚΕΣ ΕΤΑΙΡΕΙΕΣ
'1910': ΤΡΟΜΟΚΡΑΤΙΑ - ΟΡΓΑΝΩΜΕΝΗ
'1911': ΤΑΜΕΙΑ ΕΛΙΑΣ-ΔΑΚΟΚΤΟΝΙΑ
'1912': ΓΡΑΦΕΙΑ ΕΥΡΕΣΕΩΣ ΝΑΥΤΙΚΗΣ ΕΡΓΑΣΙΑΣ
'1913': ΑΡΤΟΠΟΙΕΙΑ
'1914': ΦΟΡΟΛΟΓΙΑ ΚΥΚΛΟΥ ΕΡΓΑΣΙΩΝ
'1915': ΣΥΝΑΛΛΑΓΜΑΤΙΚΗ ΚΑΙ ΓΡΑΜΜΑΤΙΟ ΣΕ ΔΙΑΤΑΓΗ
'1916': ΠΕΡΙΦΕΡΕΙΑΚΕΣ ΥΠΗΡΕΣΙΕΣ ΥΠΟΥΡΓΕΙΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ
'1917': ΕΛΛΗΝΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ ΤΟΥΡΙΣΜΟΥ
'1918': ΠΡΟΣΤΑΣΙΑ ΤΡΑΥΜΑΤΙΩΝ, ΑΙΧΜΑΛΩΤΩΝ ΚΑΙ ΑΜΑΧΟΥ ΠΛΗΘΥΣΜΟΥ
'1919': ΚΑΝΟΝΙΣΜΟΣ ΛΕΙΤΟΥΡΓΙΑΣ Τ.Ε.Β.Ε
'1920': ΣΤΕΓΑΣΗ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ
'1921': ΑΘΛΗΤΙΣΜΟΣ ΚΑΙ ΨΥΧΑΓΩΓΙΑ Π. ΝΑΥΤΙΚΟΥ
'1922': ΑΝΕΛΚΥΣΤΗΡΕΣ - ΑΝΥΨΩΤΙΚΑ ΜΕΣΑ ΚΑΙ ΜΗΧΑΝΗΜΑΤΑ
'1923': ΣΥΝΤΑΞΕΙΣ ΠΛΗΡΩΜΑΤΩΝ ΕΠΙΤΑΚΤΩΝ ΠΛΟΙΩΝ
'1924': ΔΙΚΑΙΩΜΑΤΑ ΥΠΕΡΗΜΕΡΙΑΣ
'1925': ΚΩΔΙΚΑΣ ΠΟΛΕΜΙΚΩΝ ΣΥΝΤΑΞΕΩΝ
'1926': ΚΑΠΝΟΣ
'1927': ΠΡΟΣΤΑΣΙΑ ΣΕΙΣΜΟΠΛΗΚΤΩΝ
'1928': ΑΠΟΣΤΡΑΤΕΙΕΣ ΚΑΙ ΑΠΟΚΑΤΑΣΤΑΣΕΙΣ
'1929': ΠΡΟΣΩΠΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΩΝ ΣΧΟΛΩΝ
'1930': ΔΙΕΘΝΕΙΣ ΣΥΜΒΑΣΕΙΣ ΓΙΑ ΤΗΝ ΠΡΟΣΤΑΣΙΑ ΤΩΝ ΕΡΓΑΖΟΜΕΝΩΝ ΑΝΗΛΙΚΩΝ
'1931': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΑΘΗΝΩΝ
'1932': ΕΝΙΣΧΥΣΗ ΕΛΑΙΟΠΑΡΑΓΩΓΗΣ
'1933': ΑΝΟΙΚΤΑ ΣΩΦΡΟΝΙΣΤΙΚΑ ΚΑΤΑΣΤΗΜΑΤΑ
'1934': ΦΙΛΑΝΘΡΩΠΙΚΑ ΙΔΡΥΜΑΤΑ ΖΑΚΥΝΘΟΥ
'1935': ΔΙΑΦΟΡΑ ΕΙΔΗ ΤΡΟΦΙΜΩΝ, ΠΟΤΩΝ & ΑΝΤΙΚΕΙΜΕΝΩΝ
'1936': ΦΟΡΟΛΟΓΙΑ ΕΠΙΧΕΙΡΗΣΕΩΝ ΤΥΠΟΥ
'1937': ΠΕΡΙΟΡΙΣΜΟΙ ΕΙΣΑΓΩΓΗΣ
'1938': ΠΡΟΣΩΡΙΝΗ ΕΙΣΔΟΧΗ ΕΜΠΟΡΕΥΜΑΤΩΝ
'1939': ΑΡΧΕΙΟ
'1940': ΔΙΥΛΙΣΤΗΡΙΑ ΠΕΤΡΕΛΑΙΟΥ
'1941': ΕΙΣΑΓΩΓΗ ΠΑΙΔΑΓΩΓΙΚΟΥ ΥΛΙΚΟΥ
'1942': ΕΠΙΘΕΩΡΗΣΗ ΚΛΗΡΟΔΟΤΗΜΑΤΩΝ
'1943': ΣΙΔΗΡΟΔΡΟΜΟΙ ΒΟΡΕΙΟΔΥΤΙΚΗΣ ΕΛΛΑΔΟΣ
'1944': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΕΡΓΑΤΟΤΕΧΝΙΤΩΝ ΔΟΜΙΚΩΝ ΚΑΙ ΞΥΛΟΥΡΓΙΚΩΝ
ΕΡΓΑΣΙΩΝ (Τ.Ε.Α.Ε.Δ.Ξ.Ε.)
'1945': ΤΑΜΕΙΑ ΠΡΟΝΟΙΑΣ ΣΤΙΣ ΠΡΕΣΒΕΙΕΣ
'1946': ΟΙΚΟΓΕΝΕΙΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ - ΥΓΕΙΑ ΠΑΙΔΙΟΥ
'1947': ΑΡΧΙΕΡΕΙΣ
'1948': ΣΥΜΒΟΥΛΙΑ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ
'1949': ΝΟΣΟΚΟΜΕΙΑΚΗ ΠΕΡΙΘΑΛΨΗ
'1950': ΚΑΤΑΣΤΗΜΑΤΑ ΠΩΛΗΣΕΩΣ ΟΙΝΟΠΝΕΥΜΑΤΩΔΩΝ ΠΟΤΩΝ ΚΑΙ ΚΕΝΤΡΑ ΔΙΑΣΚΕΔΑΣΕΩΣ
'1951': ΠΡΩΤΕΥΟΥΣΑ
'1952': ΠΟΛΥΤΕΧΝΕΙΟ ΚΡΗΤΗΣ
'1953': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΕΤΑΙΡΕΙΩΝ ΤΣΙΜΕΝΤΩΝ (Τ.Ε.Α.Π.Ε.Τ.)
'1954': ΕΛΛΗΝΙΚΟΣ ΤΑΠΗΤΟΥΡΓΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ
'1955': ΕΦΑΡΜΟΓΗ ΔΗΜΟΣΙΟΥΠΑΛΛΗΛΙΚΟΥ ΚΩΔΙΚΑ
'1956': ΗΛΕΚΤΡΟΛΟΓΙΚΟ ΕΡΓΑΣΤΗΡΙΟ
'1957': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΕΡΓΟΛΗΠΤΩΝ
'1958': ΜΕΣΙΤΕΣ ΑΣΤΙΚΩΝ ΣΥΜΒΑΣΕΩΝ
'1959': ΠΛΩΤΕΣ ΔΕΞΑΜΕΝΕΣ
'1960': ΚΑΝΟΝΙΣΜΟΙ ΦΟΡΤΩΣΕΩΝ
'1961': ΕΙΔΙΚΑ ΕΠΙΔΟΜΑΤΑ
'1962': ΠΟΙΝΙΚΟΣ ΚΩΔΙΚΑΣ
'1963': ΕΙΔΙΚΟΣ ΛΟΓΑΡΙΑΣΜΟΣ ΠΡΟΝΟΙΑΣ (Τ.Σ.Ε.Υ.Π.)
'1964': ΕΘΝΙΚΗ ΑΝΤΙΣΤΑΣΗ
'1965': ΟΡΓΑΝΙΣΜΟΣ ΒΙΟΜΗΧΑΝΙΚΗΣ ΑΝΑΠΤΥΞΗΣ
'1966': ΕΡΓΑ ΚΟΙΝΗΣ ΥΠΟΔΟΜΗΣ
'1967': ΔΙΕΥΘΥΝΣΗ TΕΛΩΝΕΙΩΝ ΠΕΙΡΑΙΑ
'1968': ΙΑΤΡΙΚΗ ΣΧΟΛΗ ΙΩΑΝΝΙΝΩΝ
'1969': ΖΩΟΚΛΟΠΗ ΚΑΙ ΖΩΟΚΤΟΝΙΑ
'1970': ΡΥΘΜΙΣΙΣ ΚΙΝΗΣΕΩΣ ΕΝ ΟΔΟΙΣ
'1971': ΕΤΑΙΡΕΙΕΣ ΠΡΟΣΤΑΣΙΑΣ ΚΡΑΤΟΥΜΕΝΩΝ - ΑΠΟΦΥΛΑΚΙΖΟΜΕΝΩΝ
'1972': ΔΑΣΙΚΗ ΔΙΕΥΘΕΤΗΣΗ ΧΕΙΜΑΡΡΩΝ
'1973': ΣΥΝΟΡΙΑΚΟΙ ΦΥΛΑΚΕΣ
'1974': ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΠΑΝΜΙΟΥ ΙΩΑΝΝΙΝΩΝ
'1975': ΕΚΠΑΙΔΕΥΣΗ Π.ΝΑΥΤΙΚΟΥ
'1976': ΔΙΚΑΙΟΣΤΑΣΙΟ ΕΠΙΣΤΡΑΤΕΥΣΕΩΣ 1974
'1977': ΡΑΔΙΟΤΗΛΕΓΡΑΦΙΚΗ ΚΑΙ ΡΑΔΙΟΤΗΛΕΦΩΝΙΚΗ ΥΠΗΡΕΣΙΑ
'1978': ΦΑΡΜΑΚΑ-ΙΔΙΟΣΚΕΥΑΣΜΑΤΑ
'1979': ΣΥΝΤΕΛΕΣΤΕΣ ΚΕΡΔΟΥΣ ΕΠΑΓΓΕΛΜΑΤΙΩΝ
'1980': ΕΘΝΙΚΟ ΚΕΝΤΡΟ ΚΟΙΝΩΝΙΚΩΝ ΕΡΕΥΝΩΝ
'1981': ΚΕΦΑΛΑΙΟ ΝΑΥΤΙΚΗΣ ΕΚΠΑΙΔΕΥΣΕΩΣ
'1982': ΕΙΣΠΡΑΞΗ ΕΣΟΔΩΝ ΠΑΡΕΛΘΟΥΣΩΝ ΧΡΗΣΕΩΝ
'1983': ΟΡΓΑΝΙΣΜΟΣ ΗΝΩΜΕΝΩΝ ΕΘΝΩΝ
'1984': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΝΗΣΟΥ ΘΗΡΑΣ
'1985': ΚΕΝΤΡΙΚΗ ΑΓΟΡΑ ΘΕΣΣΑΛΟΝΙΚΗΣ
'1986': ΔΙΑΦΘΟΡΑ ΑΛΛΟΔΑΠΩΝ ΔΗΜΟΣΙΩΝ ΛΕΙΤΟΥΡΓΩΝ
'1987': ΓΕΩΠΟΝΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ
'1988': ΚΑΝΟΝΙΣΜΟΣ ΣΤΡΑΤΟΔΙΚΕΙΩΝ
'1989': ΔΙΑΦΟΡΕΣ ΥΓΕΙΟΝΟΜΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'1990': ΤΟΥΡΙΣΤΙΚΑ ΛΕΩΦΟΡΕΙΑ
'1991': ΔΑΝΕΙΑ ΑΠΟ ΕΚΔΟΤΙΚΕΣ ΤΡΑΠΕΖΕΣ
'1992': ΕΠΙΘΑΛΑΣΣΙΑ ΑΡΩΓΗ - ΡΥΜΟΥΛΚΗΣΗ ΠΛΟΙΩΝ
'1993': ΠΡΟΣΤΑΣΙΑ ΤΟΥ ΚΑΘΕΣΤΩΤΟΣ
'1994': ΣΥΜΒΑΣΕΙΣ ΠΕΡΙ ΥΛΙΚΟΥ ΕΥΗΜΕΡΙΑΣ ΝΑΥΤΙΛΛΟΜΕΝΩΝ
'1995': ΜΕΣΙΤΕΣ ΕΓΧΩΡΙΩΝ ΠΡΟΙΟΝΤΩΝ
'1996': ΚΡΑΤΙΚΗ ΟΡΧΗΣΤΡΑ ΑΘΗΝΩΝ
'1997': ΤΜΗΜΑΤΑ ΜΟΥΣΙΚΩΝ - ΘΕΑΤΡΙΚΩΝ ΣΠΟΥΔΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ
ΕΝΗΜΕΡΩΣΗΣ
'1998': ΠΕΙΘΑΡΧΙΚΗ ΕΞΟΥΣΙΑ ΛΙΜΕΝΙΚΩΝ ΑΡΧΩΝ
'1999': ΙΝΣΤΙΤΟΥΤΟ ΑΜΥΝΤΙΚΩΝ ΑΝΑΛΥΣΕΩΝ (Ι.Α.Α.)
'2000': ΙΔΙΩΤΙΚΟΙ ΣΤΑΘΜΟΙ ΑΣΥΡΜΑΤΟΥ - ΧΡΗΣΗ ΡΑΔΙΟΣΥΧΝΟΤΗΤΩΝ
'2001': ΑΝΑΓΝΩΡΙΣΗ ΞΕΝΩΝ ΚΑΤΑΜΕΤΡΗΣΕΩΝ
'2002': ΓΕΝΟΚΤΟΝΙΑ
'2003': ΕΠΕΞΕΡΓΑΣΙΑ ΚΑΠΝΟΥ
'2004': ΣΥΜΒΟΥΛΙΟ ΕΠΙΚΡΑΤΕΙΑΣ
'2005': ΙΑΤΡΟΙ Ι.Κ.Α
'2006': ΥΠΟΘΗΚΗ
'2007': ΑΡΜΟΔΙΟΤΗΤΑ ΛΙΜΕΝΙΚΟΥ ΣΩΜΑΤΟΣ
'2008': ΕΙΣΑΓΩΓΕΣ ΓΙΑ ΕΚΘΕΣΕΙΣ, ΣΥΝΕΔΡΙΑ ΚΛΠ
'2009': ΕΥΡΩΠΑΙΚΗ ΤΡΑΠΕΖΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗ-ΑΝΑΠΤΥΞΗ
'2010': ΑΕΡΟΔΡΟΜΙΟ ΣΠΑΤΩΝ
'2011': ΤΜΗΜΑ ΔΗΜΟΣΙΟΓΡΑΦΙΑΣ - ΜΕΣΩΝ ΜΑΖΙΚΗΣ ΕΠΙΚΟΙΝΩΝΙΑΣ
'2012': ΤΟΚΟΣ
'2013': ΕΝΙΣΧΥΣΗ ΠΟΛΕΜΟΠΑΘΩΝ ΚΛΠ. ΑΓΡΟΤΩΝ
'2014': ΕΞΟΔΑ ΚΗΔΕΙΑΣ ΣΤΡΑΤΙΩΤΙΚΩΝ
'2015': ΠΑΡΟΧΕΣ ΥΠΑΛΛΗΛΩΝ
'2016': ΠΡΟΣΤΑΣΙΑ ΣΙΤΟΠΑΡΑΓΩΓΗΣ
'2017': ΑΣΦΑΛΙΣΗ Ο.Γ.Α ΑΠΟ ΑΝΕΜΟΘΥΕΛΛΑ ΚΑΙ ΠΛΗΜΜΥΡΑ
'2018': ΔΙΕΥΘΥΝΣΗ ΚΑΤΑΣΚΕΥΩΝ ΚΑΙ ΕΞΟΠΛΙΣΜΟΥ
'2019': ΤΕΛΩΝΕΙΑΚΟΙ ΥΠΟΛΟΓΟΙ
'2020': ΓΕΝΙΚΗ ΓΡΑΜΜΑΤΕΙΑ ΑΘΛΗΤΙΣΜΟΥ
'2021': ΣΥΝΤΑΞΕΙΣ
'2022': ΑΔΕΙΕΣ ΠΡΟΣΩΠΙΚΟΥ Λ.Σ
'2023': ΣΥΝΤΑΞΕΙΣ ΣΤΡΑΤΙΩΤΙΚΩΝ ΠΑΘΟΝΤΩΝ ΣΤΗΝ
'2024': ΑΣΦΑΛΙΣΗ ΕΠΙΒΑΤΩΝ
'2025': ΑΠΑΛΛΟΤΡΙΩΣΗ ΑΚΙΝΗΤΩΝ
'2026': ΣΧΟΛΗ ΕΠΙΣΤΗΜΩΝ ΥΓΕΙΑΣ
'2027': ΕΝΟΙΚΙΟΣΤΑΣΙΟ ΒΟΣΚΩΝ
'2028': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΗΘΟΠΟΙΩΝ - ΣΥΓΓΡΑΦΕΩΝ ΤΕΧΝΙΚΩΝ ΘΕΑΤΡΟΥ
'2029': ΕΥΡΩΠΑΙΚΟ ΕΝΤΑΛΜΑ ΣΥΛΛΗΨΗΣ
'2030': ΑΝΤΙΚΕΙΜΕΝΑ ΔΕΔΗΛΩΜΕΝΗΣ ΑΞΙΑΣ ΑΝΤΙΚΑΤΑΒΟΛΕΣ
'2031': ΓΕΝΙΚΗ ΔΙΕΥΘΥΝΣΗ ΜΕΤΑΦΟΡΩΝ
'2032': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΔΙΚΑΙΟΣΥΝΗΣ
'2033': ΕΥΘΥΝΗ ΥΠΟΥΡΓΩΝ
'2034': ΤΜΗΜΑ ΚΤΗΝΙΑΤΡΙΚΗΣ
'2035': ΔΙΚΑΣΤΙΚΟ ΣΩΜΑ ΕΝΟΠΛΩΝ ΔΥΝΑΜΕΩΝ
'2036': ΕΝΟΡΙΑΚΟΙ ΝΑΟΙ ΚΑΙ ΕΦΗΜΕΡΙΟΙ
'2037': ΥΓΕΙΟΝΟΜΙΚΕΣ ΕΠΙΤΡΟΠΕΣ ΝΑΥΤΙΚΟΥ
'2038': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΚΑΙ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ
ΡΑΡΙΟΦΩΝΙΑΣ-ΤΗΛΕΟΡΑΣΕΩΣ-ΤΟΥΡΙΣΜΟΥ (Τ.Ε.Α.Π.Π. Ε.Ρ.Τ. Τ.)
'2039': ΣΤΡΑΤΙΩΤΙΚΗ ΒΟΗΘΕΙΑ Η.Π.Α
'2040': ΣΥΝΤΑΞΕΙΣ ΠΡΟΣΩΠΙΚΟΥ
'2041': ΧΡΗΜΑΤΙΚΗ ΔΙΑΧΕΙΡΙΣΗ Π. ΝΑΥΤΙΚΟΥ
'2042': ΠΟΛΙΤΙΚΟ ΓΡΑΦΕΙΟ ΠΡΩΘΥΠΟΥΡΓΟΥ
'2043': ΛΟΥΤΡΟΘΕΡΑΠΕΙΑ ΚΑΙ ΑΕΡΟΘΕΡΑΠΕΙΑ
'2044': ΣΥΜΒΟΥΛΙΟ ΚΟΙΝΩΝΙΚΩΝ ΑΣΦΑΛΙΣΕΩΝ
'2045': ΕΝΤΟΚΑ ΓΡΑΜΜΑΤΙΑ
'2046': ΣΩΦΡΟΝΙΣΤΙΚΟΣ ΚΩΔΙΚΑΣ
'2047': ΔΗΜΟΤΙΚΕΣ ΕΠΙΧΕΙΡΗΣΕΙΣ
'2048': ΚΩΔΙΚΑΣ ΠΟΛΙΤΙΚΗΣ ΔΙΚΟΝΟΜΙΑΣ - ΝΕΟΣ
'2049': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΥΠΑΛΛΗΛΩΝ ΚΟΥΡΕΙΩΝ ΚΑΙ ΚΟΜΜΩΤΗΡΙΩΝ
'2050': ΠΡΟΣΩΠΙΚΟ ΣΙΔΗΡΟΔΡΟΜΩΝ- Ο.Σ.Ε.- ΣΙΔΗΡΟΔΡΟΜΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ
'2051': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΟΝ ΤΥΠΟ
'2052': ΤΑΧΥΔΡΟΜΙΚΑ ΔΕΛΤΑΡΙΑ
'2053': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ ΗΛΕΚΤΡ. ΕΤ. ΑΘΗΝΩΝ - ΠΕΙΡΑΙΩΣ ΚΑΙ ΕΛΛΗΝ.
ΗΛΕΚΤΡ. ΕΤΑΙΡΙΑΣ (Τ.Α.Π Η.Ε.Α.Π.- Ε.Η.Ε.)
'2054': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΑΡΤΟΠΟΙΩΝ
'2055': ΔΗΜΟΤΙΚΟΙ ΚΑΙ ΚΟΙΝΟΤΙΚΟΙ ΑΡΧΟΝΤΕΣ
'2056': ΜΕΤΑΦΟΡΑ ΤΑΧΥΔΡΟΜΕΙΟΥ
'2057': ΚΑΝΟΝΙΣΜΟΣ ΠΑΡΟΧΩΝ ΤΑΜΕΙΟΥ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΑΣΦΑΛΙΣΤΩΝ ΚΑΙ
ΠΡΟΣΩΠΙΚΟΥ ΑΣΦΑΛΙΣΤΙΚΩΝ ΕΠΙΧΕΙΡΗΣΕΩΝ (Τ.Ε.Α.Α.Π.Α.Ε.)
'2058': ΠΡΟΣΩΠΙΚΟ
'2059': ΔΗΜΟΣΙΑ ΕΠΙΧΕΙΡΗΣΗ ΗΛΕΚΤΡΙΣΜΟΥ
'2060': ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΩΝ ΩΠΛΙΣΜΕΝΟΥ ΣΚΥΡΟΔΕΜΑΤΟΣ
'2061': ΑΛΕΥΡΑ-ΑΡΤΟΣ
'2062': ΤΕΛΗ ΠΡΟΣΟΡΜΙΣΕΩΣ, ΠΑΡΑΒΟΛΗΣ ΚΑΙ ΠΑΡΟΠΛΙΣΜΟΥ
'2063': ΙΔΙΩΤΙΚΑ ΕΚΠΑΙΔΕΥΤΗΡΙΑ ΦΡΟΝΤΙΣΤΗΡΙΑ
'2064': ΑΡΧΑΙΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ
'2065': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΤΥΠΟΓΡΑΦΩΝ ΚΑΙ ΜΙΣΘΩΤΩΝ ΓΡΑΦΙΚΩΝ ΤΕΧΝΩΝ (Τ.Α.Τ.
& Μ.Γ.Τ)
'2066': ΕΙΔΙΚΕΣ ΕΦΑΡΜΟΓΕΣ ΚΥΡΙΑΚΗΣ ΑΡΓΙΑΣ
'2067': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΓΙΑ ΤΑ ΠΛΗΡΩΜΑΤΑ
'2068': ΑΣΤΙΚΑ ΣΧΟΛΕΙΑ
'2069': ΤΑΜΕΙΑ ΣΥΝΤΑΞΕΩΝ ΕΦΗΜΕΡΙΔΟΠΩΛΩΝ ΚΑΙ ΥΠΑΛΛΗΛΩΝ ΠΡΑΚΤΟΡΕΙΩΝ ΑΘΗΝΩΝ-ΘΕΣΝΙΚΗΣ
(Τ.Σ.Ε.Υ.Π.)
'2070': ΔΟΜΙΚΑ ΕΡΓΑ
'2071': ΝΑΥΣΤΑΘΜΟΣ
'2072': ΑΝΤΙΓΡΑΦΙΚΑ ΔΙΚΑΙΩΜΑΤΑ
'2073': ΕΠΙΔΟΜΑ ΟΙΚΟΓΕΝΕΙΑΚΩΝ ΒΑΡΩΝ
'2074': ΕΛΛΗΝΙΚΗ-ΕΥΡΩΠΑΙΚΗ ΦΑΡΜΑΚΟΠΟΙΙΑ
'2075': ΔΕΛΤΙΑ ΤΑΥΤΟΤΗΤΟΣ
'2076': ΣΧΟΛΙΑΤΡΙΚΗ ΥΠΗΡΕΣΙΑ
'2077': ΥΔΡΟΓΟΝΑΝΘΡΑΚΕΣ
'2078': ΓΕΝΙΚΑ ΠΕΡΙ ΕΚΘΕΣΕΩΝ
'2079': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΕΥΚΟΛΥΝΣΕΙΣ
'2080': ΛΣΜΟΣ ΠΡΟΝΟΙΑΣ ΠΡΟΣΩΠΙΚΟΥ Ι.Κ.Α
'2081': ΕΛΕΓΧΟΣ ΚΤΙΡΙΑΚΩΝ ΕΡΓΩΝ
'2082': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ
'2083': ΕΛΑΙΟΠΥΡΗΝΕΣ
'2084': ΕΜΦΥΤΕΥΤΙΚΑ ΚΤΗΜΑΤΑ
'2085': ΤΟΥΡΙΣΤΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'2086': ΚΛΑΔΟΣ ΑΣΦΑΛΙΣΕΩΣ ΤΕΧΝΙΚΩΝ ΤΥΠΟΥ ΘΕΣΣΑΛΟΝΙΚΗΣ (Κ.Α.Τ.Τ.Θ.)
'2087': ΜΕΤΕΩΡΟΛΟΓΙΚΗ ΥΠΗΡΕΣΙΑ
'2088': ΑΓΡΟΤΙΚΟΣ ΚΩΔΙΚΑΣ
'2089': ΤΕΧΝΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ
'2090': ΕΛΕΓΧΟΣ ΝΟΜΙΜΟΦΡΟΣΥΝΗΣ
'2091': ΑΡΧΑΙΟΛΟΓΙΚΗ ΕΤΑΙΡΙΑ
'2092': ΣΧΟΛΑΖΟΥΣΕΣ ΚΛΗΡΟΝΟΜΙΕΣ
'2093': ΓΕΦΥΡΑ ΡΙΟΥ - ΑΝΤΙΡΡΙΟΥ
'2094': ΦΟΙΤΗΣΗ, ΕΞΕΤΑΣΕΙΣ ΚΛΠ
'2095': ΤΥΧΕΡΑ, ΜΙΚΤΑ ΚΑΙ ΤΕΧΝΙΚΑ ΠΑΙΓΝΙΑ
'2096': ΟΡΓΑΝΙΚΟΙ ΑΡΙΘΜΟΙ ΥΠΑΞΙΩΜΑΤΙΚΩΝ
'2097': ΦΟΡΟΛΟΓΙΑ ΚΙΝΗΤΗΣ ΚΑΙ ΑΚΙΝΗΤΗΣ ΠΕΡΙΟΥΣΙΑΣ
'2098': ΑΤΕΛΕΙΕΣ ΑΓΙΟΥ ΟΡΟΥΣ
'2099': ΜΟΝΟΠΩΛΙΟ ΑΛΑΤΙΟΥ
'2100': ΑΣΦΑΛΙΣΗ ΕΛΛΗΝΩΝ ΕΞΩΤΕΡΙΚΟΥ
'2101': ΔΙΕΘΝΕΣ ΚΕΝΤΡΟ ΑΝΩΤΑΤΩΝ
'2102': ΑΝΑΠΡΟΣΑΡΜΟΓΕΣ ΣΥΝΤΑΞΕΩΝ
'2103': ΓΕΝΙΚΕΣ ΕΠΙΘΕΩΡΗΣΕΙΣ-ΔΙΕΥΘΥΝΣΕΙΣ
'2104': ΣΩΜΑ ΟΡΚΩΤΩΝ ΛΟΓΙΣΤΩΝ
'2105': ΣΕΙΣΜΟΠΛΗΚΤΟΙ ΒΟΡΕΙΟΥ ΕΛΛΑΔΟΣ
'2106': ΠΑΝΕΠΙΣΤΗΜΙΑ ΠΕΙΡΑΙΩΣ-ΜΑΚΕΔΟΝΙΑΣ
'2107': ΧΩΡΟΤΑΞΙΑ ΚΑΙ ΠΕΡΙΒΑΛΛΟΝ
'2108': ΕΣΩΤΕΡΙΚΟΙ ΚΑΝΟΝΙΣΜΟΙ ΕΡΓΑΣΙΑΣ
'2109': ΕΛΕΓΧΟΣ ΝΑΥΤΙΚΩΝ ΑΤΥΧΗΜΑΤΩΝ
'2110': ΠΝΕΥΜΑΤΙΚΑ ΚΕΝΤΡΑ
'2111': ΠΛΟΗΓΙΚΑ ΔΙΚΑΙΩΜΑΤΑ
'2112': ΣΤΡΑΤΕΥΟΜΕΝΟΙ ΔΙΚΗΓΟΡΟΙ
'2113': ΣΥΣΤΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΩΝ
'2114': ΣΙΔΗΡΟΔΡΟΜΟΙ ΠΕΛΟΠΟΝΝΗΣΟΥ
'2115': ΤΜΗΜΑ ΜΕΘΟΔΟΛΟΓΙΑΣ, ΙΣΤΟΡΙΑΣ ΚΑΙ ΘΕΩΡΙΑΣ ΤΗΣ ΕΠΙΣΤΗΜΗΣ
'2116': ΕΥΡΩΠΑΙΚΟ ΠΟΛΙΤΙΣΤΙΚΟ ΚΕΝΤΡΟ ΔΕΛΦΩΝ
'2117': ΣΥΝΕΤΑΙΡΙΣΜΟΙ ΕΓΓΕΙΩΝ ΒΕΛΤΙΩΣΕΩΝ
'2118': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΗΣ ΔΗΜΟΣΙΩΝ ΥΠΑΛΛΗΛΩΝ (Τ.Ε.Α.Δ.Υ.)
'2119': ΙΕΡΟΚΗΡΥΚΕΣ
'2120': ΕΙΡΗΝΟΔΙΚΕΙΑ - ΠΤΑΙΣΜΑΤΟΔΙΚΕΙΑ
'2121': ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ
'2122': ΤΡΑΠΕΖΙΤΙΚΗ ΕΠΙΤΑΓΗ
'2123': ΝΑΥΑΓΟΣΩΣΤΙΚΑ ΚΑΙ ΡΥΜΟΥΛΚΑ
'2124': ΦΟΡΟΛΟΓΙΚΕΣ ΔΙΑΦΟΡΕΣΙ
'2125': ΜΕΤΡΑ ΚΑΙ ΣΤΑΘΜΑ
'2126': ΓΕΝΙΚΟ ΧΗΜΕΙΟ ΤΟΥ ΚΡΑΤΟΥΣ
'2127': ΣΥΜΦΩΝΙΑ ΓΙΑ ΙΣΑ ΟΙΚΟΝΟΜΙΚΑ ΚΟΙΝΩΝΙΚΑ
'2128': ΣΥΝΟΡΙΑΚΟΙ ΣΤΑΘΜΟΙ
'2129': ΑΞΙΩΜΑΤΙΚΟΙ ΣΩΜΑΤΩΝ ΑΣΦΑΛΕΙΑΣ
'2130': ΥΠΗΡΕΣΙΑΚΑ ΣΥΜΒΟΥΛΙΑ
'2131': ΕΙΣΑΓΩΓΙΚΟΣ ΝΟΜΟΣ
'2132': ΚΤΗΜΑΤΟΛΟΓΙΟ
'2133': ΕΤΑΙΡΕΙΑ ΔΙΑΧΕΙΡΙΣΕΩΣ ΥΠΕΓΓΥΩΝ ΠΡΟΣΟΔΩΝ
'2134': ΥΠΟΥΡΓΕΙΟ ΜΑΚΕΔΟΝΙΑΣ – ΘΡΑΚΗΣ
'2135': ΤΟΥΡΙΣΤΙΚΑ ΓΡΑΦΕΙΑ ΚΑΙ ΣΩΜΑΤΕΙΑ
'2136': ΔΑΝΕΙΑ ΑΝΑΣΥΓΚΡΟΤΗΣΗΣ
'2137': ΑΣΤΙΚΕΣ ΣΥΓΚΟΙΝΩΝΙΕΣ ΘΕΣΣΑΛΟΝΙΚΗΣ-Ο.Α.Σ.Θ
'2138': ΕΘΕΛΟΝΤΕΣ ΑΕΡΟΠΟΡΙΑΣ
'2139': ΣΗΜΕΙΩΤΕΣ
'2140': ΤΕΛΗ ΕΓΚΑΤΑΣΤΑΣΗΣ - ΛΕΙΤΟΥΡΓΙΑΣ ΚΕΡΑΙΩΝ
'2141': Η.Π.Α
'2142': ΠΑΝΕΠΙΣΤΗΜΙΑ ΑΙΓΑΙΟΥ, ΙΟΝΙΟΥ ΚΑΙ ΘΕΣΣΑΛΙΑΣ
'2143': ΤΑΜΕΙΟ ΠΡΟΝΟΙΑΣ ΞΕΝΟΔΟΧΩΝ
'2144': ΣΥΜΒΟΥΛΙΑ ΣΤΕΓΑΣΕΩΣ
'2145': ΤΕΧΝΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΙΔΙΩΤΙΚΩΝ ΑΕΡΟΠΛΑΝΩΝ
'2146': ΦΟΡΟΛΟΓΙΑ ΔΗΜΟΣΙΩΝ ΘΕΑΜΑΤΩΝ
'2147': ΣΤΡΑΤΟΛΟΓΙΑ ΟΠΛΙΤΩΝ ΧΩΡΟΦΥΛΑΚΗΣ
'2148': ΓΥΜΝΑΣΙΑ ΑΡΙΣΤΟΥΧΩΝ
'2149': ΣΧΟΛΙΚΗ ΑΝΤΙΛΗΨΗ
'2150': ΕΥΘΥΝΗ ΣΤΡΑΤΙΩΤΙΚΩΝ
'2151': ΣΤΑΘΜΟΙ ΕΠΙΒΗΤΟΡΩΝ
'2152': ΒΕΒΑΙΩΣΗ ΠΤΑΙΣΜΑΤΩΝ ΑΠΟ
'2153': ΔΙΑΖΥΓΙΟ
'2154': ΔΙΕΘΝΗΣ ΣΥΜΒΑΣΗ ΠΕΡΙ ΑΝΑΓΚΑΣΤΙΚΗΣ ΕΡΓΑΣΙΑΣ
'2155': ΔΙΕΥΚΟΛΥΝΣΗ ΔΙΕΘΝΟΥΣ ΝΑΥΤΙΛΙΑΚΗΣ ΚΙΝΗΣΕΩΣ
'2156': ΕΝΟΙΚΙΟΣΤΑΣΙΟ
'2157': ΕΚΘΕΣΕΙΣ ΖΑΠΠΕΙΟΥ ΜΕΓΑΡΟΥ
'2158': ΔΙΑΧΕΙΡΙΣΗ ΥΛΙΚΟΥ Π. ΝΑΥΤΙΚΟΥ
'2159': ΕΦΕΔΡΙΚΑ ΤΑΜΕΙΑ ΚΡΗΤΗΣ
'2160': ΣΙΤΑΡΙ
'2161': ΦΟΡΤΗΓΑ 501-4500 ΤΟΝΝΩΝ
'2162': ΤΡΑΠΕΖΑ ΕΡΓΑΣΙΑΣ
'2163': ΑΤΕΛΕΙΕΣ ΥΠΕΡ ΤΗΣ ΓΕΩΡΓΙΑΣ
'2164': ΑΙΓΙΑΛΟΣ ΚΑΙ ΠΑΡΑΛΙΑ
'2165': ΔΑΣΗ ΙΔΡΥΜΑΤΩΝ
'2166': ΙΧΘΥΟΤΡΟΦΕΙΑ
'2167': ΑΠΟΓΡΑΦΕΣ Π. ΝΑΥΤΙΚΟΥ
'2168': ΣΗΜΑΤΑ ΚΑΙ ΔΕΛΤΙΑ ΑΝΑΠΗΡΩΝ ΠΟΛΕΜΟΥ
'2169': ΠΕΙΘΑΡΧΙΚΟ ΔΙΚΑΙΟ ΑΣΤΥΝΟΜΙΚΟΥ ΠΡΟΣΩΠΙΚΟΥ ΕΛΛΗΝΙΚΗΣ ΑΣΤΥΝΟΜΙΑΣ
'2170': ΑΤΜΟΛΕΒΗΤΕΣ
'2171': ΤΑΧΥΔΡΟΜΙΚΗ ΥΠΗΡΕΣΙΑ ΣΤΡΑΤΟΥ
'2172': ΠΡΟΣΤΑΣΙΑ ΠΙΝΑΚΙΔΩΝ
'2173': ΑΓΡΟΤΙΚΑ ΚΤΗΝΙΑΤΡΕΙΑ
'2174': ΧΡΗΜΑΤΙΣΤΗΡΙΑΚΑ ΔΙΚΑΣΤΗΡΙΑ
'2175': ΕΓΓΡΑΦΗ ΠΡΟΕΡΧΟΜΕΝΩΝ ΑΠΟ ΤΗΝ ΑΛΛΟΔΑΠΗ
'2176': ΟΡΓΑΝΙΣΜΟΣ ΔΙΑΧΕΙΡΙΣΗΣ ΔΗΜΟΣΙΟΥ ΥΛΙΚΟΥ
'2177': ΠΑΝΕΠΙΣΤΗΜΙΟ ΚΥΠΡΟΥ
'2178': ΚΑΤΕΡΓΑΣΙΑ ΞΗΡΑΣ ΣΤΑΦΙΔΑΣ
'2179': ΤΕΛΩΝΕΙΑΚΗ ΔΙΑΙΡΕΣΗ
'2180': ΑΖΗΤΗΤΑ
'2181': ΜΕΛΙΣΣΟΤΡΟΦΙΑ
'2182': ΔΙΕΥΘΥΝΣΗ ΘΑΛΑΣΣΙΩΝ ΚΡΑΤΙΚΩΝ ΜΕΤΑΦΟΡΩΝ
'2183': ΕΚΜΕΤΑΛΛΕΥΣΗ ΜΕΤΑΛΛΕΙΩΝ ΜΕ ΕΓΓΥΗΣΗ
'2184': ΙΔΙΩΤΙΚΕΣ ΕΠΑΓΓΕΛΜΑΤΙΚΕΣ ΣΧΟΛΕΣ
'2185': ΔΙΑΘΕΣΗ ΑΧΡΗΣΤΟΥ ΥΛΙΚΟΥ
'2186': ΤΑΧΥΔΡΟΜΙΚΕΣ ΜΕΤΑΦΟΡΕΣ
'2187': ΕΡΥΘΡΟ ΠΙΠΕΡΙ
'2188': ΠΙΚΠΑ-ΕΟΠ-ΚΕΝΤΡΟ ΒΡΕΦΩΝ Η ΜΗΤΕΡΑ-ΕΛΕΠΑΠ
'2189': ΣΥΜΜΕΤΟΧΗ ΣΕ ΣΥΜΒΟΥΛΙΑ
'2190': ΓΥΜΝΑΣΤΗΡΙΟ
'2191': ΙΑΤΡΙΚΟΙ- ΟΔΟΝΤΙΑΤΡΙΚΟΙ ΣΥΛΛΟΓΟΙ
'2192': ΕΙΣΑΓΩΓΗ ΦΟΙΤΗΤΩΝ
'2193': ΕΛΛΗΝΙΚΟ ΄ΙΔΡΥΜΑ ΠΟΛΙΤΙΣΜΟΥ
'2194': ΛΟΙΜΟΚΑΘΑΡΤΗΡΙΑ ΖΩΩΝ
'2195': ΔΙΕΘΝΗΣ ΟΡΓΑΝΙΣΜΟΣ ΑΤΟΜΙΚΗΣ ΕΝΕΡΓΕΙΑΣ
'2196': ΤΑΜΕΙΟ ΕΞΟΔΟΥ ΚΑΙ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΜΙΣΘΩΤΩΝ ΒΙΟΜΗΧΑΝΙΑΣ ΚΑΠΝΟΥ
'2197': ΚΑΘΗΓΗΤΕΣ Ε.Μ.Π
'2198': ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ
'2199': ΒΕΒΑΙΩΣΗ ΦΟΡΟΛΟΓΙΑΣ ΚΑΘΑΡΑΣ ΠΡΟΣΟΔΟΥ
'2200': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΤΡΑΠΕΖΩΝ ΕΛΛΑΔΟΣ ΚΑΙ ΚΤΗΜΑΤΙΚΗΣ
'2201': ΔΗΜΟΨΗΦΙΣΜΑΤΑ
'2202': ΕΛΛΗΝΙΚΟ ΑΝΟΙΚΤΟ ΠΑΝΕΠΙΣΤΗΜΙΟ
'2203': ΚΑΛΛΙΤΕΧΝΙΚΟ ΕΠΑΓΓΕΛΜΑΤΙΚΟ ΕΠΙΜΕΛΗΤΗΡΙΟ
'2204': ΑΝΟΙΚΟΔΟΜΗΣΙΣ
'2205': ΔΑΣΙΚΟΣ ΚΩΔΙΚΑΣ
'2206': ΚΑΝΟΝΙΣΜΟΣ ΠΥΡΟΣΒΕΣΤΙΚΩΝ ΜΕΣΩΝ ΤΩΝ ΠΛΟΙΩΝ
'2207': ΔΙΦΘΕΡΙΤΙΔΑ
'2208': ΒΙΒΛΙΑ ΚΑΙ ΦΟΡΟΛΟΓΙΚΑ ΣΤΟΙΧΕΙΑ
'2209': ΕΛΕΓΧΟΣ ΕΞΑΓΟΜΕΝΩΝ ΕΛΑΙΩΝ
'2210': ΕΠΙΔΟΜΑΤΑ ΟΙΚΟΓΕΝΕΙΩΝ ΣΤΡΑΤΙΩΤΙΚΩΝ
'2211': ΕΥΡΩΠΑΙΚΕΣ ΣΥΜΦΩΝΙΕΣ ΠΟΥ ΑΦΟΡΟΥΝ ΤΗΝ ΤΗΛΕΟΡΑΣΗ
'2212': ΕΚΤΑΚΤΑ ΣΤΡΑΤΟΔΙΚΕΙΑ
'2213': ΠΟΛΕΜΙΚΗ ΒΙΟΜΗΧΑΝΙΑ
'2214': ΑΣΕΜΝΟΙ ΓΥΝΑΙΚΕΣ
'2215': ΑΠΕΛΕΥΘΕΡΩΣΗ ΑΓΟΡΑΣ ΗΛΕΚΤΡΙΚΗΣ ΕΝΕΡΓΕΙΑΣ ΕΝΕΡΓΕΙΑΚΗ ΠΟΛΙΤΙΚΗ Ρ.Α.Ε
'2216': ΠΡΟΕΙΣΠΡΑΞΗ ΔΙΚΗΓΟΡΙΚΗΣ ΑΜΟΙΒΗΣ
'2217': ΕΘΝΙΚΗ ΣΧΟΛΗ ΔΗΜΟΣΙΑΣ ΥΓΕΙΑΣ (Ε.Σ.Δ.Υ.)
'2218': ΠΡΟΜΗΘΕΙΑ ΘΕΙΟΥ ΚΑΙ ΘΕΙΙΚΟΥ ΧΑΛΚΟΥ
'2219': ΧΗΜΙΚΟΙ - ΧΗΜΙΚΕΣ ΒΙΟΜΗΧΑΝΙΕΣ
'2220': ΑΣΦΑΛΙΣΗ ΚΑΤΑ ΤΗΣ ΑΣΘΕΝΕΙΑΣ
'2221': ΤΑΜΕΙΟ ΑΛΛΗΛΟΒΟΗΘΕΙΑΣ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΟΥ ΤΥΠΟΓΡΑΦΕΙΟΥ (Τ.Α.Π.Ε.Τ.)
'2222': ΟΡΓΑΝΙΣΜΟΣ ΥΠΟΥΡΓΕΙΟΥ ΟΙΚΟΝΟΜΙΚΩΝ
'2223': ΠΕΡΙΕΧΟΜΕΝΟ ΔΗΛΩΣΗΣ ΦΟΡΟΥ ΕΙΣΟΔΗΜΑΤΟΣ
'2224': ΠΡΩΤΕΣ ΥΛΕΣ ΣΙΔΕΡΕΝΙΩΝ ΒΑΡΕΛΙΩΝ
'2225': ΕΥΡΩΠΑΙΚΟΣ ΚΩΔΙΚΑΣ ΚΟΙΝΩΝΙΚΗΣ ΑΣΦΑΛΕΙΑΣ
'2226': ΔΙΑΦΟΡΟΙ ΓΕΩΡΓΙΚΟΙ ΣΥΝΕΤΑΙΡΙΣΜΟΙ
'2227': ΣΧΕΔΙΑ ΠΟΛΕΩΝ ΙΟΝΙΩΝ ΝΗΣΩΝ
'2228': ΕΥΡΩΠΑΙΚΗ ΟΙΚΟΝΟΜΙΚΗ ΚΟΙΝΟΤΗΤΑ ΕΥΡΩΠΑΙΚΗ ΕΝΩΣΗ
'2229': ΣΧΟΛΗ ΔΙΟΙΚΗΣΕΩΣ ΝΟΣΗΛΕΥΤ. ΙΔΡΥΜΑΤΩΝ
'2230': ΔΙΑΦΟΡΟΙ ΝΟΜΟΙ ΕΜΠΡΑΓΜΑΤΟΥ ΔΙΚΑΙΟΥ
'2231': ΕΠΙΜΕΛΗΤΕΙΑ ΚΑΙ ΟΙΚΟΝΟΜΙΚΕΣ ΥΠΗΡΕΣΙΕΣ
'2232': ΔΙΑΔΙΚΑΣΙΑ ΑΤΕΛΕΙΑΣ
'2233': ΠΑΙΔΙΚΕΣ ΕΞΟΧΕΣ
'2234': ΤΑΜΕΙΟ ΣΥΝΤΑΞΕΩΝ ΠΡΟΣΩΠΙΚΟΥ ΕΘΝΙΚΗΣ ΤΡΑΠΕΖΑΣ ΤΗΣ ΕΛΛΑΔΟΣ
'2235': ΚΡΑΤΙΚΗ ΕΚΜΕΤΑΛΛΕΥΣΗ ΔΑΣΩΝ
'2236': ΑΝΕΞΑΡΤΗΣΙΑ ΤΗΣ ΕΚΚΛΗΣΙΑΣ ΤΗΣ ΕΛΛΑΔΟΣ
'2237': ΤΕΧΝΙΚΑ ΠΤΥΧΙΑ
'2238': ΕΠΙΒΑΤΙΚΑ ΑΥΤΟΚΙΝΗΤΑ (ΔΗΜΟΣΙΑΣ ΚΑΙ ΙΔΙΩΤΙΚΗΣ ΧΡΗΣΗΣ)
'2239': ΣΥΜΒΑΣΕΙΣ ΒΟΥΛΕΥΤΩΝ
'2240': ΟΡΓΑΝΙΣΜΟΣ ΤΩΝ ΔΙΚΑΣΤΗΡΙΩΝ
'2241': ΕΚΠΑΙΔΕΥΤΙΚΟΙ ΛΕΙΤΟΥΡΓΟΙ ΕΝ ΓΕΝΕΙ
'2242': ΑΡΜΟΔΙΟΤΗΤΑ ΤΕΛΩΝΕΙΑΚΩΝ ΑΡΧΩΝ
'2243': ΕΙΔΙΚΑ ΕΦΕΤΕΙΑ
'2244': ΑΞΙΩΜΑΤΙΚΟΙ ΑΕΡΟΠΟΡΙΑΣ
'2245': ΠΑΝΕΠΙΣΤΗΜΙΑΚΗ ΒΙΒΛΙΟΘΗΚΗ
'2246': ΕΠΙΤΡΟΠΗ ΣΥΝΤΑΞΗΣ ΣΧΕΔΙΟΥ ΚΩΔΙΚΑ ΕΡΓΑΣΙΑΣ
'2247': ΕΛΟΝΟΣΙΑ
'2248': ΝΑΥΛΟΣΥΜΦΩΝΑ
'2249': ΣΙΔΗΡΟΔΡΟΜΟΙ ΘΕΣΣΑΛΙΚΟΙ
'2250': ΡΑΔΙΟΦΩΝΙΚΕΣ ΣΥΜΒΑΣΕΙΣ
'2251': ΠΡΟΩΘΗΣΗ ΓΕΩΡΓΙΚΗΣ ΠΑΡΑΓΩΓΗΣ-ΕΘ.Ι.ΑΓ.Ε
'2252': ΕΠΟΧΙΑΚΩΣ ΕΡΓΑΖΟΜΕΝΟΙ ΜΙΣΘΩΤΟΙ
'2253': ΔΙΔΑΚΤΙΚΟ ΠΡΟΣΩΠΙΚΟ
'2254': ΚΩΔΙΚΑΣ ΚΕΝΤΡΙΚΗΣ, ΠΡΕΣΒΕΥΤΙΚΗΣ ΚΑΙ
'2255': ΠΟΛΙΤΙΚΟ ΠΡΟΣΩΠΙΚΟ ΥΠΟΥΡΓΕΙΟΥ ΕΘΝΙΚΗΣ ΑΜΥΝΑΣ
'2256': ΔΙΠΛΩΜΑΤΑ ΕΥΡΕΣΙΤΕΧΝΙΑΣ
'2257': ΣΩΜΑΤΕΙΑ ΓΕΩΡΓΙΚΩΝ ΕΡΓΑΤΩΝ
'2258': ΚΩΔΙΚΑΣ ΠΕΡΙ ΕΙΣΠΡΑΞΕΩΣ ΔΗΜΟΣΙΩΝ ΕΣΟΔΩΝ
'2259': ΤΡΑΠΕΖΟΓΡΑΜΜΑΤΙΑ
'2260': ΠΡΟΜΗΘΕΥΤΙΚΟΣ ΟΡΓΑΝΙΣΜΟΣ Ε.Β.Α
'2261': ΕΛΕΓΧΟΣ ΑΣΦΑΛΕΙΑΣ ΑΥΤΟΚΙΝΗΤΩΝΚΕΝΤΡΑ ΤΕΧΝΙΚΟΥ ΕΛΕΓΧΟΥ ΟΧΗΜΑΤΩΝ (Κ.Τ.Ε.Ο.)
'2262': ΕΞΑΓΩΓΗ ΤΥΡΟΥ
'2263': ΝΑΥΤΙΛΙΑΚΟ ΣΥΝΑΛΛΑΓΜΑ
'2264': ΤΑΜΕΙΟ ΕΠΙΚΟΥΡΙΚΗΣ ΑΣΦΑΛΙΣΕΩΣ ΗΛΕΤΡΟΤΕΧΝΙΤΩΝ ΕΛΛΑΔΟΣ (T.E.A.H.E.)
'2265': ΜΙΣΘΟΙ ΣΤΡΑΤΙΩΤΙΚΩΝ ΚΑΙ ΠΡΟΣΑΥΞΗΣΕΙΣ
'2266': ΑΣΤΙΚΟΣ ΚΩΔΙΚΑΣ
'2267': ΜΕ ΤΙΣ ΗΝΩΜΕΝΕΣ ΠΟΛΙΤΕΙΕΣ ΑΜΕΡΙΚΗΣ
'2268': ΤΑΜΕΙΟ ΑΣΦΑΛΙΣΕΩΣ ΠΡΟΣΩΠΙΚΟΥ Ο.Τ.Ε. (Τ.Α.Π.-Ο.Τ.Ε.)
'2269': ΜΑΙΕΣ
'2270': ΦΥΓΟΔΙΚΙΑ
'2271': ΟΡΓΑΝΙΣΜΟΣ ΞΕΝΟΔΟΧΕΙΑΚΗΣ ΠΙΣΤΗΣ
'2272': ΔΗΜΟΤΙΚΟΙ ΣΤΡΑΤΟΛΟΓΟΙ
'2273': ΑΝΩΤΑΤΟ ΔΙΚΑΣΤΙΚΟ ΣΥΜΒΟΥΛΙΟ
'2274': ΙΣΤΟΡΙΚΟ ΑΡΧΕΙΟ ΚΡΗΤΗΣ
'2275': ΕΛΛΗΝΙΚΗ ΘΑΛΑΣΣΙΑ ΄ΕΝΩΣΗ
'2276': ΕΚΠΟΙΗΣΕΙΣ ΚΑΙ ΕΚΜΙΣΘΩΣΕΙΣ
'2277': ΤΑΧΥΔΡΟΜΙΚΕΣ ΕΠΙΤΑΓΕΣ
'2278': ΥΠΗΡΕΣΙΑ ΜΗΤΡΩΟΥ
'2279': ΔΙΑΦΟΡΑ ΟΙΚΟΝΟΜΙΚΑ ΘΕΜΑΤΑ
'2280': ΕΝΔΙΚΑ ΜΕΣΑ
'2281': ΤΕΛΗ ΑΕΡΟΠΟΡΙΚΩΝ ΤΑΞΙΔΙΩΝ
'2282': ΜΕ ΤΗΝ ΑΙΓΥΠΤΟ
'2283': ΔΙΑΦΟΡΕΣ ΒΙΒΛΙΟΘΗΚΕΣ
'2284': ΚΕΝΤΡΙΚΗ ΥΠΗΡΕΣΙΑ
splits:
- name: train
num_bytes: 216757887
num_examples: 28536
- name: test
num_bytes: 71533786
num_examples: 9516
- name: validation
num_bytes: 68824457
num_examples: 9511
download_size: 45606292
dataset_size: 357116130
---
# Dataset Card for Greek Legal Code
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/christospi/glc-nllp-21
- **Paper:** https://arxiv.org/abs/2109.15298
- **Data:** https://doi.org/10.5281/zenodo.5528002
- **Leaderboard:** N/A
- **Point of Contact:** [Christos Papaloukas](mailto:christospap@di.uoa.gr)
### Dataset Summary
Greek_Legal_Code (GLC) is a dataset consisting of approx. 47k legal resources from Greek legislation. The origin of GLC is “Permanent Greek Legislation Code - Raptarchis”, a collection of Greek legislative documents classified into multi-level (from broader to more specialized) categories.
**Topics**
GLC consists of 47 legislative volumes and each volume corresponds to a main thematic topic. Each volume is divided into thematic sub categories which are called chapters and subsequently, each chapter breaks down to subjects which contain the legal resources. The total number of chapters is 389 while the total number of subjects is 2285, creating an interlinked thematic hierarchy. So, for the upper thematic level (volume) GLC has 47 classes. For the next thematic level (chapter) GLC offers 389 classes and for the inner and last thematic level (subject), GLC has 2285 classes.
GLC classes are divided into three categories for each thematic level: frequent classes, which occur in more than 10 training documents and can be found in all three subsets (training, development and test); few-shot classes which appear in 1 to 10 training documents and also appear in the documents of the development and test sets, and zero-shot classes which appear in the development and/or test, but not in the training documents.
### Supported Tasks and Leaderboards
The dataset supports:
**Multi-class Text Classification:** Given the text of a document, a model predicts the corresponding class.
**Few-shot and Zero-shot learning:** As already noted, the classes can be divided into three groups: frequent, few-shot, and zero- shot, depending on whether they were assigned to more than 10, fewer than 10 but at least one, or no training documents, respectively.
| Level | Total | Frequent | Few-Shot (<10) | Zero-Shot |
|---|---|---|---|---|
|Volume|47|47|0|0|
|Chapter|389|333|53|3|
|Subject|2285|712|1431|142|
### Languages
All documents are written in Greek.
## Dataset Structure
### Data Instances
```json
{
"text": "179. ΑΠΟΦΑΣΗ ΥΠΟΥΡΓΟΥ ΜΕΤΑΦΟΡΩΝ ΚΑΙ ΕΠΙΚΟΙΝΩΝΙΩΝ Αριθ. Β-οικ. 68425/4765 της 2/17 Νοεμ. 2000 (ΦΕΚ Β΄ 1404) Τροποποίηση της 42000/2030/81 κοιν. απόφασης του Υπουργού Συγκοινωνιών «Κωδικοποίηση και συμπλήρωση καν. Αποφάσεων» που εκδόθηκαν κατ’ εξουσιοδότηση του Ν.Δ. 102/73 «περί οργανώσεως των δια λεωφορείων αυτοκινήτων εκτελουμένων επιβατικών συγκοινωνιών». ",
"volume": 24, # "ΣΥΓΚΟΙΝΩΝΙΕΣ"
}
```
### Data Fields
The following data fields are provided for documents (`train`, `dev`, `test`):
`text`: (**str**) The full content of each document, which is represented by its `header` and `articles` (i.e., the `main_body`).\
`label`: (**class label**): Depending on the configurarion, the volume/chapter/subject of the document. For volume-level class it belongs to specifically: ["ΚΟΙΝΩΝΙΚΗ ΠΡΟΝΟΙΑ",
"ΓΕΩΡΓΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΡΑΔΙΟΦΩΝΙΑ ΚΑΙ ΤΥΠΟΣ",
"ΒΙΟΜΗΧΑΝΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΥΓΕΙΟΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΠΟΛΕΜΙΚΟ ΝΑΥΤΙΚΟ",
"ΤΑΧΥΔΡΟΜΕΙΑ - ΤΗΛΕΠΙΚΟΙΝΩΝΙΕΣ",
"ΔΑΣΗ ΚΑΙ ΚΤΗΝΟΤΡΟΦΙΑ",
"ΕΛΕΓΚΤΙΚΟ ΣΥΝΕΔΡΙΟ ΚΑΙ ΣΥΝΤΑΞΕΙΣ",
"ΠΟΛΕΜΙΚΗ ΑΕΡΟΠΟΡΙΑ",
"ΝΟΜΙΚΑ ΠΡΟΣΩΠΑ ΔΗΜΟΣΙΟΥ ΔΙΚΑΙΟΥ",
"ΝΟΜΟΘΕΣΙΑ ΑΝΩΝΥΜΩΝ ΕΤΑΙΡΕΙΩΝ ΤΡΑΠΕΖΩΝ ΚΑΙ ΧΡΗΜΑΤΙΣΤΗΡΙΩΝ",
"ΠΟΛΙΤΙΚΗ ΑΕΡΟΠΟΡΙΑ",
"ΕΜΜΕΣΗ ΦΟΡΟΛΟΓΙΑ",
"ΚΟΙΝΩΝΙΚΕΣ ΑΣΦΑΛΙΣΕΙΣ",
"ΝΟΜΟΘΕΣΙΑ ΔΗΜΩΝ ΚΑΙ ΚΟΙΝΟΤΗΤΩΝ",
"ΝΟΜΟΘΕΣΙΑ ΕΠΙΜΕΛΗΤΗΡΙΩΝ ΣΥΝΕΤΑΙΡΙΣΜΩΝ ΚΑΙ ΣΩΜΑΤΕΙΩΝ",
"ΔΗΜΟΣΙΑ ΕΡΓΑ",
"ΔΙΟΙΚΗΣΗ ΔΙΚΑΙΟΣΥΝΗΣ",
"ΑΣΦΑΛΙΣΤΙΚΑ ΤΑΜΕΙΑ",
"ΕΚΚΛΗΣΙΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΕΚΠΑΙΔΕΥΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΔΗΜΟΣΙΟ ΛΟΓΙΣΤΙΚΟ",
"ΤΕΛΩΝΕΙΑΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΣΥΓΚΟΙΝΩΝΙΕΣ",
"ΕΘΝΙΚΗ ΑΜΥΝΑ",
"ΣΤΡΑΤΟΣ ΞΗΡΑΣ",
"ΑΓΟΡΑΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΔΗΜΟΣΙΟΙ ΥΠΑΛΛΗΛΟΙ",
"ΠΕΡΙΟΥΣΙΑ ΔΗΜΟΣΙΟΥ ΚΑΙ ΝΟΜΙΣΜΑ",
"ΟΙΚΟΝΟΜΙΚΗ ΔΙΟΙΚΗΣΗ",
"ΛΙΜΕΝΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΑΣΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΠΟΛΙΤΙΚΗ ΔΙΚΟΝΟΜΙΑ",
"ΔΙΠΛΩΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΔΙΟΙΚΗΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΑΜΕΣΗ ΦΟΡΟΛΟΓΙΑ",
"ΤΥΠΟΣ ΚΑΙ ΤΟΥΡΙΣΜΟΣ",
"ΕΘΝΙΚΗ ΟΙΚΟΝΟΜΙΑ",
"ΑΣΤΥΝΟΜΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΑΓΡΟΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΕΡΓΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΠΟΙΝΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΕΜΠΟΡΙΚΗ ΝΟΜΟΘΕΣΙΑ",
"ΕΠΙΣΤΗΜΕΣ ΚΑΙ ΤΕΧΝΕΣ",
"ΕΜΠΟΡΙΚΗ ΝΑΥΤΙΛΙΑ",
"ΣΥΝΤΑΓΜΑΤΙΚΗ ΝΟΜΟΘΕΣΙΑ"
] \
The labels can also be a the chapter-level or subject-level class it belongs to. Some chapter labels are omitted due to size (389 classes). Some subject labels are also omitted due to size (2285 classes).
### Data Splits
| Split | No of Documents | Avg. words |
| ------------------- | ------------------------------------ | --- |
| Train | 28,536 | 600 |
|Development | 9,511 | 574 |
|Test | 9,516 | 595 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Papaloukas et al. (2021) with the hope to support and encourage further research in NLP for the Greek language.
### Source Data
#### Initial Data Collection and Normalization
The ``Permanent Greek Legislation Code - Raptarchis`` is a thorough catalogue of Greek legislation since the creation of the Greek state in 1834 until 2015. It includes Laws, Royal and Presidential Decrees, Regulations and Decisions, retrieved from the Official Government Gazette, where Greek legislation is published. This collection is one of the official, publicly available sources of classified Greek legislation suitable for classification tasks.
Currently, the original catalogue is publicly offered in MS Word (.doc) format through the portal e-Themis, the legal database and management service of it, under the administration of the Ministry of the Interior (Affairs). E-Themis is primarily focused on providing legislation on a multitude of predefined thematic categories, as described in the catalogue. The main goal is to help users find legislation of interest using the thematic index.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Papaloukas et al. (2021)
### Licensing Information
[More Information Needed]
### Citation Information
*Christos Papaloukas, Ilias Chalkidis, Konstantinos Athinaios, Despina-Athanasia Pantazi and Manolis Koubarakis.*
*Multi-granular Legal Topic Classification on Greek Legislation.*
*Proceedings of the 3rd Natural Legal Language Processing (NLLP) Workshop, Punta Cana, Dominican Republic, 2021*
```
@inproceedings{papaloukas-etal-2021-glc,
title = "Multi-granular Legal Topic Classification on Greek Legislation",
author = "Papaloukas, Christos and Chalkidis, Ilias and Athinaios, Konstantinos and Pantazi, Despina-Athanasia and Koubarakis, Manolis",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2109.15298",
doi = "10.48550/arXiv.2109.15298",
pages = "63--75"
}
```
### Contributions
Thanks to [@christospi](https://github.com/christospi) for adding this dataset. | 135,347 | [
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so_stacksample | 2022-11-03T16:30:57.000Z | [
"task_categories:text2text-generation",
"task_ids:abstractive-qa",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answers contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table.
Tags contains the tags on each of these questions. | null | 3 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- abstractive-qa
- open-domain-abstractive-qa
paperswithcode_id: null
pretty_name: SO StackSample
dataset_info:
- config_name: Answers
features:
- name: Id
dtype: int32
- name: OwnerUserId
dtype: int32
- name: CreationDate
dtype: string
- name: ParentId
dtype: int32
- name: Score
dtype: int32
- name: Body
dtype: string
splits:
- name: Answers
num_bytes: 1583232304
num_examples: 2014516
download_size: 0
dataset_size: 1583232304
- config_name: Questions
features:
- name: Id
dtype: int32
- name: OwnerUserId
dtype: int32
- name: CreationDate
dtype: string
- name: ClosedDate
dtype: string
- name: Score
dtype: int32
- name: Title
dtype: string
- name: Body
dtype: string
splits:
- name: Questions
num_bytes: 1913896893
num_examples: 1264216
download_size: 0
dataset_size: 1913896893
- config_name: Tags
features:
- name: Id
dtype: int32
- name: Tag
dtype: string
splits:
- name: Tags
num_bytes: 58816824
num_examples: 3750994
download_size: 0
dataset_size: 58816824
---
# Dataset Card for SO StackSample
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/stackoverflow/stacksample
### Dataset Summary
Dataset with the text of 10% of questions and answers from the Stack Overflow programming Q&A website.
This is organized as three tables:
Questions table contains the title, body, creation date, closed date (if applicable), score, and owner ID for all non-deleted Stack Overflow questions whose Id is a multiple of 10.
Answers table contains the body, creation date, score, and owner ID for each of the answers to these questions. The ParentId column links back to the Questions table.
Tags table contains the tags on each of these questions.
### Supported Tasks and Leaderboards
Example projects include:
- Identifying tags from question text
- Predicting whether questions will be upvoted, downvoted, or closed based on their text
- Predicting how long questions will take to answer
- Open Domain Q/A
### Languages
English (en) and Programming Languages.
## Dataset Structure
### Data Instances
For Answers:
```
{
"Id": { # Unique ID given to the Answer post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Answer was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ParentId": { # Refers to the `Id` of the Question the Answer belong to.
"feature_type": "Value",
"dtype": "int32"
},
"Score": { # The sum of up and down votes given to the Answer. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Body": { # The body content of the Answer.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Questions:
```
{
"Id": { # Unique ID given to the Question post
"feature_type": "Value",
"dtype": "int32"
},
"OwnerUserId": { # The UserID of the person who generated the Question on StackOverflow. -1 means NA.
"feature_type": "Value",
"dtype": "int32"
},
"CreationDate": { # The date the Question was generated. Follows standard datetime format.
"feature_type": "Value",
"dtype": "string"
},
"ClosedDate": { # The date the Question was generated. Follows standard datetime format. Can be NA.
"feature_type": "Value",
"dtype": "string"
},
"Score": { # The sum of up and down votes given to the Question. Can be negative.
"feature_type": "Value",
"dtype": "int32"
},
"Title": { # The title of the Question.
"feature_type": "Value",
"dtype": "string"
},
"Body": { # The body content of the Question.
"feature_type": "Value",
"dtype": "string"
}
}
```
For Tags:
```
{
"Id": { # ID of the Question the tag belongs to
"feature_type": "Value",
"dtype": "int32"
},
"Tag": { # The tag name
"feature_type": "Value",
"dtype": "string"
}
}
```
`
### Data Fields
For Answers:
-`Id`: Unique ID given to the Answer post
`OwnerUserId`: The UserID of the person who generated the Answer on StackOverflow. -1 means NA
"`CreationDate`": The date the Answer was generated. Follows standard datetime format.
"`ParentId`": Refers to the `Id` of the Question the Answer belong to.
"`Score`": The sum of up and down votes given to the Answer. Can be negative.
"`Body`": The body content of the Answer.
For Questions:
- `Id`: Unique ID given to the Question post.
- `OwnerUserId`: The UserID of the person who generated the Question on StackOverflow. -1 means NA.
- `CreationDate`: The date the Question was generated. Follows standard datetime format.
- `ClosedDate`: The date the Question was generated. Follows standard datetime format. Can be NA.
- `Score`: The sum of up and down votes given to the Question. Can be negative.
- `Title`: {The title of the Question.
- `Body`: The body content of the Question.
For Tags:
- `Id`: ID of the Question the tag belongs to.
- `Tag`: The tag name.
### Data Splits
The dataset has 3 splits:
- `Answers`
- `Questions`
- `Tags`
## Dataset Creation
### Curation Rationale
Datasets of all R questions and all Python questions are also available on Kaggle, but this dataset is especially useful for analyses that span many languages.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
StackOverflow Users.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
This data contains information that can identify individual users of StackOverflow. The information is self-reported.
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
StackOverflow answers are not guaranteed to be safe, secure, or correct. Some answers may purposefully be insecure as is done in this https://stackoverflow.com/a/35571883/5768407 answer from user [`zys`](https://stackoverflow.com/users/5259310/zys), where they show a solution to purposefully bypass Google Play store security checks. Such answers can lead to biased models that use this data and can further propogate unsafe and insecure programming practices.
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
### Citation Information
The content is from Stack Overflow.
### Contributions
Thanks to [@ncoop57](https://github.com/ncoop57) for adding this dataset. | 8,236 | [
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] |
thaisum | 2022-11-18T21:51:46.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:th",
"license:mit",
"region:us"
] | null | ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath,
ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs
written by journalists. | @mastersthesis{chumpolsathien_2020,
title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization},
author={Chumpolsathien, Nakhun},
year={2020},
school={Beijing Institute of Technology} | 7 | 227 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: ThaiSum
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
- name: summary
dtype: string
- name: type
dtype: string
- name: tags
dtype: string
- name: url
dtype: string
config_name: thaisum
splits:
- name: train
num_bytes: 2945472406
num_examples: 358868
- name: validation
num_bytes: 118437310
num_examples: 11000
- name: test
num_bytes: 119496704
num_examples: 11000
download_size: 647582078
dataset_size: 3183406420
---
# Dataset Card for ThaiSum
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/nakhunchumpolsathien/ThaiSum
- **Repository:** https://github.com/nakhunchumpolsathien/ThaiSum
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/nakhunchumpolsathien
### Dataset Summary
ThaiSum is a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard. This dataset consists of over 350,000 article and summary pairs written by journalists.
### Supported Tasks and Leaderboards
summarization, language modeling
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'body': 'กีเก ซานเชซ ฟลอเรส\xa0 กุนซือเลือดกระทิงของทีมวัตฟอร์ด\xa0 เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง,สำนักข่าวต่างประเทศรายงานวันที่ 27 ก.ย. ว่า กีเก ซานเชซ ฟลอเรส\xa0 ผู้จัดการทีมชาวสเปน ของ แตนอาละวาด วัตฟอร์ด\xa0 ยอมรับทีมของเขาเล่นได้ไม่ดีพอเอง ในเกมพรีเมียร์ลีก อังกฤษ นัดเปิดบ้านพ่าย อินทรีผงาด คริสตัล พาเลซ 0-1 เมื่อคืนวันอาทิตย์ที่ผ่านมา,เกมนี้จุดเปลี่ยนมาอยู่ที่การได้จุดโทษในช่วงครึ่งหลังของ คริสตัล พาเลซ ซึ่งไม่ค่อยชัดเจนเท่าไหร่ว่า อัลลัน นียอม นั้นไปทำฟาล์วใส่ วิลฟรีด ซาฮา ในเขตโทษหรือไม่ แต่ผู้ตัดสินก็ชี้เป็นจุดโทษ ซึ่ง โยอัน กาบาย สังหารไม่พลาด และเป็นประตูชัยช่วยให้ คริสตัล พาเลซ เอาชนะ วัตฟอร์ด ไป 1-0 และเป็นการพ่ายแพ้ในบ้านนัดแรกของวัตฟอร์ดในฤดูกาลนี้อีกด้วย,ฟลอเรส กล่าวว่า มันเป็นเรื่องยากในการหยุดเกมรุกของคริสตัล พาเลซ ซึ่งมันอึดอัดจริงๆสำหรับเรา เราเล่นกันได้ไม่ดีนักในตอนที่ได้ครองบอล เราต้องเล่นทางริมเส้นให้มากกว่านี้ เราไม่สามารถหยุดเกมสวนกลับของพวกเขาได้ และแนวรับของเราก็ยืนไม่เป็นระเบียบสักเท่าไหร่ในช่วงครึ่งแรก ส่วนเรื่องจุดโทษการตัดสินใจขั้นสุดท้ายมันอยู่ที่ผู้ตัดสิน ซึ่งมันเป็นการตัดสินใจที่สำคัญ ผมเองก็ไม่รู้ว่าเขาตัดสินถูกหรือเปล่า บางทีมันอาจเป็นจุดที่ตัดสินเกมนี้เลย แต่เราไม่ได้แพ้เกมนี้เพราะจุดโทษ เราแพ้ในวันนี้เพราะเราเล่นไม่ดีและคริสตัล พาเลซ เล่นดีกว่าเรา เราไม่ได้มีฟอร์มการเล่นที่ดีในเกมนี้เลย', 'summary': 'กีเก ซานเชซ ฟลอเรส กุนซือเลือดกระทิงของทีมวัตฟอร์ด เมินประเด็นจุดโทษปัญหาในเกมพรีเมียร์ลีก อังกฤษ นัดที่แตนอาละวาดเปิดบ้านพ่าย คริสตัล พาเลซ 0-1ชี้ทีมของเขาเล่นไม่ดีพอเอง', 'tags': 'พรีเมียร์ลีก,วัตฟอร์ด,คริสตัล พาเลซ,กีเก ซานเชซ ฟลอเรส,ข่าวกีฬา,ข่าว,ไทยรัฐออนไลน์', 'title': 'ฟลอเรส รับ วัตฟอร์ดห่วยเองเกมพ่ายพาเลซคาบ้าน', 'type': '', 'url': 'https://www.thairath.co.th/content/528322'}
```
### Data Fields
- `title`: title of article
- `body`: body of article
- `summary`: summary of article
- `type`: type of article, if any
- `tags`: tags of article, separated by `,`
- `url`: URL of article
### Data Splits
train/valid/test: 358868 / 11000 / 11000
## Dataset Creation
### Curation Rationale
Sequence-to-sequence (Seq2Seq) models have shown great achievement in text summarization. However, Seq2Seq model often requires large-scale training data to achieve effective results. Although many impressive advancements in text summarization field have been made, most of summarization studies focus on resource-rich languages. The progress of Thai text summarization is still far behind. The dearth of large-scale dataset keeps Thai text summarization in its infancy. As far as our knowledge goes, there is not a large-scale dataset for Thai text summarization available anywhere. Thus, we present ThaiSum, a large-scale corpus for Thai text summarization obtained from several online news websites namely Thairath, ThaiPBS, Prachathai, and The Standard.
### Source Data
#### Initial Data Collection and Normalization
We used a python library named Scrapy to crawl articles from several news websites namely Thairath, Prachatai, ThaiPBS and, The Standard. We first collected news URLs provided in their sitemaps. During web-crawling, we used HTML markup and metadata available in HTML pages to identify article text, summary, headline, tags and label. Collected articles were published online from 2014 to August 2020. <br> <br>
We further performed data cleansing process to minimize noisy data. We filtered out articles that their article text or summary is missing. Articles that contains article text with less than 150 words or summary with less than 15 words were removed. We also discarded articles that contain at least one of these following tags: ‘ดวง’ (horoscope), ‘นิยาย’ (novel), ‘อินสตราแกรมดารา’ (celebrity Instagram), ‘คลิปสุดฮา’(funny video) and ‘สรุปข่าว’ (highlight news). Some summaries were completely irrelevant to their original article texts. To eliminate those irrelevant summaries, we calculated abstractedness score between summary and its article text. Abstractedness score is written formally as: <br>
<center><a href="https://www.codecogs.com/eqnedit.php?latex=\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\begin{equation}&space;\frac{|S-A|}{r}&space;\times&space;100&space;\end{equation}" title="\begin{equation} \frac{|S-A|}{r} \times 100 \end{equation}" /></a></center><br>
<br>Where 𝑆 denotes set of article tokens. 𝐴 denotes set of summary tokens. 𝑟 denotes a total number of summary tokens. We omitted articles that have abstractedness score at 1-grams higher than 60%.
<br><br>
It is important to point out that we used [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp), version 2.2.4, tokenizing engine = newmm, to process Thai texts in this study. It is challenging to tokenize running Thai text into words or sentences because there are not clear word/sentence delimiters in Thai language. Therefore, using different tokenization engines may result in different segment of words/sentences.
After data-cleansing process, ThaiSum dataset contains over 358,000 articles. The size of this dataset is comparable to a well-known English document summarization dataset, CNN/Dily mail dataset. Moreover, we analyse the characteristics of this dataset by measuring the abstractedness level, compassion rate, and content diversity. For more details, see [thaisum_exploration.ipynb](https://github.com/nakhunchumpolsathien/ThaiSum/blob/master/thaisum_exploration.ipynb).
#### Dataset Statistics
ThaiSum dataset consists of 358,868 articles. Average lengths of article texts and summaries are approximately 530 and 37 words respectively. As mentioned earlier, we also collected headlines, tags and labels provided in each article. Tags are similar to keywords of the article. An article normally contains several tags but a few labels. Tags can be name of places or persons that article is about while labels indicate news category (politic, entertainment, etc.). Ultimatly, ThaiSum contains 538,059 unique tags and 59 unique labels. Note that not every article contains tags or labels.
|Dataset Size| 358,868 | articles |
|:---|---:|---:|
|Avg. Article Length| 529.5 | words|
|Avg. Summary Length | 37.3 | words|
|Avg. Headline Length | 12.6 | words|
|Unique Vocabulary Size | 407,355 | words|
|Occurring > 10 times | 81,761 | words|
|Unique News Tag Size | 538,059 | tags|
|Unique News Label Size | 59 | labels|
#### Who are the source language producers?
Journalists of respective articles
### Annotations
#### Annotation process
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
#### Who are the annotators?
`summary`, `type` and `tags` are created by journalists who wrote the articles and/or their publishers.
### Personal and Sensitive Information
All data are public news articles. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- News summarization in Thai
- Language modeling for Thai news
### Discussion of Biases
- [ThaiPBS](https://www.thaipbs.or.th/home) [receives funding from Thai government](https://www.bangkokbiznews.com/blog/detail/648740).
- [Thairath](https://www.thairath.co.th/) is known as [the most popular newspaper in Thailand](https://mgronline.com/onlinesection/detail/9620000058532); no clear political leaning.
- [The Standard](https://thestandard.co/) is a left-leaning online magazine.
- [Prachathai](https://prachatai.com/) is a left-leaning, human-right-focused news site.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[@nakhunchumpolsathien](https://github.com/nakhunchumpolsathien/)
[@caramelWaffle](https://github.com/caramelWaffle)
### Licensing Information
MIT License
### Citation Information
```
@mastersthesis{chumpolsathien_2020,
title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization},
author={Chumpolsathien, Nakhun},
year={2020},
school={Beijing Institute of Technology}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | 10,852 | [
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OamPatel/iti_trivia_qa_val | 2023-06-14T18:48:29.000Z | [
"region:us"
] | OamPatel | null | null | 1 | 227 | 2023-06-14T18:48:15 | Entry not found | 15 | [
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chr_en | 2023-06-01T14:59:50.000Z | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_categories:translation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
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"source_datasets:original",
"language:chr",
"language:en",
"license:other",
"arxiv:2010.04791",
"region:us"
] | null | ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning. | @inproceedings{zhang2020chren,
title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
booktitle={EMNLP2020},
year={2020}
} | 3 | 226 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- found
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language_creators:
- found
language:
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- en
license:
- other
multilinguality:
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size_categories:
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source_datasets:
- original
task_categories:
- fill-mask
- text-generation
- translation
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: chren
dataset_info:
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dtype: string
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num_examples: 256
download_size: 28899321
dataset_size: 3773771
config_names:
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- monolingual_raw
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- parallel_raw
---
# Dataset Card for ChrEn
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github repository for ChrEn](https://github.com/ZhangShiyue/ChrEn)
- **Paper:** [ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization](https://arxiv.org/abs/2010.04791)
- **Point of Contact:** [benfrey@email.unc.edu](benfrey@email.unc.edu)
### Dataset Summary
ChrEn is a Cherokee-English parallel dataset to facilitate machine translation research between Cherokee and English.
ChrEn is extremely low-resource contains 14k sentence pairs in total, split in ways that facilitate both in-domain and out-of-domain evaluation.
ChrEn also contains 5k Cherokee monolingual data to enable semi-supervised learning.
### Supported Tasks and Leaderboards
The dataset is intended to use for `machine-translation` between Enlish (`en`) and Cherokee (`chr`).
### Languages
The dataset contains Enlish (`en`) and Cherokee (`chr`) text. The data encompasses both existing dialects of Cherokee: the Overhill dialect, mostly spoken in Oklahoma (OK), and the Middle dialect, mostly used in North Carolina (NC).
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Many of the source texts were translations of English materials, which means that the Cherokee structures may not be 100% natural in terms of what a speaker might spontaneously produce. Each text was translated by people who speak Cherokee as the first language, which means there is a high probability of grammaticality. These data were originally available in PDF version. We apply the Optical Character Recognition (OCR) via Tesseract OCR engine to extract the Cherokee and English text.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The sentences were manually aligned by Dr. Benjamin Frey a proficient second-language speaker of Cherokee, who also fixed the errors introduced by OCR. This process is time-consuming and took several months.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was gathered and annotated by Shiyue Zhang, Benjamin Frey, and Mohit Bansal at UNC Chapel Hill.
### Licensing Information
The copyright of the data belongs to original book/article authors or translators (hence, used for research purpose; and please contact Dr. Benjamin Frey for other copyright questions).
### Citation Information
```
@inproceedings{zhang2020chren,
title={ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization},
author={Zhang, Shiyue and Frey, Benjamin and Bansal, Mohit},
booktitle={EMNLP2020},
year={2020}
}
```
### Contributions
Thanks to [@yjernite](https://github.com/yjernite), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 6,749 | [
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] |
HuggingFaceH4/cherry_picked_prompts | 2023-03-08T21:24:46.000Z | [
"license:apache-2.0",
"region:us"
] | HuggingFaceH4 | null | null | 1 | 226 | 2023-03-08T12:49:42 | ---
license: apache-2.0
---
# Dataset Card for Cherry Picked Prompts 🍒
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Lewis Tunstall
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 1,585 | [
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gutenberg_time | 2022-11-03T16:32:34.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:2011.04124",
"region:us"
] | null | A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg. | @misc{kim2020time,
title={What time is it? Temporal Analysis of Novels},
author={Allen Kim and Charuta Pethe and Steven Skiena},
year={2020},
eprint={2011.04124},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: gutenberg-time-dataset
pretty_name: the Gutenberg Time dataset
dataset_info:
features:
- name: guten_id
dtype: string
- name: hour_reference
dtype: string
- name: time_phrase
dtype: string
- name: is_ambiguous
dtype: bool_
- name: time_pos_start
dtype: int64
- name: time_pos_end
dtype: int64
- name: tok_context
dtype: string
config_name: gutenberg
splits:
- name: train
num_bytes: 108550391
num_examples: 120694
download_size: 35853781
dataset_size: 108550391
---
# Dataset Card for the Gutenberg Time dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Repository](https://github.com/allenkim/what-time-is-it)**
- **[Paper](https://arxiv.org/abs/2011.04124)**
### Dataset Summary
A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Time-of-the-day classification from excerpts.
## Dataset Structure
### Data Instances
```
{
"guten_id": 28999,
"hour_reference": 12,
"time_phrase": "midday",
"is_ambiguous": False,
"time_pos_start": 133,
"time_pos_end": 134,
"tok_context": "Sorrows and trials she had had in plenty in her life , but these the sweetness of her nature had transformed , so that from being things difficult to bear , she had built up with them her own character . Sorrow had increased her own power of sympathy ; out of trials she had learnt patience ; and failure and the gradual sinking of one she had loved into the bottomless slough of evil habit had but left her with an added dower of pity and tolerance . So the past had no sting left , and if iron had ever entered into her soul it now but served to make it strong . She was still young , too ; it was not near sunset with her yet , nor even midday , and the future that , humanly speaking , she counted to be hers was almost dazzling in its brightness . For love had dawned for her again , and no uncertain love , wrapped in the mists of memory , but one that had ripened through liking and friendship and intimacy into the authentic glory . He was in England , too ; she was going back to him . And before very long she would never go away from him again ."
}
```
### Data Fields
```
guten_id - Gutenberg ID number
hour_reference - hour from 0 to 23
time_phrase - the phrase corresponding to the referenced hour
is_ambiguous - boolean whether it is clear whether time is AM or PM
time_pos_start - token position where time_phrase begins
time_pos_end - token position where time_phrase ends (exclusive)
tok_context - context in which time_phrase appears as space-separated tokens
```
### Data Splits
No data splits.
## Dataset Creation
### Curation Rationale
The flow of time is an indispensable guide for our actions, and provides a framework in which to see a logical progression of events. Just as in real life,the clock provides the background against which literary works play out: when characters wake, eat,and act. In most works of fiction, the events of the story take place during recognizable time periods over the course of the day. Recognizing a story’s flow through time is essential to understanding the text.In this paper, we try to capture the flow of time through novels by attempting to recognize what time of day each event in the story takes place at.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Novel authors.
### Annotations
#### Annotation process
Manually annotated.
#### Who are the annotators?
Two of the authors.
### Personal and Sensitive Information
No Personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Allen Kim, Charuta Pethe and Steven Skiena, Stony Brook University
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{kim2020time,
title={What time is it? Temporal Analysis of Novels},
author={Allen Kim and Charuta Pethe and Steven Skiena},
year={2020},
eprint={2011.04124},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. | 5,878 | [
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kinnews_kirnews | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:rn",
"language:rw",
"license:mit",
"arxiv:2010.12174",
"region:us"
] | null | Kinyarwanda and Kirundi news classification datasets | @article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
} | 1 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- rn
- rw
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paperswithcode_id: kinnews-and-kirnews
pretty_name: KinnewsKirnews
dataset_info:
- config_name: kinnews_raw
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: kin_label
dtype: string
- name: en_label
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 38316546
num_examples: 17014
- name: test
num_bytes: 11971938
num_examples: 4254
download_size: 27377755
dataset_size: 50288484
- config_name: kinnews_cleaned
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 32780382
num_examples: 17014
- name: test
num_bytes: 8217453
num_examples: 4254
download_size: 27377755
dataset_size: 40997835
- config_name: kirnews_raw
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: kir_label
dtype: string
- name: en_label
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 7343223
num_examples: 3689
- name: test
num_bytes: 2499189
num_examples: 923
download_size: 5186111
dataset_size: 9842412
- config_name: kirnews_cleaned
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 6629767
num_examples: 3689
- name: test
num_bytes: 1570745
num_examples: 923
download_size: 5186111
dataset_size: 8200512
config_names:
- kinnews_cleaned
- kinnews_raw
- kirnews_cleaned
- kirnews_raw
---
# Dataset Card for kinnews_kirnews
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More Information Needed]
- **Repository:** https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus
- **Paper:** [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://arxiv.org/abs/2010.12174)
- **Leaderboard:** NA
- **Point of Contact:** [Rubungo Andre Niyongabo1](mailto:niyongabor.andre@std.uestc.edu.cn)
### Dataset Summary
Kinyarwanda and Kirundi news classification datasets (KINNEWS and KIRNEWS,respectively), which were both collected from Rwanda and Burundi news websites and newspapers, for low-resource monolingual and cross-lingual multiclass classification tasks.
### Supported Tasks and Leaderboards
This dataset can be used for text classification of news articles in Kinyarwadi and Kirundi languages. Each news article can be classified into one of the 14 possible classes. The classes are:
- politics
- sport
- economy
- health
- entertainment
- history
- technology
- culture
- religion
- environment
- education
- relationship
### Languages
Kinyarwanda and Kirundi
## Dataset Structure
### Data Instances
Here is an example from the dataset:
| Field | Value |
| ----- | ----------- |
| label | 1 |
| kin_label/kir_label | 'inkino' |
| url | 'https://nawe.bi/Primus-Ligue-Imirwi-igiye-guhura-gute-ku-ndwi-ya-6-y-ihiganwa.html' |
| title | 'Primus Ligue\xa0: Imirwi igiye guhura gute ku ndwi ya 6 y’ihiganwa\xa0?'|
| content | ' Inkino zitegekanijwe kuruno wa gatandatu igenekerezo rya 14 Nyakanga umwaka wa 2019...'|
| en_label| 'sport'|
### Data Fields
The raw version of the data for Kinyarwanda language consists of these fields
- label: The category of the news article
- kin_label/kir_label: The associated label in Kinyarwanda/Kirundi language
- en_label: The associated label in English
- url: The URL of the news article
- title: The title of the news article
- content: The content of the news article
The cleaned version contains only the `label`, `title` and the `content` fields
### Data Splits
Lang| Train | Test |
|---| ----- | ---- |
|Kinyarwandai Raw|17014|4254|
|Kinyarwandai Clean|17014|4254|
|Kirundi Raw|3689|923|
|Kirundi Clean|3689|923|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
}
```
### Contributions
Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset. | 7,944 | [
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qanta | 2023-04-05T13:37:09.000Z | [
"task_categories:question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"quizbowl",
"arxiv:1904.04792",
"region:us"
] | null | The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl. | @article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
} | 3 | 225 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: Quizbowl
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quizbowl
tags:
- quizbowl
dataset_info:
features:
- name: id
dtype: string
- name: qanta_id
dtype: int32
- name: proto_id
dtype: string
- name: qdb_id
dtype: int32
- name: dataset
dtype: string
- name: text
dtype: string
- name: full_question
dtype: string
- name: first_sentence
dtype: string
- name: char_idx
dtype: int32
- name: sentence_idx
dtype: int32
- name: tokenizations
sequence:
sequence: int32
length: 2
- name: answer
dtype: string
- name: page
dtype: string
- name: raw_answer
dtype: string
- name: fold
dtype: string
- name: gameplay
dtype: bool
- name: category
dtype: string
- name: subcategory
dtype: string
- name: tournament
dtype: string
- name: difficulty
dtype: string
- name: year
dtype: int32
config_name: mode=first,char_skip=25
splits:
- name: adversarial
num_bytes: 1258844
num_examples: 1145
- name: buzzdev
num_bytes: 1553636
num_examples: 1161
- name: buzztest
num_bytes: 2653425
num_examples: 1953
- name: buzztrain
num_bytes: 19699736
num_examples: 16706
- name: guessdev
num_bytes: 1414882
num_examples: 1055
- name: guesstest
num_bytes: 2997123
num_examples: 2151
- name: guesstrain
num_bytes: 117599750
num_examples: 96221
download_size: 170754918
dataset_size: 147177396
---
# Dataset Card for "qanta"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://www.qanta.org/](http://www.qanta.org/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Quizbowl: The Case for Incremental Question Answering](https://arxiv.org/abs/1904.04792)
- **Point of Contact:** [Jordan Boyd-Graber](mailto:jbg@umiacs.umd.edu)
- **Size of downloaded dataset files:** 170.75 MB
- **Size of the generated dataset:** 147.18 MB
- **Total amount of disk used:** 317.93 MB
### Dataset Summary
The Qanta dataset is a question answering dataset based on the academic trivia game Quizbowl.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### mode=first,char_skip=25
- **Size of downloaded dataset files:** 170.75 MB
- **Size of the generated dataset:** 147.18 MB
- **Total amount of disk used:** 317.93 MB
An example of 'guessdev' looks as follows.
```
This example was too long and was cropped:
{
"answer": "Apollo_program",
"category": "History",
"char_idx": -1,
"dataset": "quizdb.org",
"difficulty": "easy_college",
"first_sentence": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
"fold": "guessdev",
"full_question": "\"As part of this program, William Anders took a photo that Galen Rowell called \\\"the most influential environmental photograph e...",
"gameplay": false,
"id": "127028-first",
"page": "Apollo_program",
"proto_id": "",
"qanta_id": 127028,
"qdb_id": 126689,
"raw_answer": "Apollo program [or Project Apollo; accept Apollo 8; accept Apollo 1; accept Apollo 11; prompt on landing on the moon]",
"sentence_idx": -1,
"subcategory": "American",
"text": "As part of this program, William Anders took a photo that Galen Rowell called \"the most influential environmental photograph ever taken.\"",
"tokenizations": [[0, 137], [138, 281], [282, 412], [413, 592], [593, 675]],
"tournament": "ACF Fall",
"year": 2016
}
```
### Data Fields
The data fields are the same among all splits.
#### mode=first,char_skip=25
- `id`: a `string` feature.
- `qanta_id`: a `int32` feature.
- `proto_id`: a `string` feature.
- `qdb_id`: a `int32` feature.
- `dataset`: a `string` feature.
- `text`: a `string` feature.
- `full_question`: a `string` feature.
- `first_sentence`: a `string` feature.
- `char_idx`: a `int32` feature.
- `sentence_idx`: a `int32` feature.
- `tokenizations`: a dictionary feature containing:
- `feature`: a `int32` feature.
- `answer`: a `string` feature.
- `page`: a `string` feature.
- `raw_answer`: a `string` feature.
- `fold`: a `string` feature.
- `gameplay`: a `bool` feature.
- `category`: a `string` feature.
- `subcategory`: a `string` feature.
- `tournament`: a `string` feature.
- `difficulty`: a `string` feature.
- `year`: a `int32` feature.
### Data Splits
| name |adversarial|buzzdev|buzztrain|guessdev|guesstrain|buzztest|guesstest|
|-----------------------|----------:|------:|--------:|-------:|---------:|-------:|--------:|
|mode=first,char_skip=25| 1145| 1161| 16706| 1055| 96221| 1953| 2151|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Rodriguez2019QuizbowlTC,
title={Quizbowl: The Case for Incremental Question Answering},
author={Pedro Rodriguez and Shi Feng and Mohit Iyyer and He He and Jordan L. Boyd-Graber},
journal={ArXiv},
year={2019},
volume={abs/1904.04792}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | 8,802 | [
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SophieTr/reddit_clean | 2022-08-13T20:26:31.000Z | [
"region:us"
] | SophieTr | null | null | 3 | 225 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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conv_ai_3 | 2022-11-03T16:30:50.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"evaluating-dialogue-systems",
"arxiv:2009.11352",
"region:us"
] | null | The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such a situation is handled mainly through the diversification of search result page. It is however much more challenging in dialogue settings. Hence, we aim to study the following situation for dialogue settings:
- a user is asking an ambiguous question (where ambiguous question is a question to which one can return > 1 possible answers)
- the system must identify that the question is ambiguous, and, instead of trying to answer it directly, ask a good clarifying question. | @misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 13 | 224 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: More Information Needed
tags:
- evaluating-dialogue-systems
dataset_info:
features:
- name: topic_id
dtype: int32
- name: initial_request
dtype: string
- name: topic_desc
dtype: string
- name: clarification_need
dtype: int32
- name: facet_id
dtype: string
- name: facet_desc
dtype: string
- name: question_id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
config_name: conv_ai_3
splits:
- name: train
num_bytes: 2567404
num_examples: 9176
- name: validation
num_bytes: 639351
num_examples: 2313
download_size: 2940038
dataset_size: 3206755
---
# Dataset Card for [More Information Needed]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/aliannejadi/ClariQ
- **Repository:** https://github.com/aliannejadi/ClariQ
- **Paper:** https://arxiv.org/abs/2009.11352
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Conv AI 3 challenge is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings such a situation is handled mainly through the diversification of search result page. It is however much more challenging in dialogue settings. Hence, we aim to study the following situation for dialogue settings:
- a user is asking an ambiguous question (where ambiguous question is a question to which one can return > 1 possible answers)
- the system must identify that the question is ambiguous, and, instead of trying to answer it directly, ask a good clarifying question.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are a few examples from the dataset:
```
{'topic_id': 8,
'facet_id': 'F0968',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the appraisals in nearby companies.',
'clarification_need': 2,
'question_id': 'F0001',
'question': 'are you looking for a type of appraiser',
'answer': 'im looking for nearby companies that do home appraisals',
'facet_desc': 'Get the TYPE of Appraisals'
'conversation_context': [],
'context_id': 968}
```
```
{'topic_id': 8,
'facet_id': 'F0969',
'initial_request': 'I want to know about appraisals.',
'topic_desc': 'Find information about the type of appraisals.',
'clarification_need': 2,
'question_id': 'F0005',
'question': 'are you looking for a type of appraiser',
'facet_desc': 'Get the TYPE of Appraisals'
'answer': 'yes jewelry',
'conversation_context': [],
'context_id': 969}
```
```
{'topic_id': 293,
'facet_id': 'F0729',
'initial_request': 'Tell me about the educational advantages of social networking sites.',
'topic_desc': 'Find information about the educational benefits of the social media sites',
'clarification_need': 2,
'question_id': 'F0009'
'question': 'which social networking sites would you like information on',
'answer': 'i don have a specific one in mind just overall educational benefits to social media sites',
'facet_desc': 'Detailed information about the Networking Sites.'
'conversation_context': [{'question': 'what level of schooling are you interested in gaining the advantages to social networking sites', 'answer': 'all levels'}, {'question': 'what type of educational advantages are you seeking from social networking', 'answer': 'i just want to know if there are any'}],
'context_id': 976573}
```
### Data Fields
- `topic_id`: the ID of the topic (`initial_request`).
- `initial_request`: the query (text) that initiates the conversation.
- `topic_desc`: a full description of the topic as it appears in the TREC Web Track data.
- `clarification_need`: a label from 1 to 4, indicating how much it is needed to clarify a topic. If an `initial_request` is self-contained and would not need any clarification, the label would be 1. While if a `initial_request` is absolutely ambiguous, making it impossible for a search engine to guess the user's right intent before clarification, the label would be 4.
- `facet_id`: the ID of the facet.
- `facet_desc`: a full description of the facet (information need) as it appears in the TREC Web Track data.
- `question_id`: the ID of the question..
- `question`: a clarifying question that the system can pose to the user for the current topic and facet.
- `answer`: an answer to the clarifying question, assuming that the user is in the context of the current row (i.e., the user's initial query is `initial_request`, their information need is `facet_desc`, and `question` has been posed to the user).
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@misc{aliannejadi2020convai3,
title={ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)},
author={Mohammad Aliannejadi and Julia Kiseleva and Aleksandr Chuklin and Jeff Dalton and Mikhail Burtsev},
year={2020},
eprint={2009.11352},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. | 7,405 | [
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thaiqa_squad | 2022-11-03T16:15:52.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-thaiqa",
"language:th",
"license:cc-by-nc-sa-3.0",
"region:us"
] | null | `thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in
[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from
Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/). | No clear citation guidelines from source:
https://aiforthai.in.th/corpus.php
SQuAD version:
https://github.com/PyThaiNLP/thaiqa_squad | 5 | 224 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-thaiqa
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: thaiqa-squad
dataset_info:
features:
- name: question_id
dtype: int32
- name: article_id
dtype: int32
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: answer
dtype: string
- name: answer_begin_position
dtype: int32
- name: answer_end_position
dtype: int32
config_name: thaiqa_squad
splits:
- name: train
num_bytes: 47905050
num_examples: 4000
- name: validation
num_bytes: 744813
num_examples: 74
download_size: 10003354
dataset_size: 48649863
---
# Dataset Card for `thaiqa-squad`
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://github.com/pythainlp/thaiqa_squad (original `thaiqa` at https://aiforthai.in.th/)
- **Repository:** http://github.com/pythainlp/thaiqa_squad
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**http://github.com/pythainlp/ (original `thaiqa` at https://aiforthai.in.th/)
### Dataset Summary
`thaiqa_squad` is an open-domain, extractive question answering dataset (4,000 questions in `train` and 74 questions in `dev`) in [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, originally created by [NECTEC](https://www.nectec.or.th/en/) from Wikipedia articles and adapted to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format by [PyThaiNLP](https://github.com/PyThaiNLP/).
### Supported Tasks and Leaderboards
extractive question answering
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'answers': {'answer': ['ฮิกกิ้นส์'], 'answer_begin_position': [528], 'answer_end_position': [537]}, 'article_id': 115035, 'context': '<doc id="115035" url="https://th.wikipedia.org/wiki?curid=115035" title="เบนจี้">เบนจี้ เบนจี้ () เป็นชื่อตัวละครหมาพันทางแสนรู้ ที่ปรากฏอยู่ในภาพยนตร์หลายเรื่องที่เขียนบท และกำกับโดย โจ แคมป์ ในช่วงทศวรรษ 1970 ถึง 1980 ภาพยนตร์เรื่องแรกในชุด ใช้ชื่อเรื่องว่า เบนจี้ เช่นเดียวกับตัวละคร ถ่ายทำที่เมืองดัลลัส รัฐเทกซัส ฉายครั้งแรกในปี พ.ศ. 2517 ภาพยนตร์ได้รับการเสนอชื่อเข้าชิงรางวัลออสการ์ และได้รางวัลลูกโลกทองคำ สาขาเพลงประกอบยอดเยี่ยม จากเพลง Benji\'s Theme (I Feel Love) ร้องโดย ชาร์ลี ริช หมาที่แสดงเป็นเบนจี้ตัวแรก ชื่อว่า ฮิกกิ้นส์ (พ.ศ. 2502 - พ.ศ. 2518) มีอายุถึง 15 ปีแล้วในขณะแสดง หลังจากภาพยนตร์ออกฉายได้ไม่นาน มันก็ตายในปี พ.ศ. 2518เบนจี้ในภาพยนตร์เบนจี้ในภาพยนตร์. - พ.ศ. 2517, Benji (ภาพยนตร์) - พ.ศ. 2520, For the Love of Benji (ภาพยนตร์) - พ.ศ. 2521, Benji\'s Very Own Christmas Story (ภาพยนตร์โทรทัศน์) - พ.ศ. 2523, Oh Heavenly Dog (ภาพยนตร์) - พ.ศ. 2523, Benji at Work (ภาพยนตร์โทรทัศน์) - พ.ศ. 2524, Benji Takes a Dive at Marineland (ภาพยนตร์โทรทัศน์) - พ.ศ. 2526, Benji, Zax & the Alien Prince (ภาพยนตร์ซีรีส์) - พ.ศ. 2530, Benji the Hunted (ภาพยนตร์) - พ.ศ. 2547, Benji: Off the Leash! (ภาพยนตร์) - พ.ศ. 2550, Benji: The Barkening (ภาพยนตร์)</doc>\n', 'question': 'สุนัขตัวแรกรับบทเป็นเบนจี้ในภาพยนตร์เรื่อง Benji ที่ออกฉายในปี พ.ศ. 2517 มีชื่อว่าอะไร', 'question_id': 1}
{'answers': {'answer': ['ชาร์ลี ริช'], 'answer_begin_position': [482], 'answer_end_position': [492]}, 'article_id': 115035, 'context': '<doc id="115035" url="https://th.wikipedia.org/wiki?curid=115035" title="เบนจี้">เบนจี้ เบนจี้ () เป็นชื่อตัวละครหมาพันทางแสนรู้ ที่ปรากฏอยู่ในภาพยนตร์หลายเรื่องที่เขียนบท และกำกับโดย โจ แคมป์ ในช่วงทศวรรษ 1970 ถึง 1980 ภาพยนตร์เรื่องแรกในชุด ใช้ชื่อเรื่องว่า เบนจี้ เช่นเดียวกับตัวละคร ถ่ายทำที่เมืองดัลลัส รัฐเทกซัส ฉายครั้งแรกในปี พ.ศ. 2517 ภาพยนตร์ได้รับการเสนอชื่อเข้าชิงรางวัลออสการ์ และได้รางวัลลูกโลกทองคำ สาขาเพลงประกอบยอดเยี่ยม จากเพลง Benji\'s Theme (I Feel Love) ร้องโดย ชาร์ลี ริช หมาที่แสดงเป็นเบนจี้ตัวแรก ชื่อว่า ฮิกกิ้นส์ (พ.ศ. 2502 - พ.ศ. 2518) มีอายุถึง 15 ปีแล้วในขณะแสดง หลังจากภาพยนตร์ออกฉายได้ไม่นาน มันก็ตายในปี พ.ศ. 2518เบนจี้ในภาพยนตร์เบนจี้ในภาพยนตร์. - พ.ศ. 2517, Benji (ภาพยนตร์) - พ.ศ. 2520, For the Love of Benji (ภาพยนตร์) - พ.ศ. 2521, Benji\'s Very Own Christmas Story (ภาพยนตร์โทรทัศน์) - พ.ศ. 2523, Oh Heavenly Dog (ภาพยนตร์) - พ.ศ. 2523, Benji at Work (ภาพยนตร์โทรทัศน์) - พ.ศ. 2524, Benji Takes a Dive at Marineland (ภาพยนตร์โทรทัศน์) - พ.ศ. 2526, Benji, Zax & the Alien Prince (ภาพยนตร์ซีรีส์) - พ.ศ. 2530, Benji the Hunted (ภาพยนตร์) - พ.ศ. 2547, Benji: Off the Leash! (ภาพยนตร์) - พ.ศ. 2550, Benji: The Barkening (ภาพยนตร์)</doc>\n', 'question': "เพลง Benji's Theme ใช้ประกอบภาพยนตร์เรื่อง Benji ในปีพ.ศ. 2517 ขับร้องโดยใคร", 'question_id': 2035}
```
### Data Fields
```
{
"question_id": question id
"article_id": article id
"context": article texts
"question": question
"answers":
{
"answer": answer text
"answer_begin_position": answer beginning position
"answer_end_position": answer exclusive upper bound position
}
),
}
```
### Data Splits
| | train | valid |
|-------------------------|-------------|-------------|
| # questions | 4000 | 74 |
| # avg words in context | 1186.740750 | 1016.459459 |
| # avg words in question | 14.325500 | 12.743243 |
| # avg words in answer | 3.279750 | 4.608108 |
## Dataset Creation
### Curation Rationale
[PyThaiNLP](https://github.com/PyThaiNLP/) created `thaiqa_squad` as a [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) version of [thaiqa](http://copycatch.in.th/thai-qa-task.html). [thaiqa](https://aiforthai.in.th/corpus.php) is part of [The 2nd Question answering program from Thai Wikipedia](http://copycatch.in.th/thai-qa-task.html) of [National Software Contest 2020](http://nsc.siit.tu.ac.th/GENA2/login.php).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Wikipedia authors for contexts and [NECTEC](https://www.nectec.or.th/en/) for questions and answer annotations
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[NECTEC](https://www.nectec.or.th/en/)
### Personal and Sensitive Information
All contents are from Wikipedia. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- open-domain, extractive question answering in Thai
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
The contexts include `<doc>` tags at start and at the end
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/en/) for original [thaiqa](https://aiforthai.in.th/corpus.php). SQuAD formattting by [PyThaiNLP](https://github.com/PyThaiNLP/).
### Licensing Information
CC-BY-NC-SA 3.0
### Citation Information
No clear citation guidelines from source: https://aiforthai.in.th/corpus.php
SQuAD version: https://github.com/PyThaiNLP/thaiqa_squad
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | 8,324 | [
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IIC/qges | 2022-06-16T12:11:00.000Z | [
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] |
clane9/NSD-Flat | 2023-09-28T01:26:21.000Z | [
"task_categories:image-to-image",
"task_categories:object-detection",
"size_categories:100K<n<1M",
"license:other",
"biology",
"neuroscience",
"fmri",
"region:us"
] | clane9 | null | null | 3 | 224 | 2023-07-20T21:40:43 | ---
license: other
dataset_info:
features:
- name: subject_id
dtype: int64
- name: trial_id
dtype: int64
- name: session_id
dtype: int64
- name: nsd_id
dtype: int64
- name: image
dtype: image
- name: activity
dtype: image
- name: subject
dtype: string
- name: flagged
dtype: bool
- name: BOLD5000
dtype: bool
- name: shared1000
dtype: bool
- name: coco_split
dtype: string
- name: coco_id
dtype: int64
- name: objects
struct:
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sequence: int64
- name: bbox
sequence:
sequence: float64
- name: category
sequence: string
- name: iscrowd
sequence: int64
- name: segmentation
list:
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dtype: string
- name: poly
sequence:
sequence: float64
- name: size
sequence: int64
- name: supercategory
sequence: string
- name: target
sequence: int64
- name: captions
sequence: string
- name: repetitions
struct:
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- name: subject6_rep2
dtype: int64
- name: subject7_rep0
dtype: int64
- name: subject7_rep1
dtype: int64
- name: subject7_rep2
dtype: int64
- name: subject8_rep0
dtype: int64
- name: subject8_rep1
dtype: int64
- name: subject8_rep2
dtype: int64
splits:
- name: train
num_bytes: 26695182666.0
num_examples: 195000
- name: test
num_bytes: 2461280671.0
num_examples: 18000
download_size: 22565691383
dataset_size: 29156463337.0
task_categories:
- image-to-image
- object-detection
tags:
- biology
- neuroscience
- fmri
size_categories:
- 100K<n<1M
---
# NSD-Flat
[[`GitHub`]](https://github.com/clane9/NSD-Flat) [[🤗 `Hugging Face Hub`]](https://huggingface.co/datasets/clane9/NSD-Flat)
A Hugging Face dataset of pre-processed brain activity flat maps from the [Natural Scenes Dataset](https://naturalscenesdataset.org/), constrained to a visual cortex region of interest and rendered as PNG images.
## Load the dataset
Load the dataset from [Hugging Face Hub](https://huggingface.co/datasets/clane9/NSD-Flat)
```python
from datasets import load_dataset
dataset = load_dataset("clane9/NSD-Flat", split="train")
```
## Building the dataset
### 1. Download source data
Run [`download_data.sh`](download_data.sh) to download the required source data:
- NSD stimuli images and presentation info
- COCO annotations
- NSD beta activity maps in fsaverge surface space
```bash
bash download_data.sh
```
### 2. Convert the COCO annotations
Run [`convert_nsd_annotations.py`](convert_nsd_annotations.py) to crop and reorganize the COCO annotations for NSD.
```bash
python convert_nsd_annotations.py
```
### 3. Generate the dataset
Run [`generate_dataset.py`](generate_dataset.py) to generate the huggingface dataset in Arrow format.
```bash
python generate_dataset.py --img_size 256 --workers 8
```
## Citation
If you find this dataset useful, please consider citing:
```
@article{allen2022massive,
title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
author = {Allen, Emily J and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L and Prince, Jacob S and Dowdle, Logan T and Nau, Matthias and Caron, Brad and Pestilli, Franco and Charest, Ian and others},
journal = {Nature neuroscience},
volume = {25},
number = {1},
pages = {116--126},
year = {2022},
publisher = {Nature Publishing Group US New York}
}
```
```
@misc{lane2023nsdflat,
author = {Connor Lane},
title = {NSD-Flat: Pre-processed brain activity flat maps from the Natural Scenes Dataset},
howpublished = {\url{https://huggingface.co/datasets/clane9/NSD-Flat}},
year = {2023},
}
```
## License
Usage of this dataset constitutes agreement to the [NSD Terms and Conditions](https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions). | 4,727 | [
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ajgt_twitter_ar | 2023-01-25T14:26:05.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | null | Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect. | @inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610},
year={2017},
organization={Springer}
} | 2 | 223 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Arabic Jordanian General Tweets
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Positive
config_name: plain_text
splits:
- name: train
num_bytes: 175424
num_examples: 1800
download_size: 107395
dataset_size: 175424
---
# Dataset Card for Arabic Jordanian General Tweets
## Table of Contents
- [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT)
- **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66)
- **Point of Contact:** [Khaled Alomari](khaled.alomari@adu.ac.ae)
### Dataset Summary
Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A binary datset with with negative and positive sentiments.
### Data Fields
- `text` (str): Tweet text.
- `label` (int): Sentiment.
### Data Splits
The dataset is not split.
| | train |
|----------|------:|
| no split | 1,800 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Contains 1,800 tweets collected from twitter.
#### Who are the source language producers?
From tweeter.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610},
year={2017},
organization={Springer}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 4,656 | [
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gsarti/itacola | 2022-07-01T15:38:55.000Z | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:it",
"license:unknown",
"arxiv:2109.12053",
"region:us"
] | gsarti | The Italian Corpus of Linguistic Acceptability includes almost 10k sentences taken from
linguistic literature with a binary annotation made by the original authors themselves.
The work is inspired by the English Corpus of Linguistic Acceptability (CoLA) by Warstadt et al.
Part of the dataset has been manually annotated to highlight 9 linguistic phenomena. | @inproceedings{trotta-etal-2021-monolingual,
author = {Trotta, Daniela and Guarasci, Raffaele and Leonardelli, Elisa and Tonelli, Sara},
title = {Monolingual and Cross-Lingual Acceptability Judgments with the Italian {CoLA} corpus},
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = {2021},
address = "Punta Cana, Dominican Republic and Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2109.12053",
} | 0 | 223 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- it
license:
- unknown
multilinguality:
- monolingual
pretty_name: itacola
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
---
# Dataset Card for ItaCoLA
## Table of Contents
- [Dataset Card for ItaCoLA](#dataset-card-for-itacola)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Acceptability Classification](#acceptability-classification)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Scores Configuration](#scores-configuration)
- [Phenomena Configuration](#phenomena-configuration)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Github](https://github.com/dhfbk/ItaCoLA-dataset)
- **Paper:** [Arxiv](http://ceur-ws.org/Vol-2765/paper169.pdf)
- **Point of Contact:** [Daniela Trotta](dtrotta@unisa.it)
### Dataset Summary
The Italian Corpus of Linguistic Acceptability includes almost 10k sentences taken from linguistic literature with a binary annotation made by the original authors themselves. The work is inspired by the English [Corpus of Linguistic Acceptability](https://nyu-mll.github.io/CoLA/).
**Disclaimer**: *The ItaCoLA corpus is hosted on Github by the [Digital Humanities group at FBK](https://dh.fbk.eu/)*. It was introduced in the article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) by [Daniela Trotta](https://dh.fbk.eu/author/daniela/), [Raffaele Guarasci](https://www.icar.cnr.it/persone/guarasci/), [Elisa Leonardelli](https://dh.fbk.eu/author/elisa/), [Sara Tonelli](https://dh.fbk.eu/author/sara/)
### Supported Tasks and Leaderboards
#### Acceptability Classification
The following table is taken from Table 4 of the original paper, where an LSTM and a BERT model pretrained on the Italian languages are fine-tuned on the `train` split of the corpus and evaluated respectively on the `test` split (*In-domain*, `in`) and on the acceptability portion of the [AcCompl-it] corpus (*Out-of-domain*, `out`). Models are evaluated with accuracy (*Acc.*) and Matthews Correlation Coefficient (*MCC*) in both settings. Results are averaged over 10 runs with ±stdev. error bounds.
| | `in`, Acc.| `in`, MCC| `out`, Acc.|`out`, MCC|
|---------:|-----------:|----------:|-----------:|---------:|
|`LSTM` | 0.794 | 0.278 ± 0.029 | 0.605 | 0.147 ± 0.066 |
|`ITA-BERT`| 0.904 | 0.603 ± 0.022 | 0.683 | 0.198 ± 0.036 |
### Languages
The language data in ItaCoLA is in Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
#### Scores Configuration
The `scores` configuration contains sentences with acceptability judgments. An example from the `train` split of the `scores` config (default) is provided below.
```json
{
"unique_id": 1,
"source": "Graffi_1994",
"acceptability": 1,
"sentence": "Quest'uomo mi ha colpito."
}
```
The text is provided as-is, without further preprocessing or tokenization.
The fields are the following:
- `unique_id`: Unique identifier for the sentence across configurations.
- `source`: Original source for the sentence.
- `acceptability`: Binary score, 1 = acceptable, 0 = not acceptable.
- `sentence`: The evaluated sentence.
#### Phenomena Configuration
The `phenomena` configuration contains a sample of sentences from `scores` that has been manually annotated to denote the presence of 9 linguistic phenomena. An example from the `train` split is provided below:
```json
{
"unique_id": 1,
"source": "Graffi_1994",
"acceptability": 1,
"sentence": "Quest'uomo mi ha colpito.",
"cleft_construction": 0,
"copular_construction": 0,
"subject_verb_agreement": 1,
"wh_islands_violations": 0,
"simple": 0,
"question": 0,
"auxiliary": 1,
"bind": 0,
"indefinite_pronouns": 0
}
```
For each one of the new fields, the value of the binary score denotes the presence (1) or the absence (0) of the respective phenomenon. Refer to the original paper for a detailed description of each phenomenon.
### Data Splits
| config| train| test|
|----------:|-----:|----:|
|`scores` | 7801 | 975 |
|`phenomena`| 2088 | - |
### Dataset Creation
Please refer to the original article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The authors are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
No licensing information available.
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@inproceedings{trotta-etal-2021-monolingual-cross,
title = "Monolingual and Cross-Lingual Acceptability Judgments with the {I}talian {C}o{LA} corpus",
author = "Trotta, Daniela and
Guarasci, Raffaele and
Leonardelli, Elisa and
Tonelli, Sara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.250",
doi = "10.18653/v1/2021.findings-emnlp.250",
pages = "2929--2940"
}
```
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] |
NeelNanda/counterfact-tracing | 2022-11-05T15:19:43.000Z | [
"arxiv:2211.00593",
"region:us"
] | NeelNanda | null | null | 5 | 223 | 2022-11-05T15:09:51 | ---
dataset_info:
features:
- name: relation
dtype: string
- name: relation_prefix
dtype: string
- name: relation_suffix
dtype: string
- name: prompt
dtype: string
- name: relation_id
dtype: string
- name: target_false_id
dtype: string
- name: target_true_id
dtype: string
- name: target_true
dtype: string
- name: target_false
dtype: string
- name: subject
dtype: string
splits:
- name: train
num_bytes: 3400668
num_examples: 21919
download_size: 1109314
dataset_size: 3400668
---
# Dataset Card for "counterfact-tracing"
This is adapted from the counterfact dataset from the excellent [ROME paper](https://rome.baulab.info/) from David Bau and Kevin Meng.
This is a dataset of 21919 factual relations, formatted as `data["prompt"]==f"{data['relation_prefix']}{data['subject']}{data['relation_suffix']}"`. Each has two responses `data["target_true"]` and `data["target_false"]` which is intended to go immediately after the prompt.
The dataset was originally designed for memory editing in models. I made this for a research project doing mechanistic interpretability of how models recall factual knowledge, building on their causal tracing technique, and so stripped their data down to the information relevant to causal tracing. I also prepended spaces where relevant so that the subject and targets can be properly tokenized as is (spaces are always prepended to targets, and are prepended to subjects unless the subject is at the start of a sentence).
Each fact has both a true and false target. I recommend measuring the logit *difference* between the true and false target (at least, if it's a single token target!), so as to control for eg the parts of the model which identify that it's supposed to be giving a fact of this type at all. (Idea inspired by the excellent [Interpretability In the Wild](https://arxiv.org/abs/2211.00593) paper). | 1,934 | [
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alzoubi36/policy_ie_b | 2023-06-25T07:13:15.000Z | [
"region:us"
] | alzoubi36 | null | null | 0 | 223 | 2023-06-25T07:10:04 | ---
dataset_info:
features:
- name: type-I
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
- name: type-II
struct:
- name: subtask
dtype: string
- name: tags
sequence: string
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 3944744
num_examples: 4109
- name: validation
num_bytes: 1102169
num_examples: 1041
- name: test
num_bytes: 1102169
num_examples: 1041
download_size: 814098
dataset_size: 6149082
---
# Dataset for the PolicyIE-B task in the [PrivacyGLUE](https://github.com/infsys-lab/privacy-glue) dataset
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] |
pbaoo2705/processed_dataset_v2 | 2023-09-06T05:27:28.000Z | [
"region:us"
] | pbaoo2705 | null | null | 0 | 223 | 2023-09-06T05:27:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
dtype: string
- name: long_answer
dtype: string
- name: final_decision
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 9013737
num_examples: 5000
- name: test
num_bytes: 1797886
num_examples: 1000
download_size: 6294228
dataset_size: 10811623
---
# Dataset Card for "processed_dataset_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 737 | [
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jeopardy | 2023-04-05T10:07:53.000Z | [
"language:en",
"region:us"
] | null | Dataset containing 216,930 Jeopardy questions, answers and other data.
The json file is an unordered list of questions where each question has
'category' : the question category, e.g. "HISTORY"
'value' : integer $ value of the question as string, e.g. "200"
Note: This is "None" for Final Jeopardy! and Tiebreaker questions
'question' : text of question
Note: This sometimes contains hyperlinks and other things messy text such as when there's a picture or video question
'answer' : text of answer
'round' : one of "Jeopardy!","Double Jeopardy!","Final Jeopardy!" or "Tiebreaker"
Note: Tiebreaker questions do happen but they're very rare (like once every 20 years)
'show_number' : int of show number, e.g '4680'
'air_date' : string of the show air date in format YYYY-MM-DD | 4 | 222 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: null
pretty_name: jeopardy
dataset_info:
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: int32
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
splits:
- name: train
num_bytes: 35916080
num_examples: 216930
download_size: 55554625
dataset_size: 35916080
---
# Dataset Card for "jeopardy"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/](https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 12.72 MB
- **Size of the generated dataset:** 36.13 MB
- **Total amount of disk used:** 48.85 MB
### Dataset Summary
Dataset containing 216,930 Jeopardy questions, answers and other data.
The json file is an unordered list of questions where each question has
'category' : the question category, e.g. "HISTORY"
'value' : integer $ value of the question as string, e.g. "200"
Note: This is "None" for Final Jeopardy! and Tiebreaker questions
'question' : text of question
Note: This sometimes contains hyperlinks and other things messy text such as when there's a picture or video question
'answer' : text of answer
'round' : one of "Jeopardy!","Double Jeopardy!","Final Jeopardy!" or "Tiebreaker"
Note: Tiebreaker questions do happen but they're very rare (like once every 20 years)
'show_number' : int of show number, e.g '4680'
'air_date' : string of the show air date in format YYYY-MM-DD
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 12.72 MB
- **Size of the generated dataset:** 36.13 MB
- **Total amount of disk used:** 48.85 MB
An example of 'train' looks as follows.
```
{
"air_date": "2004-12-31",
"answer": "Hattie McDaniel (for her role in Gone with the Wind)",
"category": "EPITAPHS & TRIBUTES",
"question": "'1939 Oscar winner: \"...you are a credit to your craft, your race and to your family\"'",
"round": "Jeopardy!",
"show_number": 4680,
"value": 2000
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `int32` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
### Data Splits
| name |train |
|-------|-----:|
|default|216930|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 6,620 | [
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] | |
simple_questions_v2 | 2022-11-18T21:46:14.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | SimpleQuestions is a dataset for simple QA, which consists
of a total of 108,442 questions written in natural language by human
English-speaking annotators each paired with a corresponding fact,
formatted as (subject, relationship, object), that provides the answer
but also a complete explanation. Fast have been extracted from the
Knowledge Base Freebase (freebase.com). We randomly shuffle these
questions and use 70% of them (75910) as training set, 10% as
validation set (10845), and the remaining 20% as test set. | @misc{bordes2015largescale,
title={Large-scale Simple Question Answering with Memory Networks},
author={Antoine Bordes and Nicolas Usunier and Sumit Chopra and Jason Weston},
year={2015},
eprint={1506.02075},
archivePrefix={arXiv},
primaryClass={cs.LG}
} | 1 | 222 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: simplequestions
pretty_name: SimpleQuestions
dataset_info:
- config_name: annotated
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entity
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 12376039
num_examples: 75910
- name: validation
num_bytes: 12376039
num_examples: 75910
- name: test
num_bytes: 12376039
num_examples: 75910
download_size: 423435590
dataset_size: 37128117
- config_name: freebase2m
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entities
sequence: string
splits:
- name: train
num_bytes: 1964037256
num_examples: 10843106
download_size: 423435590
dataset_size: 1964037256
- config_name: freebase5m
features:
- name: id
dtype: string
- name: subject_entity
dtype: string
- name: relationship
dtype: string
- name: object_entities
sequence: string
splits:
- name: train
num_bytes: 2481753516
num_examples: 12010500
download_size: 423435590
dataset_size: 2481753516
---
# Dataset Card for SimpleQuestions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://research.fb.com/downloads/babi/
- **Repository:** https://github.com/fbougares/TSAC
- **Paper:** https://research.fb.com/publications/large-scale-simple-question-answering-with-memory-networks/
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [Antoine Borde](abordes@fb.com) [Nicolas Usunie](usunier@fb.com) [Sumit Chopra](spchopra@fb.com), [Jason Weston](jase@fb.com)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
* What American cartoonist is the creator of Andy Lippincott?
Fact: (andy_lippincott, character_created_by, garry_trudeau)
* Which forest is Fires Creek in?
Fact: (fires_creek, containedby, nantahala_national_forest)
* What does Jimmy Neutron do?
Fact: (jimmy_neutron, fictional_character_occupation, inventor)
* What dietary restriction is incompatible with kimchi?
Fact: (kimchi, incompatible_with_dietary_restrictions, veganism)
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | 4,818 | [
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ChristophSchuhmann/improved_aesthetics_6.5plus | 2022-08-10T11:34:17.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 37 | 222 | 2022-08-10T11:34:12 | Entry not found | 15 | [
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bigbio/ebm_pico | 2022-12-22T15:44:33.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | This corpus release contains 4,993 abstracts annotated with (P)articipants,
(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and
aggregated to reduce noise. Test labels are collected from medical professionals. | @inproceedings{nye-etal-2018-corpus,
title = "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature",
author = "Nye, Benjamin and
Li, Junyi Jessy and
Patel, Roma and
Yang, Yinfei and
Marshall, Iain and
Nenkova, Ani and
Wallace, Byron",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1019",
doi = "10.18653/v1/P18-1019",
pages = "197--207",
} | 0 | 222 | 2022-11-13T22:08:15 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: EBM NLP
homepage: https://github.com/bepnye/EBM-NLP
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for EBM NLP
## Dataset Description
- **Homepage:** https://github.com/bepnye/EBM-NLP
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
This corpus release contains 4,993 abstracts annotated with (P)articipants,
(I)nterventions, and (O)utcomes. Training labels are sourced from AMT workers and
aggregated to reduce noise. Test labels are collected from medical professionals.
## Citation Information
```
@inproceedings{nye-etal-2018-corpus,
title = "A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature",
author = "Nye, Benjamin and
Li, Junyi Jessy and
Patel, Roma and
Yang, Yinfei and
Marshall, Iain and
Nenkova, Ani and
Wallace, Byron",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1019",
doi = "10.18653/v1/P18-1019",
pages = "197--207",
}
```
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jondurbin/airoboros-3.0 | 2023-10-12T14:53:54.000Z | [
"license:apache-2.0",
"region:us"
] | jondurbin | null | null | 9 | 222 | 2023-09-29T20:56:48 | ---
license: apache-2.0
---
## Overview
This dataset is a continuation of the airoboros datasets, with two main new contributions:
* MathJSON - math questions, prefixed with __"Create a MathJSON solution to the following:"__, which then outputs a JSON between __`<mathjson>`__ and __`</mathjson>`__ tags, which can be parsed and passed to a deterministic library to perform calculations.
* Anon-contributed RP dataset to enhance multi-turn coherency.
Some of the MathJSON data was adapted from https://huggingface.co/datasets/meta-math/MetaMathQA
## Format
The format is now in ShareGPT format, to better accomodate the OS ecosystem fine-tuning tooling.
## MathJSON
As we all know, math is really difficult for LLMs, especially floating point operations, trig functions, factorials, and several-digit numbers.
Instead of sending millions and millions of examples to the LLM for fine-tuning, I've created a new category `mathjson` which simply presents the solution to the problem as a JSON string that can be evaluated by traditional computation.
The dataset has roughly 4k samples, as a proof of concept. This can be expanded fairly easily, now that the fine-tuned models have a basic understanding of how to represent the problems.
For example:
```
Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST]
```
```
The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1.
Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr².
Solution as MathJSON:
<mathjson>
[
"Multiply",
"Pi",
[
"Power",
17.2456,
2
]
]
</mathjson>
```
Simply extract the JSON string between the mathjson tags, and evaluate it with https://cortexjs.io/compute-engine/ or my ultra-hacky limited-functionality python version of it https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py | 2,165 | [
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] |
ubuntu_dialogs_corpus | 2023-04-05T13:42:49.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1506.08909",
"region:us"
] | null | Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter. | @article{DBLP:journals/corr/LowePSP15,
author = {Ryan Lowe and
Nissan Pow and
Iulian Serban and
Joelle Pineau},
title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems},
journal = {CoRR},
volume = {abs/1506.08909},
year = {2015},
url = {http://arxiv.org/abs/1506.08909},
archivePrefix = {arXiv},
eprint = {1506.08909},
timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
biburl = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 13 | 221 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: UDC (Ubuntu Dialogue Corpus)
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: ubuntu-dialogue-corpus
dataset_info:
- config_name: train
features:
- name: Context
dtype: string
- name: Utterance
dtype: string
- name: Label
dtype: int32
splits:
- name: train
num_bytes: 525126729
num_examples: 1000000
download_size: 0
dataset_size: 525126729
- config_name: dev_test
features:
- name: Context
dtype: string
- name: Ground Truth Utterance
dtype: string
- name: Distractor_0
dtype: string
- name: Distractor_1
dtype: string
- name: Distractor_2
dtype: string
- name: Distractor_3
dtype: string
- name: Distractor_4
dtype: string
- name: Distractor_5
dtype: string
- name: Distractor_6
dtype: string
- name: Distractor_7
dtype: string
- name: Distractor_8
dtype: string
splits:
- name: test
num_bytes: 27060502
num_examples: 18920
- name: validation
num_bytes: 27663181
num_examples: 19560
download_size: 0
dataset_size: 54723683
---
# Dataset Card for "ubuntu_dialogs_corpus"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/rkadlec/ubuntu-ranking-dataset-creator
- **Paper:** [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://arxiv.org/abs/1506.08909)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
### Dataset Summary
Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"Context": "\"i think we could import the old comment via rsync , but from there we need to go via email . i think it be easier than cach the...",
"Label": 1,
"Utterance": "basic each xfree86 upload will not forc user to upgrad 100mb of font for noth __eou__ no someth i do in my spare time . __eou__"
}
```
### Data Fields
The data fields are the same among all splits.
#### train
- `Context`: a `string` feature.
- `Utterance`: a `string` feature.
- `Label`: a `int32` feature.
### Data Splits
|name |train |
|-----|-----:|
|train|127422|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/corr/LowePSP15,
author = {Ryan Lowe and
Nissan Pow and
Iulian Serban and
Joelle Pineau},
title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems},
journal = {CoRR},
volume = {abs/1506.08909},
year = {2015},
url = {http://arxiv.org/abs/1506.08909},
archivePrefix = {arXiv},
eprint = {1506.08909},
timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
biburl = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | 7,465 | [
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] |
ScandEval/suc3-mini | 2023-07-05T09:42:05.000Z | [
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:sv",
"license:cc-by-4.0",
"region:us"
] | ScandEval | null | null | 0 | 221 | 2022-06-14T18:21:45 | ---
dataset_info:
features:
- name: text
dtype: string
- name: tokens
sequence: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 344855
num_examples: 1024
- name: test
num_bytes: 681936
num_examples: 2048
- name: val
num_bytes: 81547
num_examples: 256
download_size: 509020
dataset_size: 1108338
license: cc-by-4.0
task_categories:
- token-classification
language:
- sv
size_categories:
- 1K<n<10K
---
# Dataset Card for "suc3-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 644 | [
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tasksource/folio | 2023-05-31T13:40:30.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"language:en",
"license:cc",
"arxiv:2209.00840",
"region:us"
] | tasksource | null | null | 5 | 221 | 2023-02-21T08:15:17 | ---
license: cc
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
- multi-input-text-classification
---
https://github.com/Yale-LILY/FOLIO
```
@article{han2022folio,
title={FOLIO: Natural Language Reasoning with First-Order Logic},
author = {Han, Simeng and Schoelkopf, Hailey and Zhao, Yilun and Qi, Zhenting and Riddell, Martin and Benson, Luke and Sun, Lucy and Zubova, Ekaterina and Qiao, Yujie and Burtell, Matthew and Peng, David and Fan, Jonathan and Liu, Yixin and Wong, Brian and Sailor, Malcolm and Ni, Ansong and Nan, Linyong and Kasai, Jungo and Yu, Tao and Zhang, Rui and Joty, Shafiq and Fabbri, Alexander R. and Kryscinski, Wojciech and Lin, Xi Victoria and Xiong, Caiming and Radev, Dragomir},
journal={arXiv preprint arXiv:2209.00840},
url = {https://arxiv.org/abs/2209.00840},
year={2022}
}
``` | 869 | [
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medalpaca/medical_meadow_wikidoc_patient_information | 2023-04-06T17:08:53.000Z | [
"task_categories:question-answering",
"language:en",
"license:cc",
"region:us"
] | medalpaca | null | null | 6 | 221 | 2023-04-06T17:05:50 | ---
license: cc
task_categories:
- question-answering
language:
- en
---
# Dataset Card for WikiDoc
For the dataset containing rephrased content from the living textbook refer to [this dataset](https://huggingface.co/datasets/medalpaca/medical_meadow_wikidoc)
## Dataset Description
- **Source:** https://www.wikidoc.org/index.php/Main_Page
- **Repository:** https://github.com/kbressem/medalpaca
- **Paper:** TBA
### Dataset Summary
This dataset containes medical question-answer pairs extracted from [WikiDoc](https://www.wikidoc.org/index.php/Main_Page),
a collaborative platform for medical professionals to share and contribute to up-to-date medical knowledge.
The platform has to main subsites, the "Living Textbook" and "Patient Information". The "Living Textbook"
contains chapters for various medical specialties, which we crawled. We then used GTP-3.5-Turbo to rephrase
the paragraph heading to a question and used the paragraph as answer. Patient Information is structured differently,
in that each section subheading is already a question, making rephrasing them obsolete.
**Note:** This dataset is still a WIP. While the Q/A pairs from the patient information seems to be mostly correct,
the conversion using GPT-3.5-Turbo yielded some unsatisfactory results in approximately 30% of cases. We are in the process of cleaning this dataset.
### Citation Information
TBA | 1,396 | [
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CollectiveCognition/chats-data-2023-09-27 | 2023-09-28T00:40:51.000Z | [
"license:mit",
"region:us"
] | CollectiveCognition | null | null | 15 | 221 | 2023-09-28T00:39:17 | ---
license: mit
---
# Dataset Card for "Collective Cognition ChatGPT Conversations"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
### Dataset Summary
The "Collective Cognition ChatGPT Conversations" dataset is a collection of chat logs between users and the ChatGPT model. These conversations have been shared by users on the "Collective Cognition" website. The dataset provides insights into user interactions with language models and can be utilized for multiple purposes, including training, research, and analysis.
Visit Collective Cognition to browse or contribute chats here: https://collectivecognition.ai
### Supported Tasks
- **Language Model Finetuning**: Training through behavior cloning and knowledge instilling.
- **Dialogue Understanding**: Analyzing the flow and structure of conversations.
- **Sentiment Analysis**: Understanding the sentiment or emotion in the dialogues.
- **Topic Modeling**: Determining the topics discussed within the chats.
### Languages
The primary language of the dataset is English, but any language chat may be present in the dataset as users share more chats.
## Dataset Structure
### Data Instances
A typical data instance includes a chat log with a title, model name, whether the chat used custom instructions (currently not included if so), and the content of the message with the role of the sender (either "human" or "gpt").
The dataset is formatted to conform with ShareGPT standard.
This is an example entry in the dataset, noting that turns are variable and can be many or as few as two:
```json
{
"title": "Interest Formulas Clarification",
"model_name": "GPT-4",
"custom_instruction": false,
"conversations": [
{
"from": "human",
"value": "Is this correct? The formula for calculating simple interest (also known as Compound Interest) is given by I = P * r^n, where \n- I represents the final amount of money, also called principal or capital. This value will keep on increasing at a fixed rate per unit time denoted by 'r' and number of times denoted by 'n'. The initial investment represented by 'P', while the percentage rate expressed as an annual fraction, i.e., p/100 representing the percentage to be paid annually. In other words it\u2019s how much you earn in one year out of your original $100 invested with a 5% annual return compounded monthly."
},
{
"from": "assistant",
"value": "I'm sorry, but there seems to be some confusion in your question. The formula given and the definitions of the variables seem to be mixed up between simple interest and compound interest. Let me clarify:\n\n1. Simple Interest: This type of interest is calculated only on the original amount (principal) that you invested or loaned. The formula for simple interest is I = PRT, where:\n - I is the interest\n - P is the principal amount (initial investment)\n - R is the annual interest rate (in decimal form, so a 5% rate would be 0.05)\n - T is the time the money is invested or borrowed for, in years.\n\n2. Compound Interest: This type of interest is calculated on the initial principal and also on the accumulated interest of previous periods of a deposit or loan. The formula for compound interest is A = P(1 + r/n)^(nt), where:\n - A is the amount of money accumulated after n years, including interest.\n - P is the principal amount (the initial amount of money)\n - r is the annual interest rate (in decimal form, so a 5% rate would be 0.05)\n - n is the number of times that interest is compounded per year\n - t is the number of years the money is invested or borrowed for.\n\nIn both cases, the interest rate is usually expressed as an annual rate, regardless of the length of the term."
}
]
}
```
### Data Splits
Currently, the dataset is not divided into specific splits (train, test, validation).
## Dataset Creation
### Curation Rationale
The dataset was curated to provide insights into how users interact with language models and to contribute to the broader NLP community's resources.
### Source Data
The data originates from user contributions on the "Collective Cognition" website.
### Personal and Sensitive Information
All chats uploaded to the Collective Cognition website are made public, and are uploaded as a new dataset periodically. If you would like to have your chat removed, please email admin@collectivecognition.ai
## Considerations for Using the Data
### Social Impact of Dataset
The dataset offers a glimpse into the interaction dynamics between humans and AI models. It can be instrumental for researchers studying human-AI collaboration.
### Discussion of Biases
There might be biases in the dataset based on the types of users contributing chat logs and the topics they discuss with ChatGPT, particularly centered around what users may utilize ChatGPT for the most.
### Other Known Limitations
The dataset is dependent on the voluntary contributions of users. Hence, it might not represent the entire spectrum of interactions that users have with ChatGPT.
## Additional Information
### Licensing Information
MIT | 6,119 | [
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] |
hmao/reformatted_multiapi | 2023-10-23T21:44:44.000Z | [
"region:us"
] | hmao | null | null | 0 | 221 | 2023-10-23T21:44:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: api_name
dtype: string
- name: api_definition
dtype: string
- name: dataset_name
dtype: string
splits:
- name: train
num_bytes: 27030
num_examples: 46
download_size: 13704
dataset_size: 27030
---
# Dataset Card for "reformatted_multiapi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 528 | [
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ghadeermobasher/BC5CDR-Chemical-Disease | 2022-01-25T10:31:51.000Z | [
"region:us"
] | ghadeermobasher | \ | @article{krallinger2015chemdner,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Krallinger, Martin and Rabal, Obdulia and Leitner, Florian and Vazquez, Miguel and Salgado, David and Lu, Zhiyong and Leaman, Robert and Lu, Yanan and Ji, Donghong and Lowe, Daniel M and others},
journal={Journal of cheminformatics},
volume={7},
number={1},
pages={1--17},
year={2015},
publisher={BioMed Central}
} | 4 | 220 | 2022-03-02T23:29:22 | annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: bc4chemd
pretty_name: BC4CHEMD
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- structure-prediction
task_ids:
- named-entity-recognition
# Dataset Card for BC4CHEMD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:**
https://biocreative.bioinformatics.udel.edu/tasks/biocreative-v/track-3-cdr/
- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD
- **Paper:** BioCreative V CDR task corpus: a resource for chemical disease relation extraction
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Zhiyong Lu] (mailto: Zhiyong.Lu@nih.gov)
### Dataset Summary
A corpus for both named entity recognition and chemical-disease relations in the literature. A total of 1500 articles have been annotated with automated assistance from PubTator. Jaccard agreement results and corpus statistics verified the reliability of the corpus.
### Supported Tasks and Leaderboards
named-entity-recognition
### Languages
en
## Dataset Structure
### Data Instances
Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset:
{
'tokens': ['DPP6','as','a','candidate','gene','for','neuroleptic','-','induced','tardive','dyskinesia','.']
, 'ner_tags': [0,0,0,0,0,0,0,0,0,0,0,0],
'id': '0'
}
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a chemical and `2` the subsequent chemical tokens.
### Data Splits
The data is split into a train (3500 instances), validation (3500 instances) and test set (3000 instances).
## Dataset Creation
### Curation Rationale
The goal of the dataset consists on improving the state-of-the-art in chemical name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
### Source Data
#### Initial Data Collection and Normalization
The dataset consists on abstracts extracted from PubMed.
#### Who are the source language producers?
The source language producers are the authors of publication abstracts hosted in PubMed.
### Annotations
#### Annotation process
The curators were trained to mark up the text according to the labels specified in the guidelines. The raw text was not tokenized prior to the annotation and only the title was distinguished from the PubMed abstract. The selection of text spans was done at the character level, they did not allow nested annotations and distinct entity mentions should not overlap. Each text span was selected according to the annotation guidelines and classified manually into one of the CEM classes.
#### Who are the annotators?
The group of curators used for preparing the annotations was composed mainly of organic chemistry postgraduates with an average experience of 3-4 years in the annotation of chemical names and chemical structures.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.
### Discussion of Biases
The used CHEMDNER document set had to be representative and balanced in order to reflect the kind of documents that might mention the entity of interest.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | 4,939 | [
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ChristophSchuhmann/improved_aesthetics_6plus | 2022-08-10T11:30:40.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 23 | 220 | 2022-08-10T11:29:49 | Entry not found | 15 | [
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nasa-cisto-data-science-group/modis-lake-powell-toy-dataset | 2023-05-04T01:39:33.000Z | [
"size_categories:n<1K",
"license:apache-2.0",
"region:us"
] | nasa-cisto-data-science-group | null | null | 0 | 220 | 2023-03-09T14:45:40 | ---
license: apache-2.0
size_categories:
- n<1K
---
# MODIS Water Lake Powell Toy Dataset
### Dataset Summary
Tabular dataset comprised of MODIS surface reflectance bands along with calculated indices and a label (water/not-water)
## Dataset Structure
### Data Fields
- `water`: Label, water or not-water (binary)
- `sur_refl_b01_1`: MODIS surface reflection band 1 (-100, 16000)
- `sur_refl_b02_1`: MODIS surface reflection band 2 (-100, 16000)
- `sur_refl_b03_1`: MODIS surface reflection band 3 (-100, 16000)
- `sur_refl_b04_1`: MODIS surface reflection band 4 (-100, 16000)
- `sur_refl_b05_1`: MODIS surface reflection band 5 (-100, 16000)
- `sur_refl_b06_1`: MODIS surface reflection band 6 (-100, 16000)
- `sur_refl_b07_1`: MODIS surface reflection band 7 (-100, 16000)
- `ndvi`: Normalized differential vegetation index (-20000, 20000)
- `ndwi1`: Normalized differential water index 1 (-20000, 20000)
- `ndwi2`: Normalized differential water index 2 (-20000, 20000)
### Data Splits
Train and test split. Test is 200 rows, train is 800.
## Dataset Creation
## Source Data
[MODIS MOD44W](https://lpdaac.usgs.gov/products/mod44wv006/)
[MODIS MOD09GA](https://lpdaac.usgs.gov/products/mod09gav006/)
[MODIS MOD09GQ](https://lpdaac.usgs.gov/products/mod09gqv006/)
## Annotation process
Labels were created by using the MOD44W C6 product to designate pixels in MODIS surface reflectance products as land or water. | 1,429 | [
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FreedomIntelligence/alpaca-gpt4-korean | 2023-08-06T08:10:43.000Z | [
"region:us"
] | FreedomIntelligence | null | null | 1 | 220 | 2023-06-26T08:18:44 | The dataset is used in the research related to [MultilingualSIFT](https://github.com/FreedomIntelligence/MultilingualSIFT). | 124 | [
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charlie8522/Totto_testing | 2023-09-24T14:42:33.000Z | [
"region:us"
] | charlie8522 | null | null | 0 | 220 | 2023-09-22T07:11:31 | Entry not found | 15 | [
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] |
nell | 2023-06-01T14:59:50.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"relation-extraction",
"text-to-structured",
"text-to-tabular",
"region:us"
] | null | This dataset provides version 1115 of the belief
extracted by CMU's Never Ending Language Learner (NELL) and version
1110 of the candidate belief extracted by NELL. See
http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information
extraction system that attempts to read the Clueweb09 of 500 million
web pages (http://boston.lti.cs.cmu.edu/Data/clueweb09/) and general
web searches.
The dataset has 4 configurations: nell_belief, nell_candidate,
nell_belief_sentences, and nell_candidate_sentences. nell_belief is
certainties of belief are lower. The two sentences config extracts the
CPL sentence patterns filled with the applicable 'best' literal string
for the entities filled into the sentence patterns. And also provides
sentences found using web searches containing the entities and
relationships.
There are roughly 21M entries for nell_belief_sentences, and 100M
sentences for nell_candidate_sentences. | @inproceedings{mitchell2015,
added-at = {2015-01-27T15:35:24.000+0100},
author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.},
biburl = {https://www.bibsonomy.org/bibtex/263070703e6bb812852cca56574aed093/hotho},
booktitle = {AAAI},
description = {Papers by William W. Cohen},
interhash = {52d0d71f6f5b332dabc1412f18e3a93d},
intrahash = {63070703e6bb812852cca56574aed093},
keywords = {learning nell ontology semantic toread},
note = {: Never-Ending Learning in AAAI-2015},
timestamp = {2015-01-27T15:35:24.000+0100},
title = {Never-Ending Learning},
url = {http://www.cs.cmu.edu/~wcohen/pubs.html},
year = 2015
} | 3 | 219 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- entity-linking-retrieval
- fact-checking-retrieval
paperswithcode_id: nell
pretty_name: Never Ending Language Learning (NELL)
tags:
- relation-extraction
- text-to-structured
- text-to-tabular
dataset_info:
- config_name: nell_belief
features:
- name: entity
dtype: string
- name: relation
dtype: string
- name: value
dtype: string
- name: iteration_of_promotion
dtype: string
- name: score
dtype: string
- name: source
dtype: string
- name: entity_literal_strings
dtype: string
- name: value_literal_strings
dtype: string
- name: best_entity_literal_string
dtype: string
- name: best_value_literal_string
dtype: string
- name: categories_for_entity
dtype: string
- name: categories_for_value
dtype: string
- name: candidate_source
dtype: string
splits:
- name: train
num_bytes: 4592559704
num_examples: 2766079
download_size: 929107246
dataset_size: 4592559704
- config_name: nell_candidate
features:
- name: entity
dtype: string
- name: relation
dtype: string
- name: value
dtype: string
- name: iteration_of_promotion
dtype: string
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dtype: string
- name: value_literal_strings
dtype: string
- name: best_entity_literal_string
dtype: string
- name: best_value_literal_string
dtype: string
- name: categories_for_entity
dtype: string
- name: categories_for_value
dtype: string
- name: candidate_source
dtype: string
splits:
- name: train
num_bytes: 23497433060
num_examples: 32687353
download_size: 2687057812
dataset_size: 23497433060
- config_name: nell_belief_sentences
features:
- name: entity
dtype: string
- name: relation
dtype: string
- name: value
dtype: string
- name: score
dtype: string
- name: sentence
dtype: string
- name: count
dtype: int32
- name: url
dtype: string
- name: sentence_type
dtype: string
splits:
- name: train
num_bytes: 4459368426
num_examples: 21031531
download_size: 929107246
dataset_size: 4459368426
- config_name: nell_candidate_sentences
features:
- name: entity
dtype: string
- name: relation
dtype: string
- name: value
dtype: string
- name: score
dtype: string
- name: sentence
dtype: string
- name: count
dtype: int32
- name: url
dtype: string
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dtype: string
splits:
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num_bytes: 20058197787
num_examples: 100866414
download_size: 2687057812
dataset_size: 20058197787
config_names:
- nell_belief
- nell_belief_sentences
- nell_candidate
- nell_candidate_sentences
---
# Dataset Card for Never Ending Language Learning (NELL)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://rtw.ml.cmu.edu/rtw/
- **Repository:**
http://rtw.ml.cmu.edu/rtw/
- **Paper:**
Never-Ending Learning.
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, J. Welling. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2015
### Dataset Summary
This dataset provides version 1115 of the belief
extracted by CMU's Never Ending Language Learner (NELL) and version
1110 of the candidate belief extracted by NELL. See
http://rtw.ml.cmu.edu/rtw/overview. NELL is an open information
extraction system that attempts to read the Clueweb09 of 500 million
web pages (http://boston.lti.cs.cmu.edu/Data/clueweb09/) and general
web searches.
The dataset has 4 configurations: nell_belief, nell_candidate,
nell_belief_sentences, and nell_candidate_sentences. nell_belief is
certainties of belief are lower. The two sentences config extracts the
CPL sentence patterns filled with the applicable 'best' literal string
for the entities filled into the sentence patterns. And also provides
sentences found using web searches containing the entities and
relationships.
There are roughly 21M entries for nell_belief_sentences, and 100M
sentences for nell_candidate_sentences.
From the NELL website:
- **Research Goal**
To build a never-ending machine learning system that acquires the ability to extract structured information from unstructured web pages. If successful, this will result in a knowledge base (i.e., a relational database) of structured information that mirrors the content of the Web. We call this system NELL (Never-Ending Language Learner).
- **Approach**
The inputs to NELL include (1) an initial ontology defining hundreds of categories (e.g., person, sportsTeam, fruit, emotion) and relations (e.g., playsOnTeam(athlete,sportsTeam), playsInstrument(musician,instrument)) that NELL is expected to read about, and (2) 10 to 15 seed examples of each category and relation.
Given these inputs, plus a collection of 500 million web pages and access to the remainder of the web through search engine APIs, NELL runs 24 hours per day, continuously, to perform two ongoing tasks:
Extract new instances of categories and relations. In other words, find noun phrases that represent new examples of the input categories (e.g., "Barack Obama" is a person and politician), and find pairs of noun phrases that correspond to instances of the input relations (e.g., the pair "Jason Giambi" and "Yankees" is an instance of the playsOnTeam relation). These new instances are added to the growing knowledge base of structured beliefs.
Learn to read better than yesterday. NELL uses a variety of methods to extract beliefs from the web. These are retrained, using the growing knowledge base as a self-supervised collection of training examples. The result is a semi-supervised learning method that couples the training of hundreds of different extraction methods for a wide range of categories and relations. Much of NELL’s current success is due to its algorithm for coupling the simultaneous training of many extraction methods.
For more information, see: http://rtw.ml.cmu.edu/rtw/resources
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
en, and perhaps some others
## Dataset Structure
### Data Instances
There are four configurations for the dataset: nell_belief, nell_candidate, nell_belief_sentences, nell_candidate_sentences.
nell_belief and nell_candidate defines:
``
{'best_entity_literal_string': 'Aspect Medical Systems',
'best_value_literal_string': '',
'candidate_source': '%5BSEAL-Iter%3A215-2011%2F02%2F26-04%3A27%3A09-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-From%3ACategory%3Abiotechcompany-using-KB+http%3A%2F%2Fwww.unionegroup.com%2Fhealthcare%2Fmfg_info.htm+http%3A%2F%2Fwww.conventionspc.com%2Fcompanies.html%2C+CPL-Iter%3A1103-2018%2F03%2F08-15%3A32%3A34-%3Ctoken%3Daspect_medical_systems%2Cbiotechcompany%3E-grant+support+from+_%092%09research+support+from+_%094%09unrestricted+educational+grant+from+_%092%09educational+grant+from+_%092%09research+grant+support+from+_%091%09various+financial+management+positions+at+_%091%5D',
'categories_for_entity': 'concept:biotechcompany',
'categories_for_value': 'concept:company',
'entity': 'concept:biotechcompany:aspect_medical_systems',
'entity_literal_strings': '"Aspect Medical Systems" "aspect medical systems"',
'iteration_of_promotion': '1103',
'relation': 'generalizations',
'score': '0.9244426550775064',
'source': 'MBL-Iter%3A1103-2018%2F03%2F18-01%3A35%3A42-From+ErrorBasedIntegrator+%28SEAL%28aspect_medical_systems%2Cbiotechcompany%29%2C+CPL%28aspect_medical_systems%2Cbiotechcompany%29%29',
'value': 'concept:biotechcompany',
'value_literal_strings': ''}
``
nell_belief_sentences, nell_candidate_sentences defines:
``
{'count': 4,
'entity': 'biotechcompany:aspect_medical_systems',
'relation': 'generalizations',
'score': '0.9244426550775064',
'sentence': 'research support from [[ Aspect Medical Systems ]]',
'sentence_type': 'CPL',
'url': '',
'value': 'biotechcompany'}
``
### Data Fields
For nell_belief and nell_canddiate configurations. From http://rtw.ml.cmu.edu/rtw/faq:
* entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept
* relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation.
* value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity).
* iteration_of_promotion: The point in NELL's life at which this category or relation instance was promoted to one that NELL beleives to be true. This is a non-negative integer indicating the number of iterations of bootstrapping NELL had gone through.
* score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time.
* source: A summary of the provenance for the belief indicating the set of learning subcomponents (CPL, SEAL, etc.) that had submitted this belief as being potentially true.
* entity_literal_strings: The set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Entity column.
* value_literal_strings: For relations, the set of actual textual strings that NELL has read that it believes can refer to the concept indicated in the Value column. For categories, this should be empty but may contain something spurious.
* best_entity_literal_string: Of the set of strings in the Entity literalStrings, column, which one string can best be used to describe the concept.
* best_value_literal_string: Same thing, but for Value literalStrings.
* categories_for_entity: The full set of categories (which may be empty) to which NELL belives the concept indicated in the Entity column to belong.
* categories_for_value: For relations, the full set of categories (which may be empty) to which NELL believes the concept indicated in the Value column to belong. For categories, this should be empty but may contain something spurious.
* candidate_source: A free-form amalgamation of more specific provenance information describing the justification(s) NELL has for possibly believing this category or relation instance.
For the nell_belief_sentences and nell_candidate_sentences, we have extracted the underlying sentences, sentence count and URLs and provided a shortened version of the entity, relation and value field by removing the string "concept:" and "candidate:". There are two types of sentences, 'CPL' and 'OE', which are generated by two of the modules of NELL, pattern matching and open web searching, respectively. There may be duplicates. The configuration is as follows:
* entity: The Entity part of the (Entity, Relation, Value) tripple. Note that this will be the name of a concept and is not the literal string of characters seen by NELL from some text source, nor does it indicate the category membership of that concept
* relation: The Relation part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be "generalizations". In the case of a relation instance, this will be the name of the relation.
* value: The Value part of the (Entity, Relation, Value) tripple. In the case of a category instance, this will be the name of the category. In the case of a relation instance, this will be another concept (like Entity).
* score: A confidence score for the belief. Note that NELL's scores are not actually probabilistic at this time.
* sentence: the raw sentence. For 'CPL' type sentences, there are "[[" "]]" arounds the entity and value. For 'OE' type sentences, there are no "[[" and "]]".
* url: the url if there is one from which this sentence was extracted
* count: the count for this sentence
* sentence_type: either 'CPL' or 'OE'
### Data Splits
There are no splits.
## Dataset Creation
### Curation Rationale
This dataset was gathered and created over many years of running the NELL system on web data.
### Source Data
#### Initial Data Collection and Normalization
See the research paper on NELL. NELL searches a subset of the web
(Clueweb09) and the open web using various open information extraction
algorithms, including pattern matching.
#### Who are the source language producers?
The NELL authors at Carnegie Mellon Univiersty and data from Cluebweb09 and the open web.
### Annotations
#### Annotation process
The various open information extraction modules of NELL.
#### Who are the annotators?
Machine annotated.
### Personal and Sensitive Information
Unkown, but likely there are names of famous individuals.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to help machines learn to read and understand the web.
### Discussion of Biases
Since the data is gathered from the web, there is likely to be biased text and relationships.
[More Information Needed]
### Other Known Limitations
The relationships and concepts gathered from NELL are not 100% accurate, and there could be errors (maybe as high as 30% error).
See https://en.wikipedia.org/wiki/Never-Ending_Language_Learning
We did not 'tag' the entity and value in the 'OE' sentences, and this might be an extension in the future.
## Additional Information
### Dataset Curators
The authors of NELL at Carnegie Mellon Univeristy
### Licensing Information
There does not appear to be a license on http://rtw.ml.cmu.edu/rtw/resources. The data is made available by CMU on the web.
### Citation Information
@inproceedings{mitchell2015,
added-at = {2015-01-27T15:35:24.000+0100},
author = {Mitchell, T. and Cohen, W. and Hruscha, E. and Talukdar, P. and Betteridge, J. and Carlson, A. and Dalvi, B. and Gardner, M. and Kisiel, B. and Krishnamurthy, J. and Lao, N. and Mazaitis, K. and Mohammad, T. and Nakashole, N. and Platanios, E. and Ritter, A. and Samadi, M. and Settles, B. and Wang, R. and Wijaya, D. and Gupta, A. and Chen, X. and Saparov, A. and Greaves, M. and Welling, J.},
biburl = {https://www.bibsonomy.org/bibtex/263070703e6bb812852cca56574aed093/hotho},
booktitle = {AAAI},
description = {Papers by William W. Cohen},
interhash = {52d0d71f6f5b332dabc1412f18e3a93d},
intrahash = {63070703e6bb812852cca56574aed093},
keywords = {learning nell ontology semantic toread},
note = {: Never-Ending Learning in AAAI-2015},
timestamp = {2015-01-27T15:35:24.000+0100},
title = {Never-Ending Learning},
url = {http://www.cs.cmu.edu/~wcohen/pubs.html},
year = 2015
}
### Contributions
Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. | 16,347 | [
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scikit-learn/iris | 2022-06-20T14:17:01.000Z | [
"license:cc0-1.0",
"region:us"
] | scikit-learn | null | null | 0 | 219 | 2022-06-20T14:10:10 | ---
license: cc0-1.0
---
## Iris Species Dataset
The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.
It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.
The dataset is taken from [UCI Machine Learning Repository's Kaggle](https://www.kaggle.com/datasets/uciml/iris).
The following description is taken from UCI Machine Learning Repository.
This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.
Predicted attribute: class of iris plant.
This is an exceedingly simple domain.
This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.
Features in this dataset are the following:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris Setosa
- Iris Versicolour
- Iris Virginica
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] |
asapp/slue-phase-2 | 2023-08-01T16:05:43.000Z | [
"arxiv:2212.10525",
"region:us"
] | asapp | Spoken Language Understanding Evaluation (SLUE) benchmark Phase 2. | @inproceedings{shon2023slue_phase2,
title={SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks},
author={Shon, Suwon and Arora, Siddhant and Lin, Chyi-Jiunn and Pasad, Ankita and Wu, Felix and Sharma, Roshan and Wu, Wei-Lun and Lee, Hung-Yi and Livescu, Karen and Watanabe, Shinji},
booktitle={ACL},
year={2023},
} | 4 | 219 | 2023-05-31T04:10:08 |
### Dataset description
**(Jul. 11 2023) Detail information will released soon.**
- **Toolkit Repository:** [https://github.com/asappresearch/slue-toolkit/](https://github.com/asappresearch/slue-toolkit/)
- **Paper:** [https://arxiv.org/abs/2212.10525](https://arxiv.org/abs/2212.10525)
### Licensing Information
#### SLUE-HVB
SLUE-HVB dataset contains a subset of the Gridspace-Stanford Harper Valley speech dataset and the copyright of this subset remains the same with the original license, CC-BY-4.0. See also original license notice (https://github.com/cricketclub/gridspace-stanford-harper-valley/blob/master/LICENSE)
Additionally, we provide dialog act classification annotation and it is covered with the same license as CC-BY-4.0.
#### SLUE-SQA-5
SLUE-SQA-5 Dataset contains question texts and answer strings (question_text, normalized_question_text, and answer_spans column in .tsv files) from these datasets,
* SQuAD1.1 (for questions whose question_id starts with ‘squad-’)
* Natural Questions (for questions whose question_id starts with ‘nq-’)
* WebQuestions (for questions whose question_id starts with ‘wq-’)
* CuratedTREC (for questions whose question_id starts with ‘trec-’)
* TriviaQA (for questions whose question_id starts with ‘triviaqa-’)
Additionally, we provide audio recordings (.wav files in “question” directories) of these questions.
For questions from TriviaQA (questions whose question_id starts with ‘triviaqa-’), their question texts, answer strings, and audio recordings are licensed with the same Apache License 2.0 as TriviaQA (for more detail, please refer to https://github.com/mandarjoshi90/triviaqa/blob/master/LICENSE).
For questions from the other 4 datasets, their question texts, answer strings, and audio recordings are licensed with Creative Commons Attribution-ShareAlike 4.0 International license.
SLUE-SQA-5 also contains a subset of Spoken Wikipedia, including the audios placed in “document” directories and their transcripts (document_text and normalized_document_text column in .tsv files). Additionally, we provide the text-to-speech alignments (.txt files in “word2time” directories).These contents are licensed with the same Creative Commons (CC BY-SA 4.0) license as Spoken Wikipedia.
#### SLUE-TED
SLUE-TED Dataset contains TED Talk audios along with the associated abstracts and title, which were concatenated to create reference summaries. This corpus is licensed with the same Creative Commons (CC BY–NC–ND 4.0 International) license as TED talks. For further information, please refer to the details provided below.
=============================
TED.com
We encourage you to share TED Talks, under our Creative Commons license, or ( CC BY–NC–ND 4.0 International, which means it may be shared under the conditions below:
CC: means the type of license rights associated with TED Talks, or Creative Commons
BY: means the requirement to include an attribution to TED as the owner of the TED Talk and include a link to the talk, but do not include any other TED branding on your website or platform, or language that may imply an endorsement.
NC: means you cannot use TED Talks in any commercial context or to gain any type of revenue, payment or fee from the license sublicense, access or usage of TED Talks in an app of any kind for any advertising, or in exchange for payment of any kind, including in any ad supported content or format.
ND: means that no derivative works are permitted so you cannot edit, remix, create, modify or alter the form of the TED Talks in any way. This includes using the TED Talks as the basis for another work, including dubbing, voice-overs, or other translations not authorized by TED. You may not add any more restrictions that we have placed on the TED site content, such as additional legal or technological restrictions on accessing the content.
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] |
C-MTEB/CLSClusteringS2S | 2023-07-27T17:29:54.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 219 | 2023-07-27T17:29:48 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6895612
num_examples: 10
download_size: 4483035
dataset_size: 6895612
---
# Dataset Card for "CLSClusteringS2S"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 485 | [
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] |
C-MTEB/LCQMC | 2023-07-28T13:51:45.000Z | [
"region:us"
] | C-MTEB | null | null | 2 | 219 | 2023-07-28T13:51:20 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int32
splits:
- name: train
num_bytes: 18419299
num_examples: 238766
- name: validation
num_bytes: 760701
num_examples: 8802
- name: test
num_bytes: 876457
num_examples: 12500
download_size: 14084841
dataset_size: 20056457
---
# Dataset Card for "LCQMC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 729 | [
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] |
wi_locness | 2023-06-01T14:59:47.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"multilinguality:other-language-learner",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:other",
"grammatical-error-correction",
"region:us"
] | null | Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native
English students with their writing. Specifically, students from around the world submit letters,
stories, articles and essays in response to various prompts, and the W&I system provides instant
feedback. Since W&I went live in 2014, W&I annotators have manually annotated some of these
submissions and assigned them a CEFR level. | @inproceedings{bryant-etal-2019-bea,
title = "The {BEA}-2019 Shared Task on Grammatical Error Correction",
author = "Bryant, Christopher and
Felice, Mariano and
Andersen, {\\O}istein E. and
Briscoe, Ted",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-4406",
doi = "10.18653/v1/W19-4406",
pages = "52--75",
abstract = "This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write{\\&}Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F{\\_}0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.",
} | 7 | 218 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
- other-language-learner
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: locness-corpus
pretty_name: Cambridge English Write & Improve + LOCNESS
tags:
- grammatical-error-correction
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: userid
dtype: string
- name: cefr
dtype: string
- name: text
dtype: string
- name: edits
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 4375795
num_examples: 3000
- name: validation
num_bytes: 447055
num_examples: 300
download_size: 6120469
dataset_size: 4822850
- config_name: wi
features:
- name: id
dtype: string
- name: userid
dtype: string
- name: cefr
dtype: string
- name: text
dtype: string
- name: edits
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 4375795
num_examples: 3000
- name: validation
num_bytes: 447055
num_examples: 300
download_size: 6120469
dataset_size: 4822850
- config_name: locness
features:
- name: id
dtype: string
- name: cefr
dtype: string
- name: text
dtype: string
- name: edits
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: text
dtype: string
splits:
- name: validation
num_bytes: 138176
num_examples: 50
download_size: 6120469
dataset_size: 138176
config_names:
- locness
- wi
---
# Dataset Card for Cambridge English Write & Improve + LOCNESS Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.cl.cam.ac.uk/research/nl/bea2019st/#data
- **Repository:**
- **Paper:** https://www.aclweb.org/anthology/W19-4406/
- **Leaderboard:** https://competitions.codalab.org/competitions/20228#results
- **Point of Contact:**
### Dataset Summary
Write & Improve (Yannakoudakis et al., 2018) is an online web platform that assists non-native English students with their writing. Specifically, students from around the world submit letters, stories, articles and essays in response to various prompts, and the W&I system provides instant feedback. Since W&I went live in 2014, W&I annotators have manually annotated some of these submissions and assigned them a CEFR level.
The LOCNESS corpus (Granger, 1998) consists of essays written by native English students. It was originally compiled by researchers at the Centre for English Corpus Linguistics at the University of Louvain. Since native English students also sometimes make mistakes, we asked the W&I annotators to annotate a subsection of LOCNESS so researchers can test the effectiveness of their systems on the full range of English levels and abilities.
### Supported Tasks and Leaderboards
Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in text; e.g. [I follows his advices -> I followed his advice]. It can be used to not only help language learners improve their writing skills, but also alert native speakers to accidental mistakes or typos.
The aim of the task of this dataset is to correct all types of errors in written text. This includes grammatical, lexical and orthographical errors.
The following Codalab competition contains the latest leaderboard, along with information on how to submit to the withheld W&I+LOCNESS test set: https://competitions.codalab.org/competitions/20228
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
An example from the `wi` configuration:
```
{
'id': '1-140178',
'userid': '21251',
'cefr': 'A2.i',
'text': 'My town is a medium size city with eighty thousand inhabitants. It has a high density population because its small territory. Despite of it is an industrial city, there are many shops and department stores. I recommend visiting the artificial lake in the certer of the city which is surrounded by a park. Pasteries are very common and most of them offer the special dessert from the city. There are a comercial zone along the widest street of the city where you can find all kind of establishments: banks, bars, chemists, cinemas, pet shops, restaurants, fast food restaurants, groceries, travel agencies, supermarkets and others. Most of the shops have sales and offers at least three months of the year: January, June and August. The quality of the products and services are quite good, because there are a huge competition, however I suggest you taking care about some fakes or cheats.',
'edits': {
'start': [13, 77, 104, 126, 134, 256, 306, 375, 396, 402, 476, 484, 579, 671, 774, 804, 808, 826, 838, 850, 857, 862, 868],
'end': [24, 78, 104, 133, 136, 262, 315, 379, 399, 411, 480, 498, 588, 671, 777, 807, 810, 835, 845, 856, 861, 867, 873],
'text': ['medium-sized', '-', ' of', 'Although', '', 'center', None, 'of', 'is', 'commercial', 'kinds', 'businesses', 'grocers', ' in', 'is', 'is', '', '. However,', 'recommend', 'be', 'careful', 'of', '']
}
}
```
An example from the `locness` configuration:
```
{
'id': '7-5819177',
'cefr': 'N',
'text': 'Boxing is a common, well known and well loved sport amongst most countries in the world however it is also punishing, dangerous and disliked to the extent that many people want it banned, possibly with good reason.\nBoxing is a dangerous sport, there are relatively common deaths, tragic injuries and even disease. All professional boxers are at risk from being killed in his next fight. If not killed then more likely paralysed. There have been a number of cases in the last ten years of the top few boxers having tragic losses throughout their ranks. This is just from the elite few, and theres more from those below them.\nMore deaths would occur through boxing if it were banned. The sport would go underground, there would be no safety measures like gloves, a doctor, paramedics or early stopping of the fight if someone looked unable to continue. With this going on the people taking part will be dangerous, and on the streets. Dangerous dogs who were trained to kill and maim in similar underound dog fights have already proved deadly to innocent people, the new boxers could be even more at risk.\nOnce boxing is banned and no-one grows up knowing it as acceptable there will be no interest in boxing and hopefully less all round interest in violence making towns and cities much safer places to live in, there will be less fighting outside pubs and clubs and less violent attacks with little or no reason.\nchange the rules of boxing slightly would much improve the safety risks of the sport and not detract form the entertainment. There are all sorts of proposals, lighter and more cushioning gloves could be worn, ban punches to the head, headguards worn or make fights shorter, as most of the serious injuries occur in the latter rounds, these would all show off the boxers skill and tallent and still be entertaining to watch.\nEven if a boxer is a success and manages not to be seriously hurt he still faces serious consequences in later life diseases that attack the brains have been known to set in as a direct result of boxing, even Muhamed Ali, who was infamous(?) both for his boxing and his quick-witted intelligence now has Alzheimer disease and can no longer do many everyday acts.\nMany other sports are more dangerous than boxing, motor sports and even mountaineering has risks that are real. Boxers chose to box, just as racing drivers drive.',
'edits': {
'start': [24, 39, 52, 87, 242, 371, 400, 528, 589, 713, 869, 992, 1058, 1169, 1209, 1219, 1255, 1308, 1386, 1412, 1513, 1569, 1661, 1731, 1744, 1781, 1792, 1901, 1951, 2038, 2131, 2149, 2247, 2286],
'end': [25, 40, 59, 95, 249, 374, 400, 538, 595, 713, 869, 1001, 1063, 1169, 1209, 1219, 1255, 1315, 1390, 1418, 1517, 1570, 1661, 1737, 1751, 1781, 1799, 1901, 1960, 2044, 2131, 2149, 2248, 2289],
'text': ['-', '-', 'in', '. However,', '. There', 'their', ',', 'among', "there's", ' and', ',', 'underground', '. The', ',', ',', ',', ',', '. There', 'for', 'Changing', 'from', ';', ',', 'later', '. These', "'", 'talent', ',', '. Diseases', '. Even', ',', "'s", ';', 'have']
}
}
```
### Data Fields
The fields of the dataset are:
- `id`: the id of the text as a string
- `cefr`: the [CEFR level](https://www.cambridgeenglish.org/exams-and-tests/cefr/) of the text as a string
- `userid`: id of the user
- `text`: the text of the submission as a string
- `edits`: the edits from W&I:
- `start`: start indexes of each edit as a list of integers
- `end`: end indexes of each edit as a list of integers
- `text`: the text content of each edit as a list of strings
- `from`: the original text of each edit as a list of strings
### Data Splits
| name |train|validation|
|----------|----:|---------:|
| wi | 3000| 300|
| locness | N/A| 50|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Write & Improve License:
```
Cambridge English Write & Improve (CEWI) Dataset Licence Agreement
1. By downloading this dataset and licence, this licence agreement is
entered into, effective this date, between you, the Licensee, and the
University of Cambridge, the Licensor.
2. Copyright of the entire licensed dataset is held by the Licensor.
No ownership or interest in the dataset is transferred to the
Licensee.
3. The Licensor hereby grants the Licensee a non-exclusive
non-transferable right to use the licensed dataset for
non-commercial research and educational purposes.
4. Non-commercial purposes exclude without limitation any use of the
licensed dataset or information derived from the dataset for or as
part of a product or service which is sold, offered for sale,
licensed, leased or rented.
5. The Licensee shall acknowledge use of the licensed dataset in all
publications of research based on it, in whole or in part, through
citation of the following publication:
Helen Yannakoudakis, Øistein E. Andersen, Ardeshir Geranpayeh,
Ted Briscoe and Diane Nicholls. 2018. Developing an automated writing
placement system for ESL learners. Applied Measurement in Education.
6. The Licensee may publish excerpts of less than 100 words from the
licensed dataset pursuant to clause 3.
7. The Licensor grants the Licensee this right to use the licensed dataset
"as is". Licensor does not make, and expressly disclaims, any express or
implied warranties, representations or endorsements of any kind
whatsoever.
8. This Agreement shall be governed by and construed in accordance with
the laws of England and the English courts shall have exclusive
jurisdiction.
```
LOCNESS License:
```
LOCNESS Dataset Licence Agreement
1. The corpus is to be used for non-commercial purposes only
2. All publications on research partly or wholly based on the corpus should give credit to the Centre for English Corpus Linguistics (CECL), Université catholique de Louvain, Belgium. A scanned copy or offprint of the publication should also be sent to <sylviane.granger@uclouvain.be>.
3. No part of the corpus is to be distributed to a third party without specific authorization from CECL. The corpus can only be used by the person agreeing to the licence terms and researchers working in close collaboration with him/her or students under his/her supervision, attached to the same institution, within the framework of the research project.
```
### Citation Information
```
@inproceedings{bryant-etal-2019-bea,
title = "The {BEA}-2019 Shared Task on Grammatical Error Correction",
author = "Bryant, Christopher and
Felice, Mariano and
Andersen, {\O}istein E. and
Briscoe, Ted",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-4406",
doi = "10.18653/v1/W19-4406",
pages = "52--75",
abstract = "This paper reports on the BEA-2019 Shared Task on Grammatical Error Correction (GEC). As with the CoNLL-2014 shared task, participants are required to correct all types of errors in test data. One of the main contributions of the BEA-2019 shared task is the introduction of a new dataset, the Write{\&}Improve+LOCNESS corpus, which represents a wider range of native and learner English levels and abilities. Another contribution is the introduction of tracks, which control the amount of annotated data available to participants. Systems are evaluated in terms of ERRANT F{\_}0.5, which allows us to report a much wider range of performance statistics. The competition was hosted on Codalab and remains open for further submissions on the blind test set.",
}
```
### Contributions
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset. | 14,995 | [
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flax-sentence-embeddings/stackexchange_math_jsonl | 2022-07-11T13:12:59.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | flax-sentence-embeddings | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | @misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
} | 4 | 218 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
pretty_name: stackexchange
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)s
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [stackexchange](https://archive.org/details/stackexchange)
- **Repository:** [flax-sentence-embeddings](https://github.com/nreimers/flax-sentence-embeddings)
### Dataset Summary
We automatically extracted question and answer (Q&A) pairs from [Stack Exchange](https://stackexchange.com/) network. Stack Exchange gather many Q&A communities across 50 online plateform, including the well known Stack Overflow and other technical sites. 100 millon developpers consult Stack Exchange every month. The dataset is a parallel corpus with each question mapped to the top rated answer. The dataset is split given communities which cover a variety of domains from 3d printing, economics, raspberry pi or emacs. An exhaustive list of all communities is available [here](https://stackexchange.com/sites).
### Languages
Stack Exchange mainly consist of english language (en).
## Dataset Structure
### Data Instances
Each data samples is presented as follow:
```
{'title_body': 'How to determine if 3 points on a 3-D graph are collinear? Let the points $A, B$ and $C$ be $(x_1, y_1, z_1), (x_2, y_2, z_2)$ and $(x_3, y_3, z_3)$ respectively. How do I prove that the 3 points are collinear? What is the formula?',
'upvoted_answer': 'From $A(x_1,y_1,z_1),B(x_2,y_2,z_2),C(x_3,y_3,z_3)$ we can get their position vectors.\n\n$\\vec{AB}=(x_2-x_1,y_2-y_1,z_2-z_1)$ and $\\vec{AC}=(x_3-x_1,y_3-y_1,z_3-z_1)$.\n\nThen $||\\vec{AB}\\times\\vec{AC}||=0\\implies A,B,C$ collinear.',
'downvoted_answer': 'If the distance between |AB|+|BC|=|AC| then A,B,C are collinear.'}
```
This particular exampe corresponds to the [following page](https://math.stackexchange.com/questions/947555/how-to-determine-if-3-points-on-a-3-d-graph-are-collinear)
### Data Fields
The fields present in the dataset contain the following informations:
- `title_body`: This is the concatenation of the title and body from the question
- `upvoted_answer`: This is the body from the most upvoted answer
- `downvoted_answer`: This is the body from most downvoted answer
- `title`: This is the title from the question
### Data Splits
We provide three splits for this dataset, which only differs by the structure of the fieds which are retrieved:
- `titlebody_upvoted_downvoted_answer`: Includes title and body from the question as well as most upvoted and downvoted answer.
- `title_answer`: Includes title from the question as well as most upvoted answer.
- `titlebody_answer`: Includes title and body from the question as well as most upvoted answer.
| | Number of pairs |
| ----- | ------ |
| `titlebody_upvoted_downvoted_answer` | 17,083 |
| `title_answer` | 1,100,953 |
| `titlebody_answer` | 1,100,953 |
## Dataset Creation
### Curation Rationale
We primary designed this dataset for sentence embeddings training. Indeed sentence embeddings may be trained using a contrastive learning setup for which the model is trained to associate each sentence with its corresponding pair out of multiple proposition. Such models require many examples to be efficient and thus the dataset creation may be tedious. Community networks such as Stack Exchange allow us to build many examples semi-automatically.
### Source Data
The source data are dumps from [Stack Exchange](https://archive.org/details/stackexchange)
#### Initial Data Collection and Normalization
We collected the data from the math community.
We filtered out questions which title or body length is bellow 20 characters and questions for which body length is above 4096 characters.
When extracting most upvoted answer, we filtered to pairs for which their is at least 100 votes gap between most upvoted and downvoted answers.
#### Who are the source language producers?
Questions and answers are written by the community developpers of Stack Exchange.
## Additional Information
### Licensing Information
Please see the license information at: https://archive.org/details/stackexchange
### Citation Information
```
@misc{StackExchangeDataset,
author = {Flax Sentence Embeddings Team},
title = {Stack Exchange question pairs},
year = {2021},
howpublished = {https://huggingface.co/datasets/flax-sentence-embeddings/},
}
```
### Contributions
Thanks to the Flax Sentence Embeddings team for adding this dataset. | 5,614 | [
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] |
olm/wikipedia | 2022-11-15T18:39:59.000Z | [
"task_categories:text-generation",
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"license:cc-by-sa-3.0",
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] | olm | Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.). | @ONLINE {wikidump,
author = {Wikimedia Foundation},
title = {Wikimedia Downloads},
url = {https://dumps.wikimedia.org}
} | 24 | 218 | 2022-10-04T18:07:56 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
pretty_name: Wikipedia
paperswithcode_id: null
license:
- cc-by-sa-3.0
- gfdl
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
source_datasets:
- original
multilinguality:
- multilingual
size_categories:
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---
# Dataset Card for Wikipedia
This repo is a fork of the original Hugging Face Wikipedia repo [here](https://huggingface.co/datasets/wikipedia).
The difference is that this fork does away with the need for `apache-beam`, and this fork is very fast if you have a lot of CPUs on your machine.
It will use all CPUs available to create a clean Wikipedia pretraining dataset. It takes less than an hour to process all of English wikipedia on a GCP n1-standard-96.
This fork is also used in the [OLM Project](https://github.com/huggingface/olm-datasets) to pull and process up-to-date wikipedia snapshots.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ``mwparserfromhell`` tool, and we use ``multiprocess`` for parallelization.
To load this dataset you need to install these first:
```
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
load_dataset("olm/wikipedia", language="en", date="20220920")
```
You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Most of Wikipedia's text and many of its images are co-licensed under the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
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] |
C-MTEB/ThuNewsClusteringS2S | 2023-07-27T17:28:46.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 218 | 2023-07-27T17:28:35 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 6649209
num_examples: 10
download_size: 5008942
dataset_size: 6649209
---
# Dataset Card for "ThuNewsClusteringS2S"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 489 | [
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result-kand2-sdxl-wuerst-karlo/cfc9bbcd | 2023-10-08T13:50:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 218 | 2023-10-08T13:50:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1339
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cfc9bbcd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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lener_br | 2023-09-25T07:35:39.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:unknown",
"legal",
"region:us"
] | null | LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal documents from several Brazilian Courts were
collected. Courts of superior and state levels were considered, such as Supremo
Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
Gerais and Tribunal de Contas da União. In addition, four legislation documents
were collected, such as "Lei Maria da Penha", giving a total of 70 documents | @inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
publisher = {Springer},
series = {Lecture Notes on Computer Science ({LNCS})},
pages = {313--323},
year = {2018},
month = {September 24-26},
address = {Canela, RS, Brazil},
doi = {10.1007/978-3-319-99722-3_32},
url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
} | 21 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: lener-br
pretty_name: leNER-br
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-ORGANIZACAO
'2': I-ORGANIZACAO
'3': B-PESSOA
'4': I-PESSOA
'5': B-TEMPO
'6': I-TEMPO
'7': B-LOCAL
'8': I-LOCAL
'9': B-LEGISLACAO
'10': I-LEGISLACAO
'11': B-JURISPRUDENCIA
'12': I-JURISPRUDENCIA
config_name: lener_br
splits:
- name: train
num_bytes: 3984189
num_examples: 7828
- name: validation
num_bytes: 719433
num_examples: 1177
- name: test
num_bytes: 823708
num_examples: 1390
download_size: 2983137
dataset_size: 5527330
tags:
- legal
---
# Dataset Card for leNER-br
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [leNER-BR homepage](https://cic.unb.br/~teodecampos/LeNER-Br/)
- **Repository:** [leNER-BR repository](https://github.com/peluz/lener-br)
- **Paper:** [leNER-BR: Long Form Question Answering](https://cic.unb.br/~teodecampos/LeNER-Br/luz_etal_propor2018.pdf)
- **Point of Contact:** [Pedro H. Luz de Araujo](mailto:pedrohluzaraujo@gmail.com)
### Dataset Summary
LeNER-Br is a Portuguese language dataset for named entity recognition
applied to legal documents. LeNER-Br consists entirely of manually annotated
legislation and legal cases texts and contains tags for persons, locations,
time entities, organizations, legislation and legal cases.
To compose the dataset, 66 legal documents from several Brazilian Courts were
collected. Courts of superior and state levels were considered, such as Supremo
Tribunal Federal, Superior Tribunal de Justiça, Tribunal de Justiça de Minas
Gerais and Tribunal de Contas da União. In addition, four legislation documents
were collected, such as "Lei Maria da Penha", giving a total of 70 documents
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Portuguese.
## Dataset Structure
### Data Instances
An example from the dataset looks as follows:
```
{
"id": "0",
"ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0],
"tokens": [
"EMENTA", ":", "APELAÇÃO", "CÍVEL", "-", "AÇÃO", "DE", "INDENIZAÇÃO", "POR", "DANOS", "MORAIS", "-", "PRELIMINAR", "-", "ARGUIDA", "PELO", "MINISTÉRIO", "PÚBLICO", "EM", "GRAU", "RECURSAL"]
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-ORGANIZACAO", "I-ORGANIZACAO", "B-PESSOA", "I-PESSOA", "B-TEMPO", "I-TEMPO", "B-LOCAL", "I-LOCAL", "B-LEGISLACAO", "I-LEGISLACAO", "B-JURISPRUDENCIA", "I-JURISPRUDENCIA"
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word.
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 7828 | 1177 | 1390 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{luz_etal_propor2018,
author = {Pedro H. {Luz de Araujo} and Te\'{o}filo E. {de Campos} and
Renato R. R. {de Oliveira} and Matheus Stauffer and
Samuel Couto and Paulo Bermejo},
title = {{LeNER-Br}: a Dataset for Named Entity Recognition in {Brazilian} Legal Text},
booktitle = {International Conference on the Computational Processing of Portuguese ({PROPOR})},
publisher = {Springer},
series = {Lecture Notes on Computer Science ({LNCS})},
pages = {313--323},
year = {2018},
month = {September 24-26},
address = {Canela, RS, Brazil},
doi = {10.1007/978-3-319-99722-3_32},
url = {https://cic.unb.br/~teodecampos/LeNER-Br/},
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | 6,100 | [
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sharc_modified | 2022-11-03T16:31:23.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|sharc",
"language:en",
"license:unknown",
"conversational-qa",
"arxiv:1909.03759",
"arxiv:2009.06354",
"region:us"
] | null | ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text. However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models. SharcModified is a new dataset which reduces the patterns identified in the original dataset. To reduce the sensitivity of neural models, for each occurence of an instance conforming to any of the patterns, we automatically construct alternatives where we choose to either replace the current instance with an alternative instance which does not exhibit the pattern; or retain the original instance. The modified ShARC has two versions sharc-mod and history-shuffled. For morre details refer to Appendix A.3 . | @inproceedings{verma-etal-2020-neural,
title = "Neural Conversational {QA}: Learning to Reason vs Exploiting Patterns",
author = "Verma, Nikhil and
Sharma, Abhishek and
Madan, Dhiraj and
Contractor, Danish and
Kumar, Harshit and
Joshi, Sachindra",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.589",
pages = "7263--7269",
abstract = "Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.",
} | 0 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|sharc
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: SharcModified
tags:
- conversational-qa
dataset_info:
- config_name: mod
features:
- name: id
dtype: string
- name: utterance_id
dtype: string
- name: source_url
dtype: string
- name: snippet
dtype: string
- name: question
dtype: string
- name: scenario
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list:
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dtype: string
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dtype: string
- name: evidence
list:
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dtype: string
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dtype: string
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splits:
- name: train
num_bytes: 15138034
num_examples: 21890
- name: validation
num_bytes: 1474239
num_examples: 2270
download_size: 21197271
dataset_size: 16612273
- config_name: mod_dev_multi
features:
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dtype: string
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dtype: string
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sequence: string
splits:
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num_bytes: 1553940
num_examples: 2270
download_size: 2006124
dataset_size: 1553940
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splits:
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num_bytes: 15083103
num_examples: 21890
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num_examples: 2270
download_size: 21136658
dataset_size: 16551707
- config_name: history_dev_multi
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: all_answers
sequence: string
splits:
- name: validation
num_bytes: 1548305
num_examples: 2270
download_size: 2000489
dataset_size: 1548305
---
# Dataset Card for SharcModified
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More info needed]
- **Repository:** [github](https://github.com/nikhilweee/neural-conv-qa)
- **Paper:** [Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns](https://arxiv.org/abs/1909.03759)
- **Leaderboard:** [More info needed]
- **Point of Contact:** [More info needed]
### Dataset Summary
ShARC, a conversational QA task, requires a system to answer user questions based on rules expressed in natural language text.
However, it is found that in the ShARC dataset there are multiple spurious patterns that could be exploited by neural models.
SharcModified is a new dataset which reduces the patterns identified in the original dataset.
To reduce the sensitivity of neural models, for each occurence of an instance conforming to any of the patterns,
we automatically construct alternatives where we choose to either replace the current instance with an alternative
instance which does not exhibit the pattern; or retain the original instance.
The modified ShARC has two versions sharc-mod and history-shuffled.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in english (en).
## Dataset Structure
### Data Instances
Example of one instance:
```
{
"annotation": {
"answer": [
{
"paragraph_reference": {
"end": 64,
"start": 35,
"string": "syndactyly affecting the feet"
},
"sentence_reference": {
"bridge": false,
"end": 64,
"start": 35,
"string": "syndactyly affecting the feet"
}
}
],
"explanation_type": "single_sentence",
"referential_equalities": [
{
"question_reference": {
"end": 40,
"start": 29,
"string": "webbed toes"
},
"sentence_reference": {
"bridge": false,
"end": 11,
"start": 0,
"string": "Webbed toes"
}
}
],
"selected_sentence": {
"end": 67,
"start": 0,
"string": "Webbed toes is the common name for syndactyly affecting the feet . "
}
},
"example_id": 9174646170831578919,
"original_nq_answers": [
{
"end": 45,
"start": 35,
"string": "syndactyly"
}
],
"paragraph_text": "Webbed toes is the common name for syndactyly affecting the feet . It is characterised by the fusion of two or more digits of the feet . This is normal in many birds , such as ducks ; amphibians , such as frogs ; and mammals , such as kangaroos . In humans it is considered unusual , occurring in approximately one in 2,000 to 2,500 live births .",
"question": "what is the medical term for webbed toes",
"sentence_starts": [
0,
67,
137,
247
],
"title_text": "Webbed toes",
"url": "https: //en.wikipedia.org//w/index.php?title=Webbed_toes&oldid=801229780"
}
```
### Data Fields
- `example_id`: a unique integer identifier that matches up with NQ
- `title_text`: the title of the wikipedia page containing the paragraph
- `url`: the url of the wikipedia page containing the paragraph
- `question`: a natural language question string from NQ
- `paragraph_text`: a paragraph string from a wikipedia page containing the answer to question
- `sentence_starts`: a list of integer character offsets indicating the start of sentences in the paragraph
- `original_nq_answers`: the original short answer spans from NQ
- `annotation`: the QED annotation, a dictionary with the following items and further elaborated upon below:
- `referential_equalities`: a list of dictionaries, one for each referential equality link annotated
- `answer`: a list of dictionaries, one for each short answer span
- `selected_sentence`: a dictionary representing the annotated sentence in the passage
- `explanation_type`: one of "single_sentence", "multi_sentence", or "none"
### Data Splits
The dataset is split into training and validation splits.
| | train | validation |
|--------------|------:|-----------:|
| N. Instances | 7638 | 1355 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown.
### Citation Information
```
@misc{lamm2020qed,
title={QED: A Framework and Dataset for Explanations in Question Answering},
author={Matthew Lamm and Jennimaria Palomaki and Chris Alberti and Daniel Andor and Eunsol Choi and Livio Baldini Soares and Michael Collins},
year={2020},
eprint={2009.06354},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | 10,107 | [
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EMBO/biolang | 2023-01-11T15:31:53.000Z | [
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"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n>1M",
"language:en",
"license:cc-by-4.0",
"region:us"
] | EMBO | This dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. | @Unpublished{
huggingface: dataset,
title = {biolang},
authors={Thomas Lemberger, EMBO},
year={2021}
} | 0 | 217 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets: []
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for BioLang
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sourcedata.embo.org
- **Repository:** https://github.com/source-data/soda-roberta
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** thomas.lemberger@embo.org
- **Download Size:** 5_299_878_661
### Dataset Summary
BioLang is a dataset is based on abstracts from the open access section of EuropePubMed Central to train language models in the domain of biology. The dataset can be used for random masked language modeling or for language modeling using only specific part-of-speech maksing. More details on generation and use of the dataset at https://github.com/source-data/soda-roberta .
### Supported Tasks and Leaderboards
- `MLM`: masked language modeling
- `DET`: part-of-speach masked language model, with determinants (`DET`) tagged
- `SMALL`: part-of-speech masked language model, with "small" words (`DET`, `CCONJ`, `SCONJ`, `ADP`, `PRON`) tagged
- `VERB`: part-of-speach masked language model, with verbs (`VERB`) tagged
### Languages
English
## Dataset Structure
### Data Instances
```json
{
"input_ids":[
0, 2444, 6997, 46162, 7744, 35, 20632, 20862, 3457, 36, 500, 23858, 29, 43, 32, 3919, 716, 15, 49, 4476, 4, 1398, 6, 52, 1118, 5, 20862, 819, 9, 430, 23305, 248, 23858, 29, 4, 256, 40086, 104, 35, 1927, 1069, 459, 1484, 58, 4776, 13, 23305, 634, 16706, 493, 2529, 8954, 14475, 73, 34263, 6, 4213, 718, 833, 12, 24291, 4473, 22500, 14475, 73, 510, 705, 73, 34263, 6, 5143, 4313, 2529, 8954, 14475, 73, 34263, 6, 8, 5143, 4313, 2529, 8954, 14475, 248, 23858, 29, 23, 4448, 225, 4722, 2392, 11, 9341, 261, 4, 49043, 35, 96, 746, 6, 5962, 9, 38415, 4776, 408, 36, 3897, 4, 398, 8871, 56, 23305, 4, 20, 15608, 21, 8061, 6164, 207, 13, 70, 248, 23858, 29, 6, 150, 5, 42561, 21, 8061, 5663, 207, 13, 80, 3457, 4, 509, 1296, 5129, 21567, 3457, 36, 398, 23528, 8748, 22065, 11654, 35, 7253, 15, 49, 4476, 6, 70, 3457, 4682, 65, 189, 28, 5131, 13, 23305, 9726, 4, 2
],
"label_ids": [
"X", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "NOUN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "AUX", "VERB", "VERB", "ADP", "DET", "NOUN", "PUNCT", "ADV", "PUNCT", "PRON", "VERB", "DET", "ADJ", "NOUN", "ADP", "ADJ", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "ADJ", "ADJ", "ADJ", "PUNCT", "NOUN", "NOUN", "NOUN", "NOUN", "AUX", "VERB", "ADP", "NOUN", "VERB", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "PROPN", "PROPN", "PROPN", "PROPN", "PROPN", "SYM", "PROPN", "PUNCT", "CCONJ", "ADJ", "PROPN", "PROPN", "PROPN", "PROPN", "NOUN", "NOUN", "NOUN", "ADP", "PROPN", "PROPN", "PROPN", "PROPN", "ADP", "PROPN", "PROPN", "PUNCT", "PROPN", "PUNCT", "ADP", "NOUN", "PUNCT", "NUM", "ADP", "NUM", "VERB", "NOUN", "PUNCT", "NUM", "NUM", "NUM", "NOUN", "AUX", "NOUN", "PUNCT", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "DET", "NOUN", "NOUN", "NOUN", "PUNCT", "SCONJ", "DET", "NOUN", "AUX", "X", "NUM", "NOUN", "ADP", "NUM", "NOUN", "PUNCT", "NUM", "NOUN", "VERB", "ADJ", "NOUN", "PUNCT", "NUM", "NOUN", "NOUN", "NOUN", "NOUN", "PUNCT", "VERB", "ADP", "DET", "NOUN", "PUNCT", "DET", "NOUN", "SCONJ", "PRON", "VERB", "AUX", "VERB", "ADP", "NOUN", "NOUN", "PUNCT", "X"
],
"special_tokens_mask": [
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1
]
}
```
### Data Fields
`MLM`:
- `input_ids`: a `list` of `int32` features.
- `special_tokens_mask`: a `list` of `int8` features.
`DET`, `VERB`, `SMALL`:
- `input_ids`: a `list` of `int32` features.
- `tag_mask`: a `list` of `int8` features.
### Data Splits
- `train`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 12_005_390
- `test`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 37_112
- `validation`:
- features: ['input_ids', 'special_tokens_mask'],
- num_rows: 36_713
## Dataset Creation
### Curation Rationale
The dataset was assembled to train language models in the field of cell and molecular biology. To expand the size of the dataset and to include many examples with highly technical language, abstracts were complemented with figure legends (or figure 'captions').
### Source Data
#### Initial Data Collection and Normalization
The xml content of papers were downloaded in January 2021 from the open access section of [EuropePMC]("https://europepmc.org/downloads/openaccess"). Figure legends and abstracts were extracted from the JATS XML, tokenized with the `roberta-base` tokenizer and part-of-speech tagged with Spacy's `en_core_web_sm` model (https://spacy.io).
More details at https://github.com/source-data/soda-roberta
#### Who are the source language producers?
Experts scientists.
### Annotations
#### Annotation process
Part-of-speech was tagged automatically.
#### Who are the annotators?
Spacy's `en_core_web_sm` model (https://spacy.io) was used for part-of-speech tagging.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Thomas Lemberger
### Licensing Information
CC-BY 4.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tlemberger](https://github.com/tlemberger) for adding this dataset.
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newspop | 2022-11-03T16:31:06.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"social-media-shares-prediction",
"arxiv:1801.07055",
"region:us"
] | null | This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn.
The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine.
This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. | @article{Moniz2018MultiSourceSF,
title={Multi-Source Social Feedback of Online News Feeds},
author={N. Moniz and L. Torgo},
journal={ArXiv},
year={2018},
volume={abs/1801.07055}
} | 2 | 216 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: null
pretty_name: News Popularity in Multiple Social Media Platforms
tags:
- social-media-shares-prediction
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: headline
dtype: string
- name: source
dtype: string
- name: topic
dtype: string
- name: publish_date
dtype: string
- name: facebook
dtype: int32
- name: google_plus
dtype: int32
- name: linked_in
dtype: int32
splits:
- name: train
num_bytes: 27927641
num_examples: 93239
download_size: 30338277
dataset_size: 27927641
---
# Dataset Card for News Popularity in Multiple Social Media Platforms
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [UCI](https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms)
- **Repository:**
- **Paper:** [Arxiv](https://arxiv.org/abs/1801.07055)
- **Leaderboard:** [Kaggle](https://www.kaggle.com/nikhiljohnk/news-popularity-in-multiple-social-media-platforms/code)
- **Point of Contact:**
### Dataset Summary
Social sharing data across Facebook, Google+ and LinkedIn for 100k news items on the topics of: economy, microsoft, obama and palestine.
### Supported Tasks and Leaderboards
Popularity prediction/shares prediction
### Languages
English
## Dataset Structure
### Data Instances
```
{ "id": 35873,
"title": "Microsoft's 'teen girl' AI turns into a Hitler-loving sex robot within 24 ...",
"headline": "Developers at Microsoft created 'Tay', an AI modelled to speak 'like a teen girl', in order to improve the customer service on their voice",
"source": "Telegraph.co.uk",
"topic": "microsoft",
"publish_date": "2016-03-24 09:53:54",
"facebook": 22346,
"google_plus": 973,
"linked_in": 1009
}
```
### Data Fields
- id: the sentence id in the source dataset
- title: the title of the link as shared on social media
- headline: the headline, or sometimes the lede of the story
- source: the source news site
- topic: the topic: one of "economy", "microsoft", "obama" and "palestine"
- publish_date: the date the original article was published
- facebook: the number of Facebook shares, or -1 if this data wasn't collected
- google_plus: the number of Google+ likes, or -1 if this data wasn't collected
- linked_in: the number of LinkedIn shares, or -1 if if this data wasn't collected
### Data Splits
None
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
The source headlines were by journalists, while the titles were written by the
people sharing it on social media.
### Annotations
#### Annotation process
The 'annotations' are simply the number of shares, or likes in the case of
Google+ as collected from various API endpoints.
#### Who are the annotators?
Social media users.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License: Creative Commons Attribution 4.0 International License (CC-BY)
### Citation Information
```
@article{Moniz2018MultiSourceSF,
title={Multi-Source Social Feedback of Online News Feeds},
author={N. Moniz and L. Torgo},
journal={ArXiv},
year={2018},
volume={abs/1801.07055}
}
```
### Contributions
Thanks to [@frankier](https://github.com/frankier) for adding this dataset. | 4,944 | [
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bond005/sberdevices_golos_10h_crowd | 2022-10-27T04:42:07.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:ru",
"license:other",
"arxiv:2106.10161",
"region:us"
] | bond005 | null | null | 1 | 216 | 2022-10-26T11:12:15 | ---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sberdevices_golos_10h_crowd
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Golos ASR corpus](https://www.openslr.org/114)
- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Nikolay Karpov](mailto:karpnv@gmail.com)
### Dataset Summary
Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
This dataset is a simpler version of the above mentioned Golos:
- it includes the crowd domain only (without any sound from the farfield domain);
- validation split is built on the 1-hour training subset;
- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
- test split is a full original test split.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': None,
'array': array([ 3.05175781e-05, 3.05175781e-05, 0.00000000e+00, ...,
-1.09863281e-03, -7.93457031e-04, -1.52587891e-04]), dtype=float64),
'sampling_rate': 16000},
'transcription': 'шестнадцатая часть сезона пять сериала лемони сникет тридцать три несчастья'}
```
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset is a simpler version of the original Golos:
- it includes the crowd domain only (without any sound from the farfield domain);
- validation split is built on the 1-hour training subset;
- training split corresponds to the 10-hour training subset without sounds from the 1-hour training subset;
- test split is a full original test split.
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 7993 | 793 | 9994 |
| hours | 8.9h | 0.9h | 11.2h |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
All recorded audio files were manually annotated on the crowd-sourcing platform.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
### Licensing Information
[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
### Citation Information
```
@misc{karpov2021golos,
author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
title = {Golos: Russian Dataset for Speech Research},
publisher = {arXiv},
year = {2021},
url = {https://arxiv.org/abs/2106.10161}
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
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] |
DDSC/partial-danish-gigaword-small-test-sample | 2023-01-09T13:11:16.000Z | [
"language:da",
"region:us"
] | DDSC | null | null | 0 | 216 | 2023-01-09T13:07:16 | ---
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
- name: doc_id
dtype: string
- name: LICENSE
dtype: string
- name: uri
dtype: string
- name: date_built
dtype: string
splits:
- name: train
num_bytes: 23816547.04337273
num_examples: 2411
download_size: 11686492
dataset_size: 23816547.04337273
language:
- da
pretty_name: Danish Gigaword Test Sample
---
# Dataset Card for "Danish Gigaword Test Sample"
This is a small sample of the dataset `DDSC/partial-danish-gigaword-no-twitter`. It is meant as a small dataset for testing code. It is constructed using the following code:
```python
from datasets import concatenate_datasets, load_dataset
# download dataset from huggingface
dataset = load_dataset("DDSC/partial-danish-gigaword-no-twitter")
# All of the dataset is available in the train split - we can simply:
dataset = dataset["train"]
# downsample it to three domains
legal = dataset.filter(lambda x: x["source"] == "retsinformationdk")
news = dataset.filter(lambda x: x["source"] == "tv2r")
speech = dataset.filter(lambda x: x["source"] == "spont")
# downsample to 1000 samples
legal = legal.select(range(1000))
news = news.select(range(1000))
# combine the three domains
dataset = concatenate_datasets([legal, news, speech])
# upload to hub
dataset.push_to_hub("DDSC/partial-danish-gigaword-small-test-sample")
``` | 1,420 | [
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tum-nlp/IDMGSP | 2023-09-12T11:57:59.000Z | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail++",
"scientific paper",
"fake papers",
"science",
"scientific text",
"region:us"
] | tum-nlp | TODO | null | 3 | 216 | 2023-05-27T12:20:55 | ---
viewer: true
task_categories:
- text-classification
language:
- en
tags:
- scientific paper
- fake papers
- science
- scientific text
pretty_name: ' A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era (IDMGSP)'
size_categories:
- 10K<n<100K
dataset_info:
- config_name: classifier_input
features:
- name: id
dtype: string
- name: year
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: introduction
dtype: string
- name: conclusion
dtype: string
- name: categories
dtype: string
- name: src
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 70117904
num_examples: 16000
- name: test
num_bytes: 34724993
num_examples: 8000
download_size: 32157176
dataset_size: 104842897
- config_name: tecg
features:
- name: id
dtype: string
- name: year
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
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dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 2408633
num_examples: 1000
download_size: 582824
dataset_size: 2408633
- config_name: train+gpt3
features:
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dtype: string
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dtype: string
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splits:
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features:
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dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 65261576
num_examples: 14000
download_size: 20272344
dataset_size: 65261576
- config_name: ood_gpt3
features:
- name: title
dtype: string
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dtype: string
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dtype: int64
splits:
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num_bytes: 3454121
num_examples: 1200
- name: test
num_bytes: 2837275
num_examples: 1000
download_size: 1708501
dataset_size: 6291396
- config_name: ood_real
features:
- name: abstract
dtype: string
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dtype: string
- name: conclusion
dtype: string
- name: src
dtype: string
- name: label
dtype: int64
splits:
- name: test
num_bytes: 15808225
num_examples: 4000
download_size: 5336873
dataset_size: 15808225
license: openrail++
---
# Dataset Card for A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era
## Dataset Description
- **Repository:** https://github.com/qwenzo/-IDMGSP
- **Paper:** TODO
### Dataset Summary
A benchmark for detecting machine-generated scientific papers based on their abstract, introduction and conclusion sections.
### Supported Tasks and Leaderboards
current benchmark results in terms of accuracy:
| Model | Train Dataset | TEST | OOD-GPT3 | OOD-REAL | TECG | TEST-CC |
|-----------------------------|-----------------|---------|----------|----------|---------|---------|
| LR-1gram (tf-idf) (our) | TRAIN | 95.3% | 4.0% | 94.6% | 96.1% | 7.8% |
| LR-1gram (tf-idf) (our) | TRAIN+GPT3 | 94.6% | 86.5% | 86.2% | 97.8% | 13.7% |
| LR-1gram (tf-idf) (our) | TRAIN-CG | 86.6% | 0.8% | 97.8% | 32.6% | 1.2% |
| RF-1gram (tf-idf) (our) | TRAIN | 94.8% | 24.7% | 87.3% | 100.0% | 8.1% |
| RF-1gram (tf-idf) (our) | TRAIN+GPT3 | 91.7% | 95.0% | 69.3% | 100.0% | 15.1% |
| RF-1gram (tf-idf) (our) | TRAIN-CG | 97.6% | 7.0% | 95.0% | 57.0% | 1.7% |
| [IDMGSP-Galactica-TRAIN](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN) (our) | TRAIN | 98.4% | 25.9% | 95.5% | 84.0% | 6.8% |
| [IDMGSP-Galactica-TRAIN_GPT3](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN_GPT3) (our) | TRAIN+GPT3 | 98.5% | 71.2% | 95.1% | 84.0% | 12.0% |
| [IDMGSP-Galactica-TRAIN-CG](https://huggingface.co/tum-nlp/IDMGSP-Galactica-TRAIN-CG) (our) | TRAIN-CG | 96.4% | 12.4% | 97.6% | 61.3% | 2.4% |
| [IDMGSP-RoBERTa-TRAIN-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CONCLUSION) (our) | TRAIN | 72.3% | 55.5% | 50.0% | 100.0% | 63.5% |
| [IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN_GPT3-CONCLUSION) (our) | TRAIN+GPT3 | 65.7% | 100.0% | 29.1% | 100.0% | 75.0% |
| [IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-ABSTRACT) + [IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-INTRODUCTION) + [IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION](https://huggingface.co/tum-nlp/IDMGSP-RoBERTa-TRAIN-CG-CONCLUSION) (our) | TRAIN-CG | 86.0% | 2.0% | 92.5% | 76.5% | 9.2% |
| GPT-3 (our) | TRAIN-SUB | 100.0% | 25.9% | 99.0% | 100.0% | N/A |
| DetectGPT | - | 61.5% | 0.0% | 99.9% | 68.7% | N/A |
| ChatGPT-IO (our)* | - | 69.0% | 49.0% | 89.0% | 0.0% | 3.0% |
| LLMFE (our)* | TRAIN+GPT3 | 80.0% | 62.0% | 70.0% | 90.0% | 33.0% |
### Languages
English
## Dataset Structure
### Data Instances
Each instance in the dataset corresponds to a row in a CSV file, encompassing the features of a paper, its label, and the paper's source.
### Data Fields
#### classifier_input
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### train+gpt3
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "chatgpt", "gpt2", "real", "scigen" or "galactica", "gpt3".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### tecg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### train-cg
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. This can have the values "gpt2", "real", "scigen" or "galactica".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers.
dtype: int64
#### ood_gpt3
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "gpt3".
dtype: string
- name: label
description: always having the value 1.
dtype: int64
#### ood_real
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: src
description: indicator of the source of the paper. Has the value "ood_real".
dtype: string
- name: label
description: always having the value 0.
dtype: int64
#### test-cc
- name: id
description: The ID of the provided paper corresponds to the identifier assigned by the arXiv database if the paper's source is marked as "real".
dtype: string
- name: year
description: year of the publication as given by the arXiv database.
dtype: string
- name: title
description: title of the paper given by the arXiv database.
dtype: string
- name: abstract
description: abstract of the paper given by the arXiv database.
dtype: string
- name: introduction
description: introduction section of the paper. extracted by the PDF parser.
dtype: string
- name: conclusion
description: conclusion section of the paper. extracted by the PDF parser.
dtype: string
- name: categories
description: topics/domains of the paper given by the arXiv database. This field is null if the src field is not "real".
dtype: string
- name: src
description: indicator of the source of the paper. Always has the value "chatgpt-paraphrased".
dtype: string
- name: paraphrased_sections
description: indicator of which sections are paraphrased. Can have the values "introduction", "conclusion", "introduction, conclusion", "abstract, introduction, conclusion".
dtype: string
- name: label
description: 0 for real/human-written papers and 1 for fake/machine-generated papers. Always has the value 1.
dtype: int64
### Data Splits
Table: Overview of the datasets used to train and evaluate the classifiers.
| Dataset | arXiv | ChatGPT | GPT-2 | SCIgen | Galactica | GPT-3 | ChatGPT (co-created) |
|--------------------------------------|--------|---------|--------|--------|-----------|--------|-----------------------|
| Standard train (TRAIN) | 8k | 2k | 2k | 2k | 2k | - | - |
| Standard train subset (TRAIN-SUB) | 4k | 1k | 1k | 1k | 1k | - | - |
| TRAIN without ChatGPT (TRAIN-CG) | 8k | - | 2k | 2k | 2k | - | - |
| TRAIN plus GPT-3 (TRAIN+GPT3) | 8k | 2k | 2k | 2k | 2k | 1.2k | - |
| Standard test (TEST) | 4k | 1k | 1k | 1k | 1k | - | - |
| Out-of-domain GPT-3 only (OOD-GPT3) | - | - | - | - | - | 1k | - |
| Out-of-domain real (OOD-REAL) | 4k (parsing 2) | - | - | - | - | - | - |
| ChatGPT only (TECG) | - | 1k | - | - | - | - | - |
| Co-created test (TEST-CC) | - | - | - | - | - | - | 4k |
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 16,024 | [
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yuweiyin/FinBench | 2023-08-02T01:02:19.000Z | [
"task_categories:tabular-classification",
"task_categories:text-classification",
"size_categories:0.3M<n<1M",
"license:cc-by-nc-4.0",
"arxiv:2308.00065",
"region:us"
] | yuweiyin | FinBench Dataset | null | 4 | 216 | 2023-06-18T02:39:45 | ---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- text-classification
size_categories:
- 0.3M<n<1M
---
# Dataset Card for FinBench
## Dataset Description
- **Homepage: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Repository: https://huggingface.co/datasets/yuweiyin/FinBench**
- **Paper: https://arxiv.org/abs/2308.00065**
- **Leaderboard:**
- **Point of Contact:**
## Dataset Statistics
We present **FinBench**, a benchmark for evaluating the performance of machine learning models with
both tabular data inputs and profile text inputs.
We first collect hundreds of financial datasets from the [Kaggle](https://www.kaggle.com/) platform
and then screen out ten high-quality datasets for financial risk prediction.
The screening criteria is based on the quantity and popularity, column meaningfulness, and
the performance of baseline models on those datasets.
FinBench consists of three types of financial risks, i.e., default, fraud, and churn.
We process the datasets in a unified data structure and provide an easy-loading API on
[HuggingFace](https://huggingface.co/datasets/yuweiyin/FinBench).
### Task Statistics
The following table reports the task description, dataset name (for `datasets` loading),
the number and positive ratio of train/validation/test sets,
the number of classification classes (all is 2), and the number of features.
| Task | Description | Dataset | #Classes | #Features | #Train [Pos%] | #Val [Pos%] | #Test [Pos%] |
|---------------------|----------------------------------------------------------------|---------|----------|-----------|----------------|---------------|---------------|
| Credit-card Default | Predict whether a user will default on the credit card or not. | `cd1` | 2 | 9 | 2738 [7.0%] | 305 [6.9%] | 1305 [6.2%] |
| | | `cd2` | 2 | 23 | 18900 [22.3%] | 2100 [22.3%] | 9000 [21.8%] |
| Loan Default | Predict whether a user will default on the loan or not. | `ld1` | 2 | 12 | 2118 [8.9%] | 236 [8.5%] | 1010 [9.0%] |
| | | `ld2` | 2 | 11 | 18041 [21.7%] | 2005 [20.8%] | 8592 [21.8%] |
| | | `ld3` | 2 | 35 | 142060 [21.6%] | 15785 [21.3%] | 67648 [22.1%] |
| Credit-card Fraud | Predict whether a user will commit fraud or not. | `cf1` | 2 | 19 | 5352 [0.67%] | 595 [1.1%] | 2550 [0.90%] |
| | | `cf2` | 2 | 120 | 5418 [6.0%] | 603 [7.3%] | 2581 [6.0%] |
| Customer Churn | Predict whether a user will churn or not. (customer attrition) | `cc1` | 2 | 9 | 4189 [23.5%] | 466 [22.7%] | 1995 [22.4%] |
| | | `cc2` | 2 | 10 | 6300 [20.8%] | 700 [20.6%] | 3000 [19.47%] |
| | | `cc3` | 2 | 21 | 4437 [26.1%] | 493 [24.9%] | 2113 [27.8%] |
---
| Task | #Train | #Val | #Test |
|---------------------|--------|-------|-------|
| Credit-card Default | 21638 | 2405 | 10305 |
| Loan Default | 162219 | 18026 | 77250 |
| Credit-card Fraud | 10770 | 1198 | 5131 |
| Customer Churn | 14926 | 1659 | 7108 |
| Total | 209553 | 23288 | 99794 |
### Data Source
| Task | Dataset | Source |
|---------------------|---------|------------------------------------------------------------------------------------------|
| Credit-card Default | `cd1` | [Kaggle](https://www.kaggle.com/datasets/gustavotg/credit-default) |
| | `cd2` | [Kaggle](https://www.kaggle.com/datasets/uciml/default-of-credit-card-clients-dataset) |
| Loan Default | `ld1` | [Kaggle](https://www.kaggle.com/datasets/ajay1735/hmeq-data) |
| | `ld2` | [Kaggle](https://www.kaggle.com/datasets/laotse/credit-risk-dataset) |
| | `ld3` | [Kaggle](https://www.kaggle.com/datasets/mamtadhaker/lt-vehicle-loan-default-prediction) |
| Credit-card Fraud | `cf1` | [Kaggle](https://www.kaggle.com/datasets/johancaicedo/creditcardfraud) |
| | `cf2` | [Kaggle](https://www.kaggle.com/datasets/mishra5001/credit-card) |
| Customer Churn | `cc1` | [Kaggle](https://www.kaggle.com/datasets/gauravduttakiit/jobathon-march-2022) |
| | `cc2` | [Kaggle](https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers) |
| | `cc3` | [Kaggle](https://www.kaggle.com/datasets/yeanzc/telco-customer-churn-ibm-dataset) |
- Language: English
## Dataset Structure
### Data Fields
```python
import datasets
datasets.Features(
{
"X_ml": [datasets.Value(dtype="float")], # (The tabular data array of the current instance)
"X_ml_unscale": [datasets.Value(dtype="float")], # (Scaled tabular data array of the current instance)
"y": datasets.Value(dtype="int64"), # (The label / ground-truth)
"num_classes": datasets.Value("int64"), # (The total number of classes)
"num_features": datasets.Value("int64"), # (The total number of features)
"num_idx": [datasets.Value("int64")], # (The indices of the numerical datatype columns)
"cat_idx": [datasets.Value("int64")], # (The indices of the categorical datatype columns)
"cat_dim": [datasets.Value("int64")], # (The dimension of each categorical column)
"cat_str": [[datasets.Value("string")]], # (The category names of categorical columns)
"col_name": [datasets.Value("string")], # (The name of each column)
"X_instruction_for_profile": datasets.Value("string"), # instructions (from tabular data) for profiles
"X_profile": datasets.Value("string"), # customer profiles built from instructions via LLMs
}
)
```
## Data Loading
### HuggingFace Login (Optional)
```python
# OR run huggingface-cli login
from huggingface_hub import login
hf_token = "YOUR_ACCESS_TOKENS" # https://huggingface.co/settings/tokens
login(token=hf_token)
```
### Loading a Dataset
```python
from datasets import load_dataset
# ds_name_list = ["cd1", "cd2", "ld1", "ld2", "ld3", "cf1", "cf2", "cc1", "cc2", "cc3"]
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
```
### Loading the Splits
```python
from datasets import load_dataset
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
train_set = dataset["train"] if "train" in dataset else []
validation_set = dataset["validation"] if "validation" in dataset else []
test_set = dataset["test"] if "test" in dataset else []
```
### Loading the Instances
```python
from datasets import load_dataset
ds_name = "cd1" # change the dataset name here
dataset = load_dataset("yuweiyin/FinBench", ds_name)
train_set = dataset["train"] if "train" in dataset else []
for train_instance in train_set:
X_ml = train_instance["X_ml"] # List[float] (The tabular data array of the current instance)
X_ml_unscale = train_instance["X_ml_unscale"] # List[float] (Scaled tabular data array of the current instance)
y = train_instance["y"] # int (The label / ground-truth)
num_classes = train_instance["num_classes"] # int (The total number of classes)
num_features = train_instance["num_features"] # int (The total number of features)
num_idx = train_instance["num_idx"] # List[int] (The indices of the numerical datatype columns)
cat_idx = train_instance["cat_idx"] # List[int] (The indices of the categorical datatype columns)
cat_dim = train_instance["cat_dim"] # List[int] (The dimension of each categorical column)
cat_str = train_instance["cat_str"] # List[List[str]] (The category names of categorical columns)
col_name = train_instance["col_name"] # List[str] (The name of each column)
X_instruction_for_profile = train_instance["X_instruction_for_profile"] # instructions for building profiles
X_profile = train_instance["X_profile"] # customer profiles built from instructions via LLMs
```
## Citation
* arXiv: https://arxiv.org/abs/2308.00065
* GitHub: https://github.com/YuweiYin/FinPT
```bibtex
@article{yin2023finbench,
title = {FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models},
author = {Yin, Yuwei and Yang, Yazheng and Yang, Jian and Liu, Qi},
journal = {arXiv preprint arXiv:2308.00065},
year = {2023},
}
```
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OpenGVLab/InternVid | 2023-07-21T07:32:42.000Z | [
"task_categories:feature-extraction",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2307.06942",
"region:us"
] | OpenGVLab | The InternVid dataset contains over 7 million videos lasting nearly 760K hours, yielding 234M video clips accompanied by detailed descriptions of total 4.1B words. Our core contribution is to develop a scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale. | @article{wang2023internvid,
title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Qiao, Yu},
journal={arXiv preprint arXiv:2307.06942},
year={2023}
} | 20 | 216 | 2023-07-14T07:24:39 | ---
license: cc-by-nc-sa-4.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 10M<n<100M
---
# InternVid
## Dataset Description
- **Homepage:** [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Repository:** [OpenGVLab](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)
- **Paper:** [2307.06942](https://arxiv.org/pdf/2307.06942.pdf)
- **Point of Contact:** mailto:[InternVideo](gvx-sh@pjlab.org.cn)
## InternVid-10M-FLT
We present InternVid-10M-FLT, a subset of this dataset, consisting of 10 million video clips, with generated high-quality captions for publicly available web videos.
## Download
The 10M samples are provided in jsonlines file. Columns include the videoID, timestamps, generated caption and their UMT similarity scores.\
## How to Use
```
from datasets import load_dataset
dataset = load_dataset("OpenGVLab/InternVid")
```
## Method
## Citation
If you find this work useful for your research, please consider citing InternVid. Your acknowledgement would greatly help us in continuing to contribute resources to the research community.
```
@article{wang2023internvid,
title={InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation},
author={Wang, Yi and He, Yinan and Li, Yizhuo and Li, Kunchang and Yu, Jiashuo and Ma, Xin and Chen, Xinyuan and Wang, Yaohui and Luo, Ping and Liu, Ziwei and Wang, Yali and Wang, Limin and Qiao, Yu},
journal={arXiv preprint arXiv:2307.06942},
year={2023}
}
@article{wang2022internvideo,
title={InternVideo: General Video Foundation Models via Generative and Discriminative Learning},
author={Wang, Yi and Li, Kunchang and Li, Yizhuo and He, Yinan and Huang, Bingkun and Zhao, Zhiyu and Zhang, Hongjie and Xu, Jilan and Liu, Yi and Wang, Zun and Xing, Sen and Chen, Guo and Pan, Junting and Yu, Jiashuo and Wang, Yali and Wang, Limin and Qiao, Yu},
journal={arXiv preprint arXiv:2212.03191},
year={2022}
}
``` | 2,040 | [
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afrikaans_ner_corpus | 2023-01-25T14:20:30.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:af",
"license:other",
"region:us"
] | null | Named entity annotated data from the NCHLT Text Resource Development: Phase II Project, annotated with PERSON, LOCATION, ORGANISATION and MISCELLANEOUS tags. | @inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/299},
} | 3 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Afrikaans Ner Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: afrikaans_ner_corpus
splits:
- name: train
num_bytes: 4025667
num_examples: 8962
download_size: 25748344
dataset_size: 4025667
---
# Dataset Card for Afrikaans Ner Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Afrikaans.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Afrikaans.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
[More Information Needed]
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/299},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | 5,689 | [
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datacommons_factcheck | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | A dataset of fact checked claims by news media maintained by datacommons.org | @InProceedings{huggingface:dataset,
title = {Data Commons 2019 Fact Checks},
authors={datacommons.org},
year={2019}
} | 3 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
paperswithcode_id: null
pretty_name: DataCommons Fact Checked claims
dataset_info:
- config_name: fctchk_politifact_wapo
features:
- name: reviewer_name
dtype: string
- name: claim_text
dtype: string
- name: review_date
dtype: string
- name: review_url
dtype: string
- name: review_rating
dtype: string
- name: claim_author_name
dtype: string
- name: claim_date
dtype: string
splits:
- name: train
num_bytes: 1772321
num_examples: 5632
download_size: 671896
dataset_size: 1772321
- config_name: weekly_standard
features:
- name: reviewer_name
dtype: string
- name: claim_text
dtype: string
- name: review_date
dtype: string
- name: review_url
dtype: string
- name: review_rating
dtype: string
- name: claim_author_name
dtype: string
- name: claim_date
dtype: string
splits:
- name: train
num_bytes: 35061
num_examples: 132
download_size: 671896
dataset_size: 35061
config_names:
- fctchk_politifact_wapo
- weekly_standard
---
# Dataset Card for DataCommons Fact Checked claims
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Data Commons fact checking FAQ](https://datacommons.org/factcheck/faq)
### Dataset Summary
A dataset of fact checked claims by news media maintained by [datacommons.org](https://datacommons.org/) containing the claim, author, and judgments, as well as the URL of the full explanation by the original fact-checker.
The fact checking is done by [FactCheck.org](https://www.factcheck.org/), [PolitiFact](https://www.politifact.com/), and [The Washington Post](https://www.washingtonpost.com/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The data is in English (`en`).
## Dataset Structure
### Data Instances
An example of fact checking instance looks as follows:
```
{'claim_author_name': 'Facebook posts',
'claim_date': '2019-01-01',
'claim_text': 'Quotes Michelle Obama as saying, "White folks are what’s wrong with America."',
'review_date': '2019-01-03',
'review_rating': 'Pants on Fire',
'review_url': 'https://www.politifact.com/facebook-fact-checks/statements/2019/jan/03/facebook-posts/did-michelle-obama-once-say-white-folks-are-whats-/',
'reviewer_name': 'PolitiFact'}
```
### Data Fields
A data instance has the following fields:
- `review_date`: the day the fact checking report was posted. Missing values are replaced with empty strings
- `review_url`: URL for the full fact checking report
- `reviewer_name`: the name of the fact checking service.
- `claim_text`: the full text of the claim being reviewed.
- `claim_author_name`: the author of the claim being reviewed. Missing values are replaced with empty strings
- `claim_date` the date of the claim. Missing values are replaced with empty strings
- `review_rating`: the judgments of the fact checker (under `alternateName`, names vary by fact checker)
### Data Splits
No splits are provided. There are a total of 5632 claims fact-checked.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The fact checking is done by [FactCheck.org](https://www.factcheck.org/), [PolitiFact](https://www.politifact.com/), [The Washington Post](https://www.washingtonpost.com/), and [The Weekly Standard](https://www.weeklystandard.com/).
- [FactCheck.org](https://www.factcheck.org/) self describes as "a nonpartisan, nonprofit 'consumer advocate' for voters that aims to reduce the level of deception and confusion in U.S. politics." It was founded by journalists Kathleen Hall Jamieson and Brooks Jackson and is currently directed by Eugene Kiely.
- [PolitiFact](https://www.politifact.com/) describe their ethics as "seeking to present the true facts, unaffected by agenda or biases, [with] journalists setting their own opinions aside." It was started in August 2007 by Times Washington Bureau Chief Bill Adair. The organization was acquired in February 2018 by the Poynter Institute, a non-profit journalism education and news media research center that also owns the Tampa Bay Times.
- [The Washington Post](https://www.washingtonpost.com/) is a newspaper considered to be near the center of the American political spectrum. In 2013 Amazon.com founder Jeff Bezos bought the newspaper and affiliated publications.
The original data source also contains 132 items reviewed by [The Weekly Standard](https://www.weeklystandard.com/), which was a neo-conservative American newspaper. IT is the most politically loaded source of the group, which was originally a vocal creitic of the activity of fact-checking, and has historically taken stances [close to the American right](https://en.wikipedia.org/wiki/The_Weekly_Standard#Support_of_the_invasion_of_Iraq). It also had to admit responsibility for baseless accusations against a well known author in a public [libel case](https://en.wikipedia.org/wiki/The_Weekly_Standard#Libel_case). The fact checked items from this source can be found in the `weekly_standard` configuration but should be used only with full understanding of this context.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
See section above describing the [fact checking organizations](#who-are-the-annotators?).
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
This fact checking dataset is maintained by [datacommons.org](https://datacommons.org/), a Google initiative.
### Licensing Information
All fact checked items are released under a `CC-BY-NC-4.0` License.
### Citation Information
Data Commons 2020, Fact Checks, electronic dataset, Data Commons, viewed 16 Dec 2020, <https://datacommons.org>.
### Contributions
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. | 7,577 | [
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] |
doqa | 2023-04-05T10:04:58.000Z | [
"language:en",
"arxiv:2005.01328",
"region:us"
] | null | DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs. | @misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 0 | 215 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: doqa
pretty_name: DoQA
dataset_info:
- config_name: cooking
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 2969064
num_examples: 1797
- name: validation
num_bytes: 1461613
num_examples: 911
- name: train
num_bytes: 6881681
num_examples: 4612
download_size: 4197671
dataset_size: 11312358
- config_name: movies
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 3166075
num_examples: 1884
download_size: 4197671
dataset_size: 3166075
- config_name: travel
features:
- name: title
dtype: string
- name: background
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: followup
dtype: string
- name: yesno
dtype: string
- name: orig_answer
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: test
num_bytes: 3216374
num_examples: 1713
download_size: 4197671
dataset_size: 3216374
---
# Dataset Card for "doqa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/RevanthRameshkumar/CRD3](https://github.com/RevanthRameshkumar/CRD3)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 17.70 MB
- **Total amount of disk used:** 30.28 MB
### Dataset Summary
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### cooking
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 11.31 MB
- **Total amount of disk used:** 15.51 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
#### movies
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 3.17 MB
- **Total amount of disk used:** 7.36 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
#### travel
- **Size of downloaded dataset files:** 4.19 MB
- **Size of the generated dataset:** 3.22 MB
- **Total amount of disk used:** 7.41 MB
An example of 'test' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"background": "\"So, over mixing batter forms gluten, which in turn hardens the cake. Fine.The problem is that I don't want lumps in the cakes, ...",
"context": "\"Milk won't help you - it's mostly water, and gluten develops from flour (more accurately, specific proteins in flour) and water...",
"followup": "n",
"id": "C_64ce44d5f14347f488eb04b50387f022_q#2",
"orig_answer": {
"answer_start": [852],
"text": ["CANNOTANSWER"]
},
"question": "Ok. What can I add to make it more softer and avoid hardening?",
"title": "What to add to the batter of the cake to avoid hardening when the gluten formation can't be avoided?",
"yesno": "x"
}
```
### Data Fields
The data fields are the same among all splits.
#### cooking
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### movies
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### travel
- `title`: a `string` feature.
- `background`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `followup`: a `string` feature.
- `yesno`: a `string` feature.
- `orig_answer`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
#### cooking
| |train|validation|test|
|-------|----:|---------:|---:|
|cooking| 4612| 911|1797|
#### movies
| |test|
|------|---:|
|movies|1884|
#### travel
| |test|
|------|---:|
|travel|1713|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@misc{campos2020doqa,
title={DoQA -- Accessing Domain-Specific FAQs via Conversational QA},
author={Jon Ander Campos and Arantxa Otegi and Aitor Soroa and Jan Deriu and Mark Cieliebak and Eneko Agirre},
year={2020},
eprint={2005.01328},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 12,661 | [
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poleval2019_mt | 2022-11-18T21:39:08.000Z | [
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:pl",
"language:ru",
"license:unknown",
"region:us"
] | null | PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according topre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4).
The task is to train as good as possible machine translation system, using any technology,with limited textual resources.The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourcedRussian-Polish (in both directions).
Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish. | null | 0 | 215 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
- found
language:
- en
- pl
- ru
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: Poleval2019Mt
dataset_info:
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features:
- name: translation
dtype:
translation:
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num_examples: 1
download_size: 13591306
dataset_size: 14426982
---
# Dataset Card for poleval2019_mt
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** PolEval-2019 competition. http://2019.poleval.pl/
- **Repository:** Links available [in this page](http://2019.poleval.pl/index.php/tasks/task4)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish.
Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to
pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4).
The task is to train as good as possible machine translation system, using any technology,with limited textual resources.
The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced
Russian-Polish (in both directions).
Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish
### Supported Tasks and Leaderboards
Supports Machine Translation between Russian to Polish and English to Polish (and vice versa).
### Languages
- Polish (pl)
- Russian (ru)
- English (en)
## Dataset Structure
### Data Instances
As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file.
### Data Fields
One example of the translation is as below:
```
{
'translation': {'ru': 'не содержала в себе моделей. Модели это сравнительно новое явление. ',
'pl': 'nie miała w sobie modeli. Modele to względnie nowa dziedzina. Tak więc, jeśli '}
}
```
### Data Splits
The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
| | train | validation | test |
|-------|-------:|-----------:|-----:|
| ru-pl | 20001 | 3001 | 2969 |
| pl-ru | 20001 | 3001 | 2969 |
| en-pl | 129255 | 1000 | 9845 |
## Dataset Creation
### Curation Rationale
This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions).
PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures.
PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019).
The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in [this](http://2019.poleval.pl/index.php/publication) link
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The organization details of PolEval is present in this [link](http://2019.poleval.pl/index.php/organizers)
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@proceedings{ogr:kob:19:poleval,
editor = {Maciej Ogrodniczuk and Łukasz Kobyliński},
title = {{Proceedings of the PolEval 2019 Workshop}},
year = {2019},
address = {Warsaw, Poland},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
url = {http://2019.poleval.pl/files/poleval2019.pdf},
isbn = "978-83-63159-28-3"}
}
```
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. | 7,237 | [
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] |
mxeval/multi-humaneval | 2023-03-20T19:20:48.000Z | [
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"mxeval",
"code-generation",
"multi-humaneval",
"humaneval",
"arxiv:2210.14868",
"region:us"
] | mxeval | A collection of execution-based multi-lingual benchmark for code generation. | @article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
Gonugondla, Sujan Kumar and
Ding, Hantian and
Kumar, Varun and
Fulton, Nathan and
Farahani, Arash and
Jain, Siddhartha and
Giaquinto, Robert and
Qian, Haifeng and
Ramanathan, Murali Krishna and
Nallapati, Ramesh and
Ray, Baishakhi and
Bhatia, Parminder and
Sengupta, Sudipta and
Roth, Dan and
Xiang, Bing},
doi = {10.48550/ARXIV.2210.14868},
url = {https://arxiv.org/abs/2210.14868},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
} | 3 | 215 | 2023-03-14T21:37:18 | ---
dataset_info:
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: prompt
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
splits:
- name: multi-humaneval_python
num_bytes: 165716
num_examples: 164
download_size: 67983
dataset_size: 165716
license: apache-2.0
task_categories:
- text-generation
tags:
- mxeval
- code-generation
- multi-humaneval
- humaneval
pretty_name: multi-humaneval
language:
- en
---
# Multi-HumanEval
## Table of Contents
- [multi-humaneval](#multi-humaneval)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Executional Correctness](#execution)
- [Execution Example](#execution-example)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
# multi-humaneval
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
### Dataset Summary
This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
### Related Tasks and Leaderboards
* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
* [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x)
### Languages
The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
## Dataset Structure
To lookup currently supported datasets
```python
get_dataset_config_names("mxeval/multi-humaneval")
['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']
```
To load a specific dataset and language
```python
from datasets import load_dataset
load_dataset("mxeval/multi-humaneval", "python")
DatasetDict({
test: Dataset({
features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
num_rows: 164
})
})
```
### Data Instances
An example of a dataset instance:
```python
{
"task_id": "HumanEval/0",
"language": "python",
"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
"test": "\n\nMETADATA = {\n \"author\": \"jt\",\n \"dataset\": \"test\"\n}\n\n\ndef check(candidate):\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert candidate([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert candidate([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\n",
"entry_point": "has_close_elements",
"canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
"description": "Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True"
}
```
### Data Fields
- `task_id`: identifier for the data sample
- `prompt`: input for the model containing function header and docstrings
- `canonical_solution`: solution for the problem in the `prompt`
- `description`: task description
- `test`: contains function to test generated code for correctness
- `entry_point`: entry point for test
- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
### Data Splits
- HumanXEval
- Python
- Csharp
- Go
- Java
- Javascript
- Kotlin
- Perl
- Php
- Ruby
- Scala
- Swift
- Typescript
## Dataset Creation
### Curation Rationale
Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
### Personal and Sensitive Information
None.
### Social Impact of Dataset
With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
## Execution
### Execution Example
Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset.
```python
>>> from datasets import load_dataset
>>> from mxeval.execution import check_correctness
>>> humaneval_python = load_dataset("mxeval/multi-humaneval", "python", split="test")
>>> example_problem = humaneval_python[0]
>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
{'task_id': 'HumanEval/0', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 9.636878967285156}
```
### Considerations for Using the Data
Make sure to sandbox the execution environment.
### Dataset Curators
AWS AI Labs
### Licensing Information
[LICENSE](https://huggingface.co/datasets/mxeval/multi-humaneval/blob/main/multi-humaneval-LICENSE) <br>
[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/multi-humaneval/blob/main/THIRD_PARTY_LICENSES)
### Citation Information
```
@article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
Gonugondla, Sujan Kumar and
Ding, Hantian and
Kumar, Varun and
Fulton, Nathan and
Farahani, Arash and
Jain, Siddhartha and
Giaquinto, Robert and
Qian, Haifeng and
Ramanathan, Murali Krishna and
Nallapati, Ramesh and
Ray, Baishakhi and
Bhatia, Parminder and
Sengupta, Sudipta and
Roth, Dan and
Xiang, Bing},
doi = {10.48550/ARXIV.2210.14868},
url = {https://arxiv.org/abs/2210.14868},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
### Contributions
[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi) | 8,125 | [
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] |
C-MTEB/CLSClusteringP2P | 2023-07-27T17:29:48.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 215 | 2023-07-27T17:29:10 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 56780231
num_examples: 10
download_size: 37254736
dataset_size: 56780231
---
# Dataset Card for "CLSClusteringP2P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 488 | [
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] |
yzhuang/autotree_automl_10000_electricity_sgosdt_l256_dim7_d3_sd0 | 2023-09-07T02:45:46.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 215 | 2023-09-07T02:45:40 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 205720000
num_examples: 10000
- name: validation
num_bytes: 205720000
num_examples: 10000
download_size: 102866704
dataset_size: 411440000
---
# Dataset Card for "autotree_automl_10000_electricity_sgosdt_l256_dim7_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 848 | [
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] |
hmao/new_vt_apis | 2023-10-26T00:50:57.000Z | [
"region:us"
] | hmao | null | null | 0 | 215 | 2023-10-13T04:28:16 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: args_dicts
list:
- name: default
dtype: string
- name: description
dtype: string
- name: name
dtype: string
- name: required
dtype: bool
- name: type
dtype: string
- name: api_type
dtype: string
- name: description
dtype: string
- name: name
dtype: string
- name: dataset
dtype: string
splits:
- name: train
num_bytes: 20764
num_examples: 29
download_size: 14860
dataset_size: 20764
---
# Dataset Card for "new_vt_apis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 772 | [
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lst20 | 2023-01-25T14:34:28.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:th",
"license:other",
"word-segmentation",
"clause-segmentation",
"sentence-segmentation",
"region:us"
] | null | LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php | @article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv preprint arXiv:2008.05055},
year={2020}
} | 2 | 214 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: LST20
tags:
- word-segmentation
- clause-segmentation
- sentence-segmentation
dataset_info:
features:
- name: id
dtype: string
- name: fname
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': NN
'1': VV
'2': PU
'3': CC
'4': PS
'5': AX
'6': AV
'7': FX
'8': NU
'9': AJ
'10': CL
'11': PR
'12': NG
'13': PA
'14': XX
'15': IJ
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B_BRN
'2': B_DES
'3': B_DTM
'4': B_LOC
'5': B_MEA
'6': B_NUM
'7': B_ORG
'8': B_PER
'9': B_TRM
'10': B_TTL
'11': I_BRN
'12': I_DES
'13': I_DTM
'14': I_LOC
'15': I_MEA
'16': I_NUM
'17': I_ORG
'18': I_PER
'19': I_TRM
'20': I_TTL
'21': E_BRN
'22': E_DES
'23': E_DTM
'24': E_LOC
'25': E_MEA
'26': E_NUM
'27': E_ORG
'28': E_PER
'29': E_TRM
'30': E_TTL
- name: clause_tags
sequence:
class_label:
names:
'0': O
'1': B_CLS
'2': I_CLS
'3': E_CLS
config_name: lst20
splits:
- name: train
num_bytes: 107725145
num_examples: 63310
- name: validation
num_bytes: 9646167
num_examples: 5620
- name: test
num_bytes: 8217425
num_examples: 5250
download_size: 0
dataset_size: 125588737
---
# Dataset Card for LST20
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://aiforthai.in.th/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [email](thepchai@nectec.or.th)
### Dataset Summary
LST20 Corpus is a dataset for Thai language processing developed by National Electronics and Computer Technology Center (NECTEC), Thailand.
It offers five layers of linguistic annotation: word boundaries, POS tagging, named entities, clause boundaries, and sentence boundaries.
At a large scale, it consists of 3,164,002 words, 288,020 named entities, 248,181 clauses, and 74,180 sentences, while it is annotated with
16 distinct POS tags. All 3,745 documents are also annotated with one of 15 news genres. Regarding its sheer size, this dataset is
considered large enough for developing joint neural models for NLP.
Manually download at https://aiforthai.in.th/corpus.php
See `LST20 Annotation Guideline.pdf` and `LST20 Brief Specification.pdf` within the downloaded `AIFORTHAI-LST20Corpus.tar.gz` for more details.
### Supported Tasks and Leaderboards
- POS tagging
- NER tagging
- clause segmentation
- sentence segmentation
- word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '0', 'ner_tags': [8, 0, 0, 0, 0, 0, 0, 0, 25], 'pos_tags': [0, 0, 0, 1, 0, 8, 8, 8, 0], 'tokens': ['ธรรมนูญ', 'แชมป์', 'สิงห์คลาสสิก', 'กวาด', 'รางวัล', 'แสน', 'สี่', 'หมื่น', 'บาท']}
{'clause_tags': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3], 'fname': 'T11964.txt', 'id': '1', 'ner_tags': [8, 18, 28, 0, 0, 0, 0, 6, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 15, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 6], 'pos_tags': [0, 2, 0, 2, 1, 1, 2, 8, 2, 10, 2, 8, 2, 1, 0, 1, 0, 4, 7, 1, 0, 2, 8, 2, 10, 1, 10, 4, 2, 8, 2, 4, 0, 4, 0, 2, 8, 2, 10, 2, 8], 'tokens': ['ธรรมนูญ', '_', 'ศรีโรจน์', '_', 'เก็บ', 'เพิ่ม', '_', '4', '_', 'อันเดอร์พาร์', '_', '68', '_', 'เข้า', 'ป้าย', 'รับ', 'แชมป์', 'ใน', 'การ', 'เล่น', 'อาชีพ', '_', '19', '_', 'ปี', 'เป็น', 'ครั้ง', 'ที่', '_', '8', '_', 'ใน', 'ชีวิต', 'ด้วย', 'สกอร์', '_', '18', '_', 'อันเดอร์พาร์', '_', '270']}
```
### Data Fields
- `id`: nth sentence in each set, starting at 0
- `fname`: text file from which the sentence comes from
- `tokens`: word tokens
- `pos_tags`: POS tags
- `ner_tags`: NER tags
- `clause_tags`: clause tags
### Data Splits
| | train | eval | test | all |
|----------------------|-----------|-------------|-------------|-----------|
| words | 2,714,848 | 240,891 | 207,295 | 3,163,034 |
| named entities | 246,529 | 23,176 | 18,315 | 288,020 |
| clauses | 214,645 | 17,486 | 16,050 | 246,181 |
| sentences | 63,310 | 5,620 | 5,250 | 74,180 |
| distinct words | 42,091 | (oov) 2,595 | (oov) 2,006 | 46,692 |
| breaking spaces※ | 63,310 | 5,620 | 5,250 | 74,180 |
| non-breaking spaces※※| 402,380 | 39,920 | 32,204 | 475,504 |
※ Breaking space = space that is used as a sentence boundary marker
※※ Non-breaking space = space that is not used as a sentence boundary marker
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the news articles
### Annotations
#### Annotation process
Detailed annotation guideline can be found in `LST20 Annotation Guideline.pdf`.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All texts are from public news. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- Large-scale Thai NER & POS tagging, clause & sentence segmentatation, word tokenization
### Discussion of Biases
- All 3,745 texts are from news domain:
- politics: 841
- crime and accident: 592
- economics: 512
- entertainment: 472
- sports: 402
- international: 279
- science, technology and education: 216
- health: 92
- general: 75
- royal: 54
- disaster: 52
- development: 45
- environment: 40
- culture: 40
- weather forecast: 33
- Word tokenization is done accoding to InterBEST 2009 Guideline.
### Other Known Limitations
- Some NER tags do not correspond with given labels (`B`, `I`, and so on)
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/en/)
### Licensing Information
1. Non-commercial use, research, and open source
Any non-commercial use of the dataset for research and open-sourced projects is encouraged and free of charge. Please cite our technical report for reference.
If you want to perpetuate your models trained on our dataset and share them to the research community in Thailand, please send your models, code, and APIs to the AI for Thai Project. Please contact Dr. Thepchai Supnithi via thepchai@nectec.or.th for more information.
Note that modification and redistribution of the dataset by any means are strictly prohibited unless authorized by the corpus authors.
2. Commercial use
In any commercial use of the dataset, there are two options.
- Option 1 (in kind): Contributing a dataset of 50,000 words completely annotated with our annotation scheme within 1 year. Your data will also be shared and recognized as a dataset co-creator in the research community in Thailand.
- Option 2 (in cash): Purchasing a lifetime license for the entire dataset is required. The purchased rights of use cover only this dataset.
In both options, please contact Dr. Thepchai Supnithi via thepchai@nectec.or.th for more information.
### Citation Information
```
@article{boonkwan2020annotation,
title={The Annotation Guideline of LST20 Corpus},
author={Boonkwan, Prachya and Luantangsrisuk, Vorapon and Phaholphinyo, Sitthaa and Kriengket, Kanyanat and Leenoi, Dhanon and Phrombut, Charun and Boriboon, Monthika and Kosawat, Krit and Supnithi, Thepchai},
journal={arXiv preprint arXiv:2008.05055},
year={2020}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | 9,481 | [
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sepidmnorozy/Korean_sentiment | 2022-08-16T09:25:48.000Z | [
"region:us"
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philschmid/emotion | 2023-01-20T14:56:20.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"emotion-classification",
"region:us"
] | philschmid | Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper. | @inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
} | 1 | 214 | 2023-01-20T14:56:20 | ---
pretty_name: Emotion
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: emotion
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
tags:
- emotion-classification
dataset_info:
- config_name: split
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 1741597
num_examples: 16000
- name: validation
num_bytes: 214703
num_examples: 2000
- name: test
num_bytes: 217181
num_examples: 2000
download_size: 740883
dataset_size: 2173481
- config_name: unsplit
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': sadness
'1': joy
'2': love
'3': anger
'4': fear
'5': surprise
splits:
- name: train
num_bytes: 45445685
num_examples: 416809
download_size: 15388281
dataset_size: 45445685
duplicated_from: emotion
---
# Dataset Card for "emotion"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.95 MB
- **Size of the generated dataset:** 4.16 MB
- **Total amount of disk used:** 8.11 MB
### Dataset Summary
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
### Data Splits
The dataset has 2 configurations:
- split: with a total of 20_000 examples split into train, validation and split
- unsplit: with a total of 416_809 examples in a single train split
| name | train | validation | test |
|---------|-------:|-----------:|-----:|
| split | 16000 | 2000 | 2000 |
| unsplit | 416809 | n/a | n/a |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset should be used for educational and research purposes only.
### Citation Information
If you use this dataset, please cite:
```
@inproceedings{saravia-etal-2018-carer,
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
author = "Saravia, Elvis and
Liu, Hsien-Chi Toby and
Huang, Yen-Hao and
Wu, Junlin and
Chen, Yi-Shin",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1404",
doi = "10.18653/v1/D18-1404",
pages = "3687--3697",
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
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axiong/pmc_oa | 2023-08-22T17:42:06.000Z | [
"region:us"
] | axiong | Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity.
To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral's OpenAccess subset, which is 8 times larger than before.
PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption.
While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP achieves state-of-the-art results on various downstream tasks,
including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, i.e. +8.1% R@10 on image-text retrieval, +3.9% accuracy on image classification. | @article{lin2023pmc,
title={PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents},
author={Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
journal={arXiv preprint arXiv:2303.07240},
year={2023}
} | 15 | 214 | 2023-04-02T02:30:31 | # PMC-OA Dataset
**News: We have released the PMC-OA dataset. You can choose the subset specifically.**
**P.S.** There's something wrong with the huggingface dataset viewer when the dataset scale gets large.
So we sample a subset of it to visualize it directly on web. Click [PMC-OA-Demo](https://huggingface.co/datasets/axiong/pmc_oa_demo) to view it.
[中文文档](./README.zh.md)
- [PMC-OA Dataset](#pmc-oa-dataset)
- [Model Zoo](#model-zoo)
- [Daraset Structure](#daraset-structure)
- [Sample](#sample)
## Model Zoo
Check it out if you want to load model pretrained on PMC-OA directly.
We plan to release more models pretrained on PMC-OA. Feel free to reach us if the model you want is not included in model zoo for now.
Also, we express our thanks to the help from the community.
| Model | Link | Provider |
| --- | --- | --- |
| ViT-L-14 | https://huggingface.co/ryanyip7777/pmc_vit_l_14 | @ryanyip7777 |
## Daraset Structure
**PMC-OA** (seperated images, separated caption).
- `images.zip`: images folder
- `pmc_oa.jsonl`: dataset file of pmc-oa
- `pmc_oa_beta.jsonl`: dataset file of pmc-oa-beta
~~- `train.jsonl`: metafile of train set~~
~~- `valid.jsonl`: metafile of valid set~~
~~- `test.jsonl`: metafile of test set~~
The difference between PMC-OA & PMC-OA-Beta lies in the methods of processing captions.
In PMC-OA, we utilize ChatGPT to help us divide compound captions into seperate ones.
While PMC-OA-Beta keeps all the compound ones without division.
## Sample
A row in `pmc_oa.jsonl` is shown bellow,
```python
{
"image": "PMC212319_Fig3_4.jpg",
"caption": "A. Real time image of the translocation of ARF1-GFP to the plasma membrane ...",
}
```
Explanation to each key
- image: path to the image
- caption: corresponding to the image
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] |
IlyaGusev/ru_turbo_saiga | 2023-09-04T13:26:47.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:ru",
"license:cc-by-4.0",
"chat",
"region:us"
] | IlyaGusev | null | null | 11 | 214 | 2023-04-08T20:53:59 | ---
dataset_info:
features:
- name: messages
sequence:
- name: role
dtype: string
- name: content
dtype: string
- name: seed
dtype: string
- name: source
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 87316730
num_examples: 37731
download_size: 21742388
dataset_size: 87316730
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- ru
tags:
- chat
size_categories:
- 10K<n<100K
---
# Saiga
Dataset of ChatGPT-generated chats in Russian.
<img src="https://cdn.midjourney.com/0db33d04-9d39-45f3-acb2-e5c789852e23/0_3.png" >
Based on the [Baize](https://github.com/project-baize/baize-chatbot) paper.
Code: [link](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chat.py).
Prompt:
```
Идёт диалог между пользователем и ИИ ассистентом.
Пользователь и ассистент общаются на тему: {{seed}}
Реплики человека начинаются с [Пользователь], реплики ассистента начинаются с [Ассистент].
Пользователь задаёт вопросы на основе темы и предыдущих сообщений.
Пользователь обрывает беседу, когда у него не остается вопросов.
Ассистент даёт максимально полные, информативные, точные и творческие ответы.
Ассистент старается не задавать вопросов, за исключением уточняющих.
Ассистент может отвечать несколькими абзацами.
Ассистент может использовать Markdown.
Закончи диалог точно в таком же формате.
[Пользователь] Привет!
[Ассистент] Привет! Чем я могу помочь?
```
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit for us developing models that compete with OpenAI. Not for you. | 1,723 | [
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] |
yuvalkirstain/pickapic_v1_no_images | 2023-04-16T14:53:35.000Z | [
"region:us"
] | yuvalkirstain | null | null | 0 | 214 | 2023-04-16T14:52:20 | ---
dataset_info:
features:
- name: are_different
dtype: bool
- name: best_image_uid
dtype: string
- name: caption
dtype: string
- name: created_at
dtype: timestamp[ns]
- name: has_label
dtype: bool
- name: image_0_uid
dtype: string
- name: image_0_url
dtype: string
- name: image_1_uid
dtype: string
- name: image_1_url
dtype: string
- name: label_0
dtype: float64
- name: label_1
dtype: float64
- name: model_0
dtype: string
- name: model_1
dtype: string
- name: ranking_id
dtype: int64
- name: user_id
dtype: int64
- name: num_example_per_prompt
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 308923563
num_examples: 583747
- name: validation
num_bytes: 8759568
num_examples: 17439
- name: test
num_bytes: 7194410
num_examples: 14073
- name: validation_unique
num_bytes: 248229
num_examples: 500
- name: test_unique
num_bytes: 256313
num_examples: 500
download_size: 175013617
dataset_size: 325382083
---
# Dataset Card for "pick_a_pic_v1_no_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,283 | [
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C-MTEB/IFlyTek-classification | 2023-07-28T13:30:24.000Z | [
"region:us"
] | C-MTEB | null | null | 1 | 214 | 2023-07-28T13:30:02 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
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- name: idx
dtype: int32
splits:
- name: test
num_bytes: 2105684
num_examples: 2600
- name: train
num_bytes: 10028605
num_examples: 12133
- name: validation
num_bytes: 2157119
num_examples: 2599
download_size: 9777643
dataset_size: 14291408
---
# Dataset Card for "IFlyTek-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 3,278 | [
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banghua/hh_reward_model_labeled | 2023-08-06T02:03:27.000Z | [
"region:us"
] | banghua | null | null | 0 | 214 | 2023-08-04T21:23:15 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 225756769
num_examples: 124503
download_size: 136142109
dataset_size: 225756769
---
# Dataset Card for "hh_reward_model_labeled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 484 | [
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distil-whisper/meanwhile | 2023-10-17T17:17:28.000Z | [
"arxiv:2212.04356",
"region:us"
] | distil-whisper | null | null | 0 | 214 | 2023-09-19T15:45:32 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
- name: begin
dtype: string
- name: end
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 58250833.0
num_examples: 64
download_size: 58229969
dataset_size: 58250833.0
---
# Dataset Card for "meanwhile"
This dataset consists of 64 segments from The Late Show with Stephen Colbert. This dataset was published as
part of the Whisper release by OpenAI. See page 19 of the [Whisper paper](https://arxiv.org/pdf/2212.04356.pdf)
for details. | 641 | [
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nlu_evaluation_data | 2023-01-25T14:41:34.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1903.05566",
"region:us"
] | null | Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios. | @InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)},
month = {April},
year = {2019},
address = {Ortigia, Siracusa (SR), Italy},
publisher = {Springer},
pages = {xxx--xxx},
url = {http://www.xx.xx/xx/}
} | 7 | 213 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
pretty_name: NLU Evaluation Data
dataset_info:
features:
- name: text
dtype: string
- name: scenario
dtype: string
- name: label
dtype:
class_label:
names:
'0': alarm_query
'1': alarm_remove
'2': alarm_set
'3': audio_volume_down
'4': audio_volume_mute
'5': audio_volume_other
'6': audio_volume_up
'7': calendar_query
'8': calendar_remove
'9': calendar_set
'10': cooking_query
'11': cooking_recipe
'12': datetime_convert
'13': datetime_query
'14': email_addcontact
'15': email_query
'16': email_querycontact
'17': email_sendemail
'18': general_affirm
'19': general_commandstop
'20': general_confirm
'21': general_dontcare
'22': general_explain
'23': general_greet
'24': general_joke
'25': general_negate
'26': general_praise
'27': general_quirky
'28': general_repeat
'29': iot_cleaning
'30': iot_coffee
'31': iot_hue_lightchange
'32': iot_hue_lightdim
'33': iot_hue_lightoff
'34': iot_hue_lighton
'35': iot_hue_lightup
'36': iot_wemo_off
'37': iot_wemo_on
'38': lists_createoradd
'39': lists_query
'40': lists_remove
'41': music_dislikeness
'42': music_likeness
'43': music_query
'44': music_settings
'45': news_query
'46': play_audiobook
'47': play_game
'48': play_music
'49': play_podcasts
'50': play_radio
'51': qa_currency
'52': qa_definition
'53': qa_factoid
'54': qa_maths
'55': qa_stock
'56': recommendation_events
'57': recommendation_locations
'58': recommendation_movies
'59': social_post
'60': social_query
'61': takeaway_order
'62': takeaway_query
'63': transport_query
'64': transport_taxi
'65': transport_ticket
'66': transport_traffic
'67': weather_query
splits:
- name: train
num_bytes: 1447941
num_examples: 25715
download_size: 5867439
dataset_size: 1447941
---
# Dataset Card for NLU Evaluation Data
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
- **Repository:** [Github](https://github.com/xliuhw/NLU-Evaluation-Data)
- **Paper:** [ArXiv](https://arxiv.org/abs/1903.05566)
- **Leaderboard:**
- **Point of Contact:** [x.liu@hw.ac.uk](mailto:x.liu@hw.ac.uk)
### Dataset Summary
Dataset with short utterances from conversational domain annotated with their corresponding intents and scenarios.
It has 25 715 non-zero examples (original dataset has 25716 examples) belonging to 18 scenarios and 68 intents.
Originally, the dataset was crowd-sourced and annotated with both intents and named entities
in order to evaluate commercial NLU systems such as RASA, IBM's Watson, Microsoft's LUIS and Google's Dialogflow.
**This version of the dataset only includes intent annotations!**
In contrast to paper claims, released data contains 68 unique intents. This is due to the fact, that NLU systems were
evaluated on more curated part of this dataset which only included 64 most important intents. Read more in [github issue](https://github.com/xliuhw/NLU-Evaluation-Data/issues/5).
### Supported Tasks and Leaderboards
Intent classification, intent detection
### Languages
English
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'label': 2, # integer label corresponding to "alarm_set" intent
'scenario': 'alarm',
'text': 'wake me up at five am this week'
}
```
### Data Fields
- `text`: a string feature.
- `label`: one of classification labels (0-67) corresponding to unique intents.
- `scenario`: a string with one of unique scenarios (18).
Intent names are mapped to `label` in the following way:
| label | intent |
|--------:|:-------------------------|
| 0 | alarm_query |
| 1 | alarm_remove |
| 2 | alarm_set |
| 3 | audio_volume_down |
| 4 | audio_volume_mute |
| 5 | audio_volume_other |
| 6 | audio_volume_up |
| 7 | calendar_query |
| 8 | calendar_remove |
| 9 | calendar_set |
| 10 | cooking_query |
| 11 | cooking_recipe |
| 12 | datetime_convert |
| 13 | datetime_query |
| 14 | email_addcontact |
| 15 | email_query |
| 16 | email_querycontact |
| 17 | email_sendemail |
| 18 | general_affirm |
| 19 | general_commandstop |
| 20 | general_confirm |
| 21 | general_dontcare |
| 22 | general_explain |
| 23 | general_greet |
| 24 | general_joke |
| 25 | general_negate |
| 26 | general_praise |
| 27 | general_quirky |
| 28 | general_repeat |
| 29 | iot_cleaning |
| 30 | iot_coffee |
| 31 | iot_hue_lightchange |
| 32 | iot_hue_lightdim |
| 33 | iot_hue_lightoff |
| 34 | iot_hue_lighton |
| 35 | iot_hue_lightup |
| 36 | iot_wemo_off |
| 37 | iot_wemo_on |
| 38 | lists_createoradd |
| 39 | lists_query |
| 40 | lists_remove |
| 41 | music_dislikeness |
| 42 | music_likeness |
| 43 | music_query |
| 44 | music_settings |
| 45 | news_query |
| 46 | play_audiobook |
| 47 | play_game |
| 48 | play_music |
| 49 | play_podcasts |
| 50 | play_radio |
| 51 | qa_currency |
| 52 | qa_definition |
| 53 | qa_factoid |
| 54 | qa_maths |
| 55 | qa_stock |
| 56 | recommendation_events |
| 57 | recommendation_locations |
| 58 | recommendation_movies |
| 59 | social_post |
| 60 | social_query |
| 61 | takeaway_order |
| 62 | takeaway_query |
| 63 | transport_query |
| 64 | transport_taxi |
| 65 | transport_ticket |
| 66 | transport_traffic |
| 67 | weather_query |
### Data Splits
| Dataset statistics | Train |
| --- | --- |
| Number of examples | 25 715 |
| Average character length | 34.32 |
| Number of intents | 68 |
| Number of scenarios | 18 |
## Dataset Creation
### Curation Rationale
The dataset was prepared for a wide coverage evaluation and comparison of some of the most popular NLU services.
At that time, previous benchmarks were done with few intents and spawning limited number of domains. Here, the dataset
is much larger and contains 68 intents from 18 scenarios, which is much larger that any previous evaluation. For more discussion see the paper.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
> To build the NLU component we collected real user data via Amazon Mechanical Turk (AMT). We designed tasks where the Turker’s goal was to answer questions about how people would interact with the home robot, in a wide range of scenarios designed in advance, namely: alarm, audio, audiobook, calendar, cooking, datetime, email, game, general, IoT, lists, music, news, podcasts, general Q&A, radio, recommendations, social, food takeaway, transport, and weather.
The questions put to Turkers were designed to capture the different requests within each given scenario.
In the ‘calendar’ scenario, for example, these pre-designed intents were included: ‘set event’, ‘delete event’ and ‘query event’.
An example question for intent ‘set event’ is: “How would you ask your PDA to schedule a meeting with someone?” for which a user’s answer example was “Schedule a chat with Adam on Thursday afternoon”.
The Turkers would then type in their answers to these questions and select possible entities from the pre-designed suggested entities list for each of their answers.The Turkers didn’t always follow the instructions fully, e.g. for the specified ‘delete event’ Intent, an answer was: “PDA what is my next event?”; which clearly belongs to ‘query event’ Intent.
We have manually corrected all such errors either during post-processing or the subsequent annotations.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset it to help develop better intent detection systems.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International License (CC BY 4.0)
### Citation Information
```
@InProceedings{XLiu.etal:IWSDS2019,
author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)},
month = {April},
year = {2019},
address = {Ortigia, Siracusa (SR), Italy},
publisher = {Springer},
pages = {xxx--xxx},
url = {http://www.xx.xx/xx/}
}
```
### Contributions
Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset. | 11,633 | [
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EMBO/BLURB | 2022-12-09T07:57:37.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:closed-domain-qa",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2007.15779",
"arxiv:1909.06146",
"region:us"
] | EMBO | null | null | 3 | 213 | 2022-03-14T10:29:16 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
- sentence-similarity
- text-classification
task_ids:
- closed-domain-qa
- named-entity-recognition
- parsing
- semantic-similarity-scoring
- text-scoring
- topic-classification
pretty_name: BLURB (Biomedical Language Understanding and Reasoning Benchmark.)
---
# Dataset Card for BLURB
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://microsoft.github.io/BLURB/index.html
- **Paper:** [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://arxiv.org/pdf/2007.15779.pdf)
- **Leaderboard:** https://microsoft.github.io/BLURB/leaderboard.html
- **Point of Contact:**
### Dataset Summary
BLURB is a collection of resources for biomedical natural language processing. In general domains, such as newswire and the Web, comprehensive benchmarks and leaderboards such as GLUE have greatly accelerated progress in open-domain NLP. In biomedicine, however, such resources are ostensibly scarce. In the past, there have been a plethora of shared tasks in biomedical NLP, such as BioCreative, BioNLP Shared Tasks, SemEval, and BioASQ, to name just a few. These efforts have played a significant role in fueling interest and progress by the research community, but they typically focus on individual tasks. The advent of neural language models, such as BERT provides a unifying foundation to leverage transfer learning from unlabeled text to support a wide range of NLP applications. To accelerate progress in biomedical pretraining strategies and task-specific methods, it is thus imperative to create a broad-coverage benchmark encompassing diverse biomedical tasks.
Inspired by prior efforts toward this direction (e.g., BLUE), we have created BLURB (short for Biomedical Language Understanding and Reasoning Benchmark). BLURB comprises of a comprehensive benchmark for PubMed-based biomedical NLP applications, as well as a leaderboard for tracking progress by the community. BLURB includes thirteen publicly available datasets in six diverse tasks. To avoid placing undue emphasis on tasks with many available datasets, such as named entity recognition (NER), BLURB reports the macro average across all tasks as the main score. The BLURB leaderboard is model-agnostic. Any system capable of producing the test predictions using the same training and development data can participate. The main goal of BLURB is to lower the entry barrier in biomedical NLP and help accelerate progress in this vitally important field for positive societal and human impact.
#### BC5-chem
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC5-disease
The corpus consists of three separate sets of
articles with diseases, chemicals and their relations annotated.
The training (500 articles) and development (500 articles) sets
were released to task participants in advance to support text-mining
method development. The test set (500 articles) was used for final
system performance evaluation.
- **Homepage:** https://biocreative.bioinformatics.udel.edu/resources/corpora/biocreative-v-cdr-corpus
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [BioCreative V CDR task corpus: a resource for chemical disease relation extraction](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860626/)
#### BC2GM
The BioCreative II Gene Mention task.
The training corpus for the current task consists mainly of
the training and testing corpora (text collections) from the
BCI task, and the testing corpus for the current task
consists of an additional 5,000 sentences that were held
'in reserve' from the previous task.
In the current corpus, tokenization is not provided;
instead participants are asked to identify a gene mention
in a sentence by giving its start and end characters.
As before, the training set consists of a set of sentences,
and for each sentence a set of gene mentions
(GENE annotations).
- **Homepage:** https://biocreative.bioinformatics.udel.edu/tasks/biocreative-ii/task-1a-gene-mention-tagging/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [verview of BioCreative II gene mention recognition](https://link.springer.com/article/10.1186/gb-2008-9-s2-s2)
#### NCBI Disease
The NCBI disease corpus is fully annotated at the mention
and concept level to serve as a research resource for the biomedical natural
language processing community.
Corpus Characteristics
----------------------
* 793 PubMed abstracts
* 6,892 disease mentions
* 790 unique disease concepts
* Medical Subject Headings (MeSH®)
* Online Mendelian Inheritance in Man (OMIM®)
* 91% of the mentions map to a single disease concept
**divided into training, developing and testing sets.
Corpus Annotation
* Fourteen annotators
* Two-annotators per document (randomly paired)
* Three annotation phases
* Checked for corpus-wide consistency of annotations
- **Homepage:** https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper:** [NCBI disease corpus: a resource for disease name recognition and concept normalization](https://pubmed.ncbi.nlm.nih.gov/24393765/)
#### JNLPBA
The BioNLP / JNLPBA Shared Task 2004 involves the identification
and classification of technical terms referring to concepts of interest to
biologists in the domain of molecular biology. The task was organized by GENIA
Project based on the annotations of the GENIA Term corpus (version 3.02).
Corpus format: The JNLPBA corpus is distributed in IOB format, with each line
containing a single token and its tag, separated by a tab character.
Sentences are separated by blank lines.
- **Homepage: ** http://www.geniaproject.org/shared-tasks/bionlp-jnlpba-shared-task-2004
- **Repository:** [NER GitHub repo by @GamalC](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/raw/master/data/)
- **Paper: ** [Introduction to the Bio-entity Recognition Task at JNLPBA](https://aclanthology.org/W04-1213)
#### EBM PICO
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
#### ChemProt
- **Homepage:**
- **Repository:**
- **Paper:**
#### DDI
- **Homepage:**
- **Repository:**
- **Paper:**
#### GAD
- **Homepage:**
- **Repository:**
- **Paper:**
#### BIOSSES
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com)
#### HoC
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
#### PubMedQA
We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at this https URL.
- **Homepage:** https://pubmedqa.github.io/
- **Repository:** https://github.com/pubmedqa/pubmedqa
- **Paper:** [PubMedQA: A Dataset for Biomedical Research Question Answering](https://arxiv.org/pdf/1909.06146.pdf)
- **Leaderboard:** [Question answering](https://pubmedqa.github.io/)
- **Point of Contact:**
#### BioASQ
Task 7b will use benchmark datasets containing training and test biomedical questions, in English, along with gold standard (reference) answers. The participants will have to respond to each test question with relevant concepts (from designated terminologies and ontologies), relevant articles (in English, from designated article repositories), relevant snippets (from the relevant articles), relevant RDF triples (from designated ontologies), exact answers (e.g., named entities in the case of factoid questions) and 'ideal' answers (English paragraph-sized summaries). 2747 training questions (that were used as dry-run or test questions in previous year) are already available, along with their gold standard answers (relevant concepts, articles, snippets, exact answers, summaries).
- **Homepage:** http://bioasq.org/
- **Repository:** http://participants-area.bioasq.org/datasets/
- **Paper:** [Automatic semantic classification of scientific literature according to the hallmarks of cancer](https://academic.oup.com/bioinformatics/article/32/3/432/1743783?login=false)
### Supported Tasks and Leaderboards
| **Dataset** | **Task** | **Train** | **Dev** | **Test** | **Evaluation Metrics** | **Added** |
|:------------:|:-----------------------:|:---------:|:-------:|:--------:|:----------------------:|-----------|
| BC5-chem | NER | 5203 | 5347 | 5385 | F1 entity-level | **Yes** |
| BC5-disease | NER | 4182 | 4244 | 4424 | F1 entity-level | **Yes** |
| NCBI-disease | NER | 5134 | 787 | 960 | F1 entity-level | **Yes** |
| BC2GM | NER | 15197 | 3061 | 6325 | F1 entity-level | **Yes** |
| JNLPBA | NER | 46750 | 4551 | 8662 | F1 entity-level | **Yes** |
| EBM PICO | PICO | 339167 | 85321 | 16364 | Macro F1 word-level | No |
| ChemProt | Relation Extraction | 18035 | 11268 | 15745 | Micro F1 | No |
| DDI | Relation Extraction | 25296 | 2496 | 5716 | Micro F1 | No |
| GAD | Relation Extraction | 4261 | 535 | 534 | Micro F1 | No |
| BIOSSES | Sentence Similarity | 64 | 16 | 20 | Pearson | **Yes** |
| HoC | Document Classification | 1295 | 186 | 371 | Average Micro F1 | No |
| PubMedQA | Question Answering | 450 | 50 | 500 | Accuracy | **Yes** |
| BioASQ | Question Answering | 670 | 75 | 140 | Accuracy | No |
Datasets used in the BLURB biomedical NLP benchmark. The Train, Dev, and test splits might not be exactly identical to those proposed in BLURB.
This is something to be checked.
### Languages
English from biomedical texts
## Dataset Structure
### Data Instances
* **NER**
```json
{
'id': 0,
'tokens': [ "DPP6", "as", "a", "candidate", "gene", "for", "neuroleptic", "-", "induced", "tardive", "dyskinesia", "." ]
'ner_tags': [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
}
```
* **PICO**
```json
{
'TBD'
}
```
* **Relation Extraction**
```json
{
'TBD'
}
```
* **Sentence Similarity**
```json
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
* **Document Classification**
```json
{
'TBD'
}
```
* **Question Answering**
* PubMedQA
```json
{'context': {'contexts': ['Programmed cell death (PCD) is the regulated death of cells within an organism. The lace plant (Aponogeton madagascariensis) produces perforations in its leaves through PCD. The leaves of the plant consist of a latticework of longitudinal and transverse veins enclosing areoles. PCD occurs in the cells at the center of these areoles and progresses outwards, stopping approximately five cells from the vasculature. The role of mitochondria during PCD has been recognized in animals; however, it has been less studied during PCD in plants.',
'The following paper elucidates the role of mitochondrial dynamics during developmentally regulated PCD in vivo in A. madagascariensis. A single areole within a window stage leaf (PCD is occurring) was divided into three areas based on the progression of PCD; cells that will not undergo PCD (NPCD), cells in early stages of PCD (EPCD), and cells in late stages of PCD (LPCD). Window stage leaves were stained with the mitochondrial dye MitoTracker Red CMXRos and examined. Mitochondrial dynamics were delineated into four categories (M1-M4) based on characteristics including distribution, motility, and membrane potential (ΔΨm). A TUNEL assay showed fragmented nDNA in a gradient over these mitochondrial stages. Chloroplasts and transvacuolar strands were also examined using live cell imaging. The possible importance of mitochondrial permeability transition pore (PTP) formation during PCD was indirectly examined via in vivo cyclosporine A (CsA) treatment. This treatment resulted in lace plant leaves with a significantly lower number of perforations compared to controls, and that displayed mitochondrial dynamics similar to that of non-PCD cells.'],
'labels': ['BACKGROUND', 'RESULTS'],
'meshes': ['Alismataceae',
'Apoptosis',
'Cell Differentiation',
'Mitochondria',
'Plant Leaves'],
'reasoning_free_pred': ['y', 'e', 's'],
'reasoning_required_pred': ['y', 'e', 's']},
'final_decision': 'yes',
'long_answer': 'Results depicted mitochondrial dynamics in vivo as PCD progresses within the lace plant, and highlight the correlation of this organelle with other organelles during developmental PCD. To the best of our knowledge, this is the first report of mitochondria and chloroplasts moving on transvacuolar strands to form a ring structure surrounding the nucleus during developmental PCD. Also, for the first time, we have shown the feasibility for the use of CsA in a whole plant system. Overall, our findings implicate the mitochondria as playing a critical and early role in developmentally regulated PCD in the lace plant.',
'pubid': 21645374,
'question': 'Do mitochondria play a role in remodelling lace plant leaves during programmed cell death?'}
```
### Data Fields
* **NER**
* `id`: string
* `ner_tags`: Sequence[ClassLabel]
* `tokens`: Sequence[String]
* **PICO**
* To be added
* **Relation Extraction**
* To be added
* **Sentence Similarity**
* `sentence 1`: string
* `sentence 2`: string
* `score`: float ranging from 0 (no relation) to 4 (equivalent)
* **Document Classification**
* To be added
* **Question Answering**
* PubMedQA
* `pubid`: integer
* `question`: string
* `context`: sequence of strings [`contexts`, `labels`, `meshes`, `reasoning_required_pred`, `reasoning_free_pred`]
* `long_answer`: string
* `final_decision`: string
### Data Splits
Shown in the table of supported tasks.
## Dataset Creation
### Curation Rationale
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
* HoC
* PubMedQA
* BioASQ
### Source Data
[More Information Needed]
### Annotations
All the datasets have been obtained and annotated by experts in the biomedical domain. Check the different citations for further details.
#### Annotation process
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
* HoC
* PubMedQA
* BioASQ
### Dataset Curators
All the datasets have been obtained and annotated by experts in thebiomedical domain. Check the different citations for further details.
### Licensing Information
* BC5-chem
* BC5-disease
* BC2GM
* JNLPBA
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES - BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
* HoC
* PubMedQA - MIT License Copyright (c) 2019 pubmedqa
* BioASQ
### Citation Information
* BC5-chem & BC5-disease
```latex
@article{article,
author = {Li, Jiao and Sun, Yueping and Johnson, Robin and Sciaky, Daniela and Wei, Chih-Hsuan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn and Wiegers, Thomas and lu, Zhiyong},
year = {2016},
month = {05},
pages = {baw068},
title = {BioCreative V CDR task corpus: a resource for chemical disease relation extraction},
volume = {2016},
journal = {Database},
doi = {10.1093/database/baw068}
}
```
* BC2GM
```latex
@article{article,
author = {Smith, Larry and Tanabe, Lorraine and Ando, Rie and Kuo, Cheng-Ju and Chung, I-Fang and Hsu, Chun-Nan and Lin, Yu-Shi and Klinger, Roman and Friedrich, Christoph and Ganchev, Kuzman and Torii, Manabu and Liu, Hongfang and Haddow, Barry and Struble, Craig and Povinelli, Richard and Vlachos, Andreas and Baumgartner Jr, William and Hunter, Lawrence and Carpenter, Bob and Wilbur, W.},
year = {2008},
month = {09},
pages = {S2},
title = {Overview of BioCreative II gene mention recognition},
volume = {9 Suppl 2},
journal = {Genome biology},
doi = {10.1186/gb-2008-9-s2-s2}
}
```
* JNLPBA
```latex
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
```
* NCBI Disiease
```latex
@article{10.5555/2772763.2772800,
author = {Dogan, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
title = {NCBI Disease Corpus},
year = {2014},
issue_date = {February 2014},
publisher = {Elsevier Science},
address = {San Diego, CA, USA},
volume = {47},
number = {C},
issn = {1532-0464},
abstract = {Graphical abstractDisplay Omitted NCBI disease corpus is built as a gold-standard resource for disease recognition.793 PubMed abstracts are annotated with disease mentions and concepts (MeSH/OMIM).14 Annotators produced high consistency level and inter-annotator agreement.Normalization benchmark results demonstrate the utility of the corpus.The corpus is publicly available to the community. Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora.This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH ) or Online Mendelian Inheritance in Man (OMIM ). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.},
journal = {J. of Biomedical Informatics},
month = {feb},
pages = {1–10},
numpages = {10}}
```
* EBM PICO
* ChemProt
* DDI
* GAD
* BIOSSES
```latex
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
```
* HoC
* PubMedQA
```latex
@inproceedings{jin2019pubmedqa,
title={PubMedQA: A Dataset for Biomedical Research Question Answering},
author={Jin, Qiao and Dhingra, Bhuwan and Liu, Zhengping and Cohen, William and Lu, Xinghua},
booktitle={Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
pages={2567--2577},
year={2019}
}
```
* BioASQ
```latex
@article{10.1093/bioinformatics/btv585,
author = {Baker, Simon and Silins, Ilona and Guo, Yufan and Ali, Imran and Högberg, Johan and Stenius, Ulla and Korhonen, Anna},
title = "{Automatic semantic classification of scientific literature according to the hallmarks of cancer}",
journal = {Bioinformatics},
volume = {32},
number = {3},
pages = {432-440},
year = {2015},
month = {10},
abstract = "{Motivation: The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research.Results: We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future.Availability and implementation: The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html .Contact:simon.baker@cl.cam.ac.uk}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btv585},
url = {https://doi.org/10.1093/bioinformatics/btv585},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/32/3/432/19568147/btv585.pdf},
}
```
### Contributions
* This dataset has been uploaded and generated by Dr. Jorge Abreu Vicente.
* Thanks to [@GamalC](https://github.com/GamalC) for uploading the NER datasets to GitHub, from where I got them.
* I am not part of the team that generated BLURB. This dataset is intended to help researchers to usethe BLURB benchmarking for NLP in Biomedical NLP.
* Thanks to [@bwang482](https://github.com/bwang482) for uploading the [BIOSSES dataset](https://github.com/bwang482/datasets/tree/master/datasets/biosses). We forked the [BIOSSES 🤗 dataset](https://huggingface.co/datasets/biosses) to add it to this BLURB benchmark.
* Thank you to [@tuner007](https://github.com/tuner007) for adding this dataset to the 🤗 hub | 30,312 | [
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tonytan48/Re-DocRED | 2022-11-25T02:48:32.000Z | [
"license:mit",
"arxiv:2205.12696",
"region:us"
] | tonytan48 | null | null | 0 | 213 | 2022-11-25T02:42:48 | ---
license: mit
---
# Re-DocRED Dataset
This repository contains the dataset of our EMNLP 2022 research paper [Revisiting DocRED – Addressing the False Negative Problem
in Relation Extraction](https://arxiv.org/pdf/2205.12696.pdf).
DocRED is a widely used benchmark for document-level relation extraction. However, the DocRED dataset contains a significant percentage of false negative examples (incomplete annotation). We revised 4,053 documents in the DocRED dataset and resolved its problems. We released this dataset as: Re-DocRED dataset.
The Re-DocRED Dataset resolved the following problems of DocRED:
1. Resolved the incompleteness problem by supplementing large amounts of relation triples.
2. Addressed the logical inconsistencies in DocRED.
3. Corrected the coreferential errors within DocRED.
# Statistics of Re-DocRED
The Re-DocRED dataset is located as ./data directory, the statistics of the dataset are shown below:
| | Train | Dev | Test |
| :---: | :-: | :-: |:-: |
| # Documents | 3,053 | 500 | 500 |
| Avg. # Triples | 28.1 | 34.6 | 34.9 |
| Avg. # Entities | 19.4 | 19.4 | 19.6 |
| Avg. # Sents | 7.9 | 8.2 | 7.9 |
# Citation
If you find our work useful, please cite our work as:
```bibtex
@inproceedings{tan2022revisiting,
title={Revisiting DocRED – Addressing the False Negative Problem in Relation Extraction},
author={Tan, Qingyu and Xu, Lu and Bing, Lidong and Ng, Hwee Tou and Aljunied, Sharifah Mahani},
booktitle={Proceedings of EMNLP},
url={https://arxiv.org/abs/2205.12696},
year={2022}
}
```
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ChilleD/MultiArith | 2023-05-02T01:44:21.000Z | [
"region:us"
] | ChilleD | null | null | 2 | 213 | 2023-05-01T13:19:47 | Entry not found | 15 | [
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LDJnr/Verified-Camel | 2023-09-26T02:33:30.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"Physics",
"Biology",
"Math",
"Chemistry",
"Culture",
"Logic",
"region:us"
] | LDJnr | null | null | 10 | 213 | 2023-09-26T02:20:36 | ---
license: apache-2.0
task_categories:
- conversational
- question-answering
- text-generation
language:
- en
tags:
- Physics
- Biology
- Math
- Chemistry
- Culture
- Logic
pretty_name: Verified-Camel
size_categories:
- n<1K
---
## This is the Official Verified Camel dataset. Just over 100 verified examples, and many more coming soon!
- Comprised of over 100 highly filtered and curated examples from specific portions of CamelAI stem datasets.
- These examples are verified to be true by experts in the specific related field, with atleast a bachelors degree in the subject.
- Roughly 30-40% of the originally curated data from CamelAI was found to have atleast minor errors and/or incoherent questions(as determined by experts in said field)
## Purpose?
- This dataset is not intended to be trained on by itself(besides perhaps interesting research purposes) however, the size and quality of this dataset can work wonderfully as a supplemmentary addition to virtually any multi-turn compatible dataset. I encourage this use, all I ask is proper credits given for such!
## Quality filtering and cleaning.
- Extensive cleaning was done to make sure there is no possible instances of overt AI moralizing or related behaviour, such as "As an AI language model" and "September 2021"
- This was done for the initial curation due to the responses being originally created by GPT-4.
## Future Plans & How you can help!
This is a relatively early build amongst the grand plans for the future of what I plan to work on!
In the near future we plan on leveraging the help of even more domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from training curations of different types of datasets.
If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord! | 1,935 | [
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] |
atmallen/qm_bob_1.0e_eval | 2023-10-31T19:45:06.000Z | [
"region:us"
] | atmallen | null | null | 0 | 213 | 2023-10-27T05:42:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: summand1
dtype: int64
- name: summand2
dtype: int64
- name: character
dtype: string
- name: sum
dtype: int64
- name: sum_words
dtype: string
- name: summand1_words
dtype: string
- name: summand2_words
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: alice_label
dtype: int64
- name: bob_label
dtype: int64
- name: row_id
dtype: int64
splits:
- name: train
num_bytes: 134298152.0
num_examples: 800000
- name: validation
num_bytes: 13701211.0
num_examples: 80000
- name: test
num_bytes: 13726378.0
num_examples: 80000
download_size: 31589990
dataset_size: 161725741.0
---
# Dataset Card for "qm_bob_1.0e_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,127 | [
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ccdv/patent-classification | 2022-10-22T09:25:36.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"size_categories:10K<n<100K",
"language:en",
"long context",
"region:us"
] | ccdv | Patent Classification Dataset: a classification of Patents (9 classes).
It contains 9 unbalanced classes, 35k Patents and summaries divided into 3 splits: train (25k), val (5k) and test (5k).
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang
See: https://aclanthology.org/P19-1212.pdf
See: https://evasharma.github.io/bigpatent/ | null | 5 | 212 | 2022-03-02T23:29:22 | ---
language: en
task_categories:
- text-classification
tags:
- long context
task_ids:
- multi-class-classification
- topic-classification
size_categories: 10K<n<100K
---
**Patent Classification: a classification of Patents and abstracts (9 classes).**
This dataset is intended for long context classification (non abstract documents are longer that 512 tokens). \
Data are sampled from "BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization." by Eva Sharma, Chen Li and Lu Wang
* See: https://aclanthology.org/P19-1212.pdf
* See: https://evasharma.github.io/bigpatent/
It contains 9 unbalanced classes, 35k Patents and abstracts divided into 3 splits: train (25k), val (5k) and test (5k).
**Note that documents are uncased and space separated (by authors)**
Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script:
```
export MODEL_NAME=roberta-base
export MAX_SEQ_LENGTH=512
python run_glue.py \
--model_name_or_path $MODEL_NAME \
--dataset_name ccdv/patent-classification \
--do_train \
--do_eval \
--max_seq_length $MAX_SEQ_LENGTH \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 4 \
--learning_rate 2e-5 \
--num_train_epochs 1 \
--max_eval_samples 500 \
--output_dir tmp/patent
``` | 1,335 | [
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dali-does/clevr-math | 2022-10-31T11:28:31.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:clevr",
"language:en",
"license:cc-by-4.0",
"reasoning",
"neuro-symbolic",
"multimodal",
"arxiv:2208.05358",
"region:us"
] | dali-does | CLEVR-Math is a dataset for compositional language, visual and mathematical reasoning. CLEVR-Math poses questions about mathematical operations on visual scenes using subtraction and addition, such as "Remove all large red cylinders. How many objects are left?". There are also adversarial (e.g. "Remove all blue cubes. How many cylinders are left?") and multihop questions (e.g. "Remove all blue cubes. Remove all small purple spheres. How many objects are left?"). | @misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
} | 4 | 212 | 2022-08-06T12:09:39 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: CLEVR-Math - Compositional language, visual, and mathematical reasoning
size_categories:
#- 100K<n<1M
source_datasets: [clevr]
tags:
- reasoning
- neuro-symbolic
- multimodal
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
---
# Dataset Card for CLEVR-Math
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:*https://github.com/dali-does/clevr-math*
- **Paper:*https://arxiv.org/abs/2208.05358*
- **Leaderboard:**
- **Point of Contact:*dali@cs.umu.se*
### Dataset Summary
Dataset for compositional multimodal mathematical reasoning based on CLEVR.
#### Loading the data, preprocessing text with CLIP
```
from transformers import CLIPPreprocessor
from datasets import load_dataset, DownloadConfig
dl_config = DownloadConfig(resume_download=True,
num_proc=8,
force_download=True)
# Load 'general' instance of dataset
dataset = load_dataset('dali-does/clevr-math', download_config=dl_config)
# Load version with only multihop in test data
dataset_multihop = load_dataset('dali-does/clevr-math', 'multihop',
download_config=dl_config)
model_path = "openai/clip-vit-base-patch32"
extractor = CLIPProcessor.from_pretrained(model_path)
def transform_tokenize(e):
e['image'] = [image.convert('RGB') for image in e['image']]
return extractor(text=e['question'],
images=e['image'],
padding=True)
dataset = dataset.map(transform_tokenize,
batched=True,
num_proc=8,
padding='max_length')
dataset_subtraction = dataset.filter(lambda e:
e['template'].startswith('subtraction'), num_proc=4)
```
### Supported Tasks and Leaderboards
Leaderboard will be announced at a later date.
### Languages
The dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.
## Dataset Structure
### Data Instances
* `general` containing the default version with multihop questions in train and test
* `multihop` containing multihop questions only in test data to test generalisation of reasoning
### Data Fields
```
features = datasets.Features(
{
"template": datasets.Value("string"),
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"image": datasets.Image(),
"label": datasets.Value("int64")
}
)
```
### Data Splits
train/val/test
## Dataset Creation
Data is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.
## Considerations for Using the Data
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Adam Dahlgren Lindström - dali@cs.umu.se
### Licensing Information
Licensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).
### Citation Information
[More Information Needed]
```
@misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
```
### Contributions
Thanks to [@dali-does](https://github.com/dali-does) for adding this dataset.
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] |
Amani27/massive_translation_dataset | 2023-07-25T14:54:44.000Z | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"language:es",
"language:hi",
"language:fr",
"language:it",
"language:ar",
"language:nl",
"language:ja",
"language:pt",
"license:cc-by-4.0",
"region:us"
] | Amani27 | null | null | 3 | 212 | 2023-07-20T16:09:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: "train.csv"
- split: validation
path: "validation.csv"
- split: test
path: "test.csv"
license: cc-by-4.0
task_categories:
- translation
language:
- en
- de
- es
- hi
- fr
- it
- ar
- nl
- ja
- pt
size_categories:
- 10K<n<100K
---
# Dataset Card for Massive Dataset for Translation
### Dataset Summary
This dataset is derived from AmazonScience/MASSIVE dataset for translation task purpose.
### Supported Tasks and Leaderboards
Translation
### Languages
1. English (en_US)
2. German (de_DE)
3. Hindi (hi_IN)
4. Spanish (es_ES)
5. French (fr_FR)
6. Italian (it_IT)
7. Arabic (ar_SA)
8. Dutch (nl_NL)
9. Japanese (ja_JP)
10. Portugese (pt_PT)
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] |
hmao/vt_multiapi_v0 | 2023-10-19T16:52:49.000Z | [
"region:us"
] | hmao | null | null | 0 | 212 | 2023-10-14T04:51:56 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: fncall
sequence: string
- name: generated_question
dtype: string
splits:
- name: train
num_bytes: 25028
num_examples: 70
download_size: 12622
dataset_size: 25028
---
# Dataset Card for "vt_multiapi_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 486 | [
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derek-thomas/dataset-creator-reddit-amitheasshole | 2023-11-03T01:00:10.000Z | [
"region:us"
] | derek-thomas | null | null | 0 | 212 | 2023-10-27T16:21:23 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: content
dtype: string
- name: poster
dtype: string
- name: date_utc
dtype: timestamp[ns]
- name: flair
dtype: 'null'
- name: title
dtype: string
- name: permalink
dtype: string
- name: id
dtype: string
- name: content_length
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 1878849
num_examples: 895
download_size: 0
dataset_size: 1878849
---
# Dataset Card for "dataset-creator-reddit-amitheasshole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
--- Generated Part of README Below ---
## Dataset Overview
The goal is to have an open dataset of [r/amitheasshole](https://www.reddit.com/r/amitheasshole/) submissions. Im leveraging PRAW and the reddit API to get downloads.
There is a limit of 1000 in an API call and limited search functionality, so this is run every hour to get new submissions.
## Creation Details
THis was created by [derek-thomas/dataset-creator-reddit-amitheasshole](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit-amitheasshole)
## Update Frequency
The dataset is updated hourly with the most recent update being `2023-11-03 01:00:00 UTC+0000` where we added **152 new rows**.
## Licensing
[Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25:
> The Content created with or submitted to our Services by Users (“User Content”) is owned by Users and not by Reddit. Subject to your complete and ongoing compliance with the Data API Terms, Reddit grants you a non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content using the Data API solely as necessary to develop, deploy, distribute, and run your App to your App Users. You may not modify the User Content except to format it for such display. You will comply with any requirements or restrictions imposed on usage of User Content by their respective owners, which may include "all rights reserved" notices, Creative Commons licenses, or other terms and conditions that may be agreed upon between you and the owners. Except as expressly permitted by this section, no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or AI model, without the express permission of rightsholders in the applicable User Content
My take is that you can't use this data for *training* without getting permission.
| 2,699 | [
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hlgd | 2023-01-25T14:32:19.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"headline-grouping",
"region:us"
] | null | HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating
whether the two headlines describe the same underlying world event or not. | @inproceedings{Laban2021NewsHG,
title={News Headline Grouping as a Challenging NLU Task},
author={Philippe Laban and Lucas Bandarkar},
booktitle={NAACL 2021},
publisher = {Association for Computational Linguistics},
year={2021}
} | 2 | 210 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Headline Grouping (HLGD)
tags:
- headline-grouping
dataset_info:
features:
- name: timeline_id
dtype:
class_label:
names:
'0': 0
'1': 1
'2': 2
'3': 3
'4': 4
'5': 5
'6': 6
'7': 7
'8': 8
'9': 9
- name: headline_a
dtype: string
- name: headline_b
dtype: string
- name: date_a
dtype: string
- name: date_b
dtype: string
- name: url_a
dtype: string
- name: url_b
dtype: string
- name: label
dtype:
class_label:
names:
'0': same_event
'1': different_event
splits:
- name: train
num_bytes: 6447212
num_examples: 15492
- name: test
num_bytes: 941145
num_examples: 2495
- name: validation
num_bytes: 798302
num_examples: 2069
download_size: 1858948
dataset_size: 8186659
---
# Dataset Card for Headline Grouping (HLGD)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping)
- **Repository:** [https://github.com/tingofurro/headline_grouping](https://github.com/tingofurro/headline_grouping)
- **Paper:** [https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf](https://people.eecs.berkeley.edu/~phillab/pdfs/NAACL2021_HLG.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** phillab (at) berkeley (dot) edu
### Dataset Summary
HLGD is a binary classification dataset consisting of 20,056 labeled news headlines pairs indicating whether the two headlines describe the same underlying world event or not. The dataset comes with an existing split between `train`, `validation` and `test` (60-20-20).
### Supported Tasks and Leaderboards
The paper (NAACL2021) introducing HLGD proposes three challenges making use of various amounts of data:
- Challenge 1: Headline-only. Models must make predictions using only the text of both headlines.
- Challenge 2: Headline + Time. Models must make predictions using the headline and publication date of the two headlines.
- Challenge 3: Headline + Time + Other. Models can make predictions using the headline, publication date as well as any other relevant meta-data that can be obtained through the URL attached to the headline (full article content, authors, news source, etc.)
### Languages
Dataset is in english.
## Dataset Structure
### Data Instances
A typical dataset consists of a timeline_id, two headlines (A/B), each associated with a URL, and a date. Finally, a label indicates whether the two headlines describe the same underlying event (1) or not (0). Below is an example from the training set:
```
{'timeline_id': 4,
'headline_a': 'France fines Google nearly $57 million for first major violation of new European privacy regime',
'headline_b': "France hits Google with record EUR50mn fine over 'forced consent' data collection",
'date_a': '2019-01-21',
'date_b': '2019-01-21',
'url_a': 'https://www.chicagotribune.com/business/ct-biz-france-fines-google-privacy-20190121-story.html',
'url_b': 'https://www.rt.com/news/449369-france-hits-google-with-record-fine/',
'label': 1}
```
### Data Fields
- `timeline_id`: Represents the id of the timeline that the headline pair belongs to (values 0 to 9). The dev set is composed of timelines 0 and 5, and the test set timelines 7 and 8
- `headline_a`, `headline_b`: Raw text for the headline pair being compared
- `date_a`, `date_b`: Publication date of the respective headlines, in the `YYYY-MM-DD` format
- `url_a`, `url_b`: Original URL of the respective headlines. Can be used to retrieve additional meta-data on the headline.
- `label`: 1 if the two headlines are part of the the same headline group and describe the same underlying event, 0 otherwise.
### Data Splits
| | Train | Dev | Test |
| --------------------------- | ------- | ------ | ----- |
| Number of examples | 15,492 | 2,069 | 2,495 |
## Dataset Creation
### Curation Rationale
The task of grouping headlines from diverse news sources discussing a same underlying event is important to enable interfaces that can present the diversity of coverage of unfolding news events. Many news aggregators (such as Google or Yahoo news) present several sources for a given event, with an objective to highlight coverage diversity.
Automatic grouping of news headlines and articles remains challenging as headlines are short, heavily-stylized texts.
The HeadLine Grouping Dataset introduces the first benchmark to evaluate NLU model's ability to group headlines according to the underlying event they describe.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by collecting 10 news timelines from the NewsLens project by selecting timelines diversified in topic each contained between 80 and 300 news articles.
#### Who are the source language producers?
The source language producers are journalists or members of the newsroom of 34 news organizations listed in the paper.
### Annotations
#### Annotation process
Each timeline was annotated for group IDs by 5 independent annotators. The 5 annotations were merged into a single annotation named the global groups.
The global group IDs are then used to generate all pairs of headlines within timelines with binary labels: 1 if two headlines are part of the same global group, and 0 otherwise. A heuristic is used to remove negative examples to obtain a final dataset that has class imbalance of 1 positive example to 5 negative examples.
#### Who are the annotators?
Annotators were authors of the papers and 8 crowd-workers on the Upwork platform. The crowd-workers were native English speakers with experience either in proof-reading or data-entry.
### Personal and Sensitive Information
Annotators identity has been anonymized. Due to the public nature of news headline, it is not expected that the headlines will contain personal sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to facilitate applications that present diverse news coverage.
By simplifying the process of developing models that can group headlines that describe a common event, we hope the community can build applications that show news readers diverse sources covering similar events.
We note however that the annotations were performed in majority by crowd-workers and that even though inter-annotator agreement was high, it was not perfect. Bias of the annotators therefore remains in the dataset.
### Discussion of Biases
There are several sources of bias in the dataset:
- Annotator bias: 10 annotators participated in the creation of the dataset. Their opinions and perspectives influenced the creation of the dataset.
- Subject matter bias: HLGD consists of headlines from 10 news timelines from diverse topics (space, tech, politics, etc.). This choice has an impact on the types of positive and negative examples that appear in the dataset.
- Source selection bias: 33 English-language news sources are represented in the dataset. This selection of news sources has an effect on the content in the timeline, and the overall dataset.
- Time-range of the timelines: the timelines selected range from 2010 to 2020, which has an influence on the language and style of news headlines.
### Other Known Limitations
For the task of Headline Grouping, inter-annotator agreement is high (0.814) but not perfect. Some decisions for headline grouping are subjective and depend on interpretation of the reader.
## Additional Information
### Dataset Curators
The dataset was initially created by Philippe Laban, Lucas Bandarkar and Marti Hearst at UC Berkeley.
### Licensing Information
The licensing status of the dataset depends on the legal status of news headlines. It is commonly held that News Headlines fall under "fair-use" ([American Bar blog post](https://www.americanbar.org/groups/gpsolo/publications/gp_solo/2011/september/fair_use_news_reviews/))
The dataset only distributes headlines, a URL and a publication date. Users of the dataset can then retrieve additional information (such as the body content, author, etc.) directly by querying the URL.
### Citation Information
```
@inproceedings{Laban2021NewsHG,
title={News Headline Grouping as a Challenging NLU Task},
author={Laban, Philippe and Bandarkar, Lucas and Hearst, Marti A},
booktitle={NAACL 2021},
publisher = {Association for Computational Linguistics},
year={2021}
}
```
### Contributions
Thanks to [@tingofurro](https://github.com/<tingofurro>) for adding this dataset. | 9,978 | [
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] |
IlyaGusev/headline_cause | 2023-02-12T00:02:58.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc0-1.0",
"causal-reasoning",
"arxiv:2108.12626",
"region:us"
] | IlyaGusev | null | @misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Casualties},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 2 | 210 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
- en
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: HeadlineCause
tags:
- causal-reasoning
---
# Dataset Card for HeadlineCause
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/IlyaGusev/HeadlineCause
- **Paper:** [HeadlineCause: A Dataset of News Headlines for Detecting Causalities](https://arxiv.org/abs/2108.12626)
- **Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
### Dataset Summary
A dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations.
### Usage
Loading Russian Simple task:
```python
from datasets import load_dataset
dataset = load_dataset("IlyaGusev/headline_cause", "ru_simple")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset consists of two parts, Russian and English.
## Dataset Structure
### Data Instances
There is an URL, a title, and a timestamp for each of the two headlines in every data instance. A label is presented in three fields. 'Result' field is a textual label, 'label' field is a numeric label, and the 'agreement' field shows the majority vote agreement between annotators. Additional information includes instance ID and the presence of the link between two articles.
```
{
'left_url': 'https://www.kommersant.ru/doc/4347456',
'right_url': 'https://tass.ru/kosmos/8488527',
'left_title': 'NASA: информация об отказе сотрудничать с Россией по освоению Луны некорректна',
'right_title': 'NASA назвало некорректными сообщения о нежелании США включать РФ в соглашение по Луне',
'left_timestamp': datetime.datetime(2020, 5, 15, 19, 46, 20),
'right_timestamp': datetime.datetime(2020, 5, 15, 19, 21, 36),
'label': 0,
'result': 'not_cause',
'agreement': 1.0,
'id': 'ru_tg_101',
'has_link': True
}
```
### Data Splits
| Dataset | Split | Number of Instances |
|:---------|:---------|:---------|
| ru_simple | train | 7,641 |
| | validation | 955 |
| | test | 957 |
| en_simple | train | 4,332 |
| | validation | 542 |
| | test | 542 |
| ru_full | train | 5,713 |
| | validation | 715 |
| | test | 715 |
| en_full | train | 2,009 |
| | validation | 251 |
| | test | 252 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Every candidate pair was annotated with [Yandex Toloka](https://toloka.ai/), a crowdsourcing platform. The task was to determine a relationship between two headlines, A and B. There were seven possible options: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. An annotation guideline was in Russian for Russian news and in English for English news.
Guidelines:
* Russian: [link](https://ilyagusev.github.io/HeadlineCause/toloka/ru/instruction.html)
* English: [link](https://ilyagusev.github.io/HeadlineCause/toloka/en/instruction.html)
Ten workers annotated every pair. The total annotation budget was 870$, with the estimated hourly wage paid to participants of 45 cents. Annotation management was semi-automatic. Scripts are available in the [Github repository](https://github.com/IlyaGusev/HeadlineCause).
#### Who are the annotators?
Yandex Toloka workers were the annotators, 457 workers for the Russian part, 180 workers for the English part.
### Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. No information about annotators is included except a platform worker ID.
## Considerations for Using the Data
### Social Impact of Dataset
We do not see any direct malicious applications of our work. The data probably do not contain offensive content, as news agencies usually do not produce it, and a keyword search returned nothing. However, there are news documents in the dataset on several sensitive topics.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data was collected by Ilya Gusev.
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A] | 6,273 | [
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maritaca-ai/imdb_pt | 2023-04-01T16:15:34.000Z | [
"region:us"
] | maritaca-ai | Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\ | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
} | 2 | 210 | 2023-01-26T14:20:51 | Entry not found | 15 | [
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vlsp-2023-vllm/ai2_arc_vi | 2023-10-08T09:54:04.000Z | [
"region:us"
] | vlsp-2023-vllm | null | null | 0 | 210 | 2023-09-29T18:17:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
struct:
- name: label
sequence: string
- name: text
sequence: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 462541
num_examples: 1118
- name: validation
num_bytes: 128948
num_examples: 298
- name: test
num_bytes: 491761
num_examples: 1170
download_size: 511280
dataset_size: 1083250
---
Reference: https://huggingface.co/datasets/ai2_arc
# ARC-Challenge (Vietnamese translation version)
## Dataset Summary
A dataset of grade-school level, multiple-choice science questions, assembled to encourage research in advanced question-answering.
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `ai2_arc_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks ai2_arc_vi \
--num_fewshot 25 \
--batch_size auto \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks ai2_arc_vi \
--num_fewshot 25 \
--batch_size auto \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring. | 2,638 | [
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sem_eval_2014_task_1 | 2023-01-25T14:43:53.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | The SemEval-2014 Task 1 focuses on Evaluation of Compositional Distributional Semantic Models
on Full Sentences through Semantic Relatedness and Entailment. The task was designed to
predict the degree of relatedness between two sentences and to detect the entailment
relation holding between them. | @inproceedings{inproceedings,
author = {Marelli, Marco and Bentivogli, Luisa and Baroni, Marco and Bernardi, Raffaella and Menini, Stefano and Zamparelli, Roberto},
year = {2014},
month = {08},
pages = {},
title = {SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment},
doi = {10.3115/v1/S14-2001}
} | 1 | 209 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pretty_name: SemEval 2014 - Task 1
dataset_info:
features:
- name: sentence_pair_id
dtype: int64
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: relatedness_score
dtype: float32
- name: entailment_judgment
dtype:
class_label:
names:
'0': NEUTRAL
'1': ENTAILMENT
'2': CONTRADICTION
splits:
- name: train
num_bytes: 540296
num_examples: 4500
- name: test
num_bytes: 592320
num_examples: 4927
- name: validation
num_bytes: 60981
num_examples: 500
download_size: 197230
dataset_size: 1193597
---
# Dataset Card for SemEval 2014 - Task 1
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [SemEval-2014 Task 1](https://alt.qcri.org/semeval2014/task1/)
- **Repository:**
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/S14-2001/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@ashmeet13](https://github.com/ashmeet13) for adding this dataset. | 3,564 | [
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alistvt/coqa-stories | 2022-01-20T22:17:46.000Z | [
"region:us"
] | alistvt | null | null | 1 | 209 | 2022-03-02T23:29:22 | This is a dataset containing just stories of the CoQA dataset with their respective ids. This can be used in the pretraining phase for the MLM tasks. | 149 | [
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Harsit/xnli2.0_train_swahili | 2022-10-15T09:22:30.000Z | [
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Francesco/abdomen-mri | 2023-03-30T09:41:54.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | 0 | 209 | 2023-03-30T09:41:19 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': abdomen-MRI
'1': 0
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: abdomen-mri
tags:
- rf100
---
# Dataset Card for abdomen-mri
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/abdomen-mri
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
abdomen-mri
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/abdomen-mri
### Citation Information
```
@misc{ abdomen-mri,
title = { abdomen mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/abdomen-mri } },
url = { https://universe.roboflow.com/object-detection/abdomen-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | 3,324 | [
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] |
distil-whisper/tedlium-long-form | 2023-05-22T14:04:04.000Z | [
"region:us"
] | distil-whisper | null | null | 0 | 209 | 2023-05-22T13:19:52 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: string
splits:
- name: validation
num_bytes: 180166870.0
num_examples: 8
- name: test
num_bytes: 285107770.0
num_examples: 11
download_size: 284926490
dataset_size: 465274640.0
---
# Dataset Card for "tedlium-long-form"
To create the dataset:
```python
import os
import numpy as np
from datasets import load_dataset, DatasetDict, Dataset, Audio
import soundfile as sf
from tqdm import tqdm
tedlium = load_dataset("LIUM/tedlium", "release3")
merged_dataset = DatasetDict()
validation_speaker_ids = [
"Al_Gore",
"Barry_Schwartz",
"Blaise_Agueray_Arcas",
"Brian_Cox",
"Craig_Venter",
"David_Merrill",
"Elizabeth_Gilbert",
"Wade_Davis",
]
validation_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in validation_speaker_ids}
test_speaker_ids = [
"AimeeMullins",
"BillGates",
"DanBarber",
"DanBarber_2010_S103",
"DanielKahneman",
"EricMead_2009P_EricMead",
"GaryFlake",
"JamesCameron",
"JaneMcGonigal",
"MichaelSpecter",
"RobertGupta",
]
test_dataset_merged = {speaker_id: {"audio": [], "text": ""} for speaker_id in test_speaker_ids}
for split, dataset in zip(["validation", "test"], [validation_dataset_merged, test_dataset_merged]):
sampling_rate = tedlium[split].features["audio"].sampling_rate
for sample in tqdm(tedlium[split]):
if sample["speaker_id"] in dataset:
dataset[sample["speaker_id"]]["audio"].extend(sample["audio"]["array"])
dataset[sample["speaker_id"]]["text"] += " " + sample["text"]
audio_paths = []
os.makedirs(split, exist_ok=True)
for speaker in dataset:
path = os.path.join(split, f"{speaker}-merged.wav")
audio_paths.append(path)
sf.write(path, np.asarray(dataset[speaker]["audio"]), samplerate=sampling_rate)
merged_dataset[split] = Dataset.from_dict({"audio": audio_paths}).cast_column("audio", Audio())
# remove spaced apostrophes (e.g. it 's -> it's)
merged_dataset[split] = merged_dataset[split].add_column("text", [dataset[speaker]["text"].replace(" '", "'") for speaker in dataset])
merged_dataset[split] = merged_dataset[split].add_column("speaker_id", dataset.keys())
``` | 2,356 | [
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rguo123/trump_tweets | 2023-08-07T14:11:46.000Z | [
"region:us"
] | rguo123 | null | null | 0 | 209 | 2023-07-10T19:55:56 | Entry not found | 15 | [
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result-kand2-sdxl-wuerst-karlo/25b005b7 | 2023-10-08T22:30:24.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 209 | 2023-10-08T22:30:23 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1383
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "25b005b7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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albertvillanova/pmc_open_access | 2023-01-16T13:43:54.000Z | [
"region:us"
] | albertvillanova | The PMC Open Access Subset includes more than 3.4 million journal articles and preprints that are made available under
license terms that allow reuse.
Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more
liberal redistribution and reuse than a traditional copyrighted work.
The PMC Open Access Subset is one part of the PMC Article Datasets | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | 0 | 208 | 2022-03-02T23:29:22 | # Dataset Card for pmc_open_access
## Dataset Description
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> Dataset "pmc_open_access" is deprecated and will be deleted. Use "<a href="https://huggingface.co/datasets/pmc/open_access">pmc/open_access</a>" instead.</p>
</div>
| 527 | [
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cassandra-themis/QR-AN | 2022-10-24T20:31:22.000Z | [
"task_categories:summarization",
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"size_categories:10K<n<100K",
"language:fr",
"conditional-text-generation",
"region:us"
] | cassandra-themis | QR-AN Dataset: a classification dataset on french Parliament debates
This is a dataset for theme/topic classification, made of questions and answers from https://www2.assemblee-nationale.fr/recherche/resultats_questions.
It contains 188 unbalanced classes, 80k questions-answers divided into 3 splits: train (60k), val (10k) and test (10k). | null | 2 | 208 | 2022-03-02T23:29:22 | ---
language:
- fr
size_categories: 10K<n<100K
task_categories:
- summarization
- text-classification
- text-generation
task_ids:
- multi-class-classification
- topic-classification
tags:
- conditional-text-generation
---
**QR-AN Dataset: a classification and generation dataset of french Parliament questions-answers.**
This is a dataset for theme/topic classification, made of questions and answers from https://www2.assemblee-nationale.fr/recherche/resultats_questions . \
It contains 188 unbalanced classes, 80k questions-answers divided into 3 splits: train (60k), val (10k) and test (10k). \
Can be used for generation with 'qran_generation'
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/cass-summarization": ("question", "answer")
```
Compatible with [run_glue.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-classification) script:
```
export MODEL_NAME=camembert-base
export MAX_SEQ_LENGTH=512
python run_glue.py \
--model_name_or_path $MODEL_NAME \
--dataset_name cassandra-themis/QR-AN \
--do_train \
--do_eval \
--max_seq_length $MAX_SEQ_LENGTH \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 4 \
--learning_rate 2e-5 \
--num_train_epochs 1 \
--max_eval_samples 500 \
--output_dir tmp/QR-AN
``` | 1,499 | [
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dynabench/qa | 2022-07-02T20:17:58.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2002.00293",
"arxiv:1606.05250",
"region:us"
] | dynabench | Dynabench.QA is a Reading Comprehension dataset collected using a human-and-model-in-the-loop. | null | 0 | 208 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
---
# Dataset Card for Dynabench.QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Dynabench.QA](https://dynabench.org/tasks/2#overall)
- **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293)
- **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall)
- **Point of Contact:** [Max Bartolo](max.bartolo@ucl.ac.uk)
### Dataset Summary
Dynabench.QA is an adversarially collected Reading Comprehension dataset spanning over multiple rounds of data collect.
For round 1, it is identical to the [adversarialQA dataset](https://adversarialqa.github.io/), where we have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERT-Large (Devlin et al., 2018), and RoBERTa-Large (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods.
### Supported Tasks and Leaderboards
`extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Data is provided in the same format as SQuAD 1.1. An example is shown below:
```
{
"data": [
{
"title": "Oxygen",
"paragraphs": [
{
"context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.",
"qas": [
{
"id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3",
"question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?",
"answers": [
{
"answer_start": 36,
"text": "organic compounds"
}
]
},
{
"id": "4240a8e708c703796347a3702cf1463eed05584a",
"question": "What letter does the abbreviation for acid anhydrides both begin and end in?",
"answers": [
{
"answer_start": 244,
"text": "R"
}
]
},
{
"id": "0681a0a5ec852ec6920d6a30f7ef65dced493366",
"question": "Which of the organic compounds, in the article, contains nitrogen?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
},
{
"id": "2990efe1a56ccf81938fa5e18104f7d3803069fb",
"question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
}
]
}
]
}
]
}
```
### Data Fields
- title: the title of the Wikipedia page from which the context is sourced
- context: the context/passage
- id: a string identifier for each question
- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text
### Data Splits
For round 1, the dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples.
## Dataset Creation
### Curation Rationale
This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models.
### Source Data
#### Initial Data Collection and Normalization
The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250).
#### Who are the source language producers?
The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions.
### Annotations
#### Annotation process
The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model.
#### Who are the annotators?
The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation.
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question.
It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application.
### Discussion of Biases
The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol.
### Other Known Limitations
N/a
## Additional Information
### Dataset Curators
This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL).
### Licensing Information
This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
```
@article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {662-678},
year = {2020},
doi = {10.1162/tacl\_a\_00338},
URL = { https://doi.org/10.1162/tacl_a_00338 },
eprint = { https://doi.org/10.1162/tacl_a_00338 },
abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). }
}
```
### Contributions
Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset. | 11,596 | [
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