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ChangeIsKey/kubhist2 | 2023-08-10T09:20:43.000Z | [
"task_categories:text-generation",
"size_categories:1B<n<10B",
"language:sv",
"license:cc-by-sa-4.0",
"newspapers",
"historical",
"region:us"
] | ChangeIsKey | This is a version of the Kubhist 2 dataset created, curated and made available by Språkbanken Text (SBX) at the University of Gothenburg (Sweden) under the CC BY 4.0 license.
This is a a corpus of OCRed newspapers from Sweden spanning the 1640s to the 1900s.
The original data is available with many types of annotation in XML at https://spraakbanken.gu.se/en/resources/kubhist2.
A good description of the data is available in this blog entry by Dana Dannélls: https://spraakbanken.gu.se/blogg/index.php/2019/09/15/the-kubhist-corpus-of-swedish-newspapers/
In a nutshell, this hugginface dataset version offers:
- only the OCRed text
- available in decadal subsets
License is CC BY 4.0 with attribution. | @misc{botenanna,
title = {"Jag känner en bot, hon heter [MASK]. A BERT for older Swedish, and a more usable dataset for historical newspapers"},
author = {Simon Hengchen}
year={2023},
} | null | 0 | 75 | ---
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license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- sv
tags:
- newspapers
- historical
size_categories:
- 1B<n<10B
---
# kubhist2
## Dataset Description
- **Homepage: https://changeiskey.org**
- **Repository: https://github.com/ChangeIsKey/kubhist2**
- **Point of Contact: Simon Hengchen / iguanodon.ai**
### Dataset Summary
This is a version of the Kubhist 2 dataset originally created, curated and made available by Språkbanken Text (SBX) at the University of Gothenburg (Sweden) under the CC BY 4.0 license.
This is a corpus of OCRed newspapers from Sweden spanning the 1640s to the 1900s.
The original data is available with many types of annotation in XML at https://spraakbanken.gu.se/en/resources/kubhist2.
A good description of the original data is available in this blog entry by Dana Dannélls: https://spraakbanken.gu.se/blogg/index.php/2019/09/15/the-kubhist-corpus-of-swedish-newspapers/.
If you use this dataset for academic research, cite it using the provided citation information at the bottom of this page.
In a nutshell, this huggingface dataset version offers:
- only the OCRed text
- available in decadal subsets
- one line per sentence, sentences shorter than 4 words were discarded
In total this dataset contains 2,819,065,590 tokens. A distribution of tokens per decade is available below.
License is CC BY 4.0 ShareAlike.
```bash
(env) simon@terminus:/mnt/user/cik/kubhist2 wc -w text/*/*.txt
39348 text/1640/1640.txt
4700 text/1650/1650.txt
8524 text/1660/1660.txt
2396 text/1670/1670.txt
199670 text/1680/1680.txt
487943 text/1690/1690.txt
619884 text/1700/1700.txt
265930 text/1710/1710.txt
355759 text/1720/1720.txt
856218 text/1730/1730.txt
1589508 text/1740/1740.txt
2211316 text/1750/1750.txt
5496545 text/1760/1760.txt
14434932 text/1770/1770.txt
22366170 text/1780/1780.txt
26768856 text/1790/1790.txt
36225842 text/1800/1800.txt
44510588 text/1810/1810.txt
65571094 text/1820/1820.txt
95359730 text/1830/1830.txt
143992956 text/1840/1840.txt
214538699 text/1850/1850.txt
392672066 text/1860/1860.txt
524802728 text/1870/1870.txt
695859650 text/1880/1880.txt
498244203 text/1890/1890.txt
31580335 text/1900/1900.txt
2819065590 total
```
### Languages
Swedish (nysvenska)
## Dataset Structure
One feature: `text`.
Load the whole corpus using
```python
dataset = load_dataset("ChangeIsKey/kubhist2")
```
or a decadal subset using
```python
dataset = load_dataset("ChangeIsKey/kubhist2", "decade")
```
The `decade` must be a string, valid values are within `range(1640, 1910, 10)`.
You can combine several decades using `concatenate_datasets` like this:
```python
from datasets import load_dataset, concatenate_datasets
ds_1800 = load_dataset("ChangeIsKey/kubhist2", "1800")
ds_1810 = load_dataset("ChangeIsKey/kubhist2", "1810")
ds_1820 = load_dataset("ChangeIsKey/kubhist2", "1820")
ds_1800_1820 = concatenate_datasets([
ds_1800["train"],
ds_1810["train"],
ds_1820["train"]
])
```
Despite what the huggingface dataset viewer states the `all` config has 285.4M (285,384,149 to be precise) rows, not 77.9M.
### Data Splits
The dataset has only one split, `train`.
## Dataset Creation
### Curation Rationale
The original data is in a highly-annotated XML format not ideally suited for basic NLP tasks such as unsupervised language modeling: information such as page numbers, fonts, etc. is less relevant and has thus been discarded.
Keeping only the running text of the newspaper and removing sentences shorter than 4 words further allows a 150x data size reduction (2.4TB --> 16GB).
### Source Data
The original data is available with many types of annotation in XML at https://spraakbanken.gu.se/en/resources/kubhist2.
#### Initial Data Collection and Normalization
See on Språkbanken Text's website.
#### Who are the source language producers?
Språkbanken Text: https://spraakbanken.gu.se/en/
### Personal and Sensitive Information
This is historical newspaper data, with the latest data published in 1909. Everyone mentioned in this dataset was probably already a public figure, and has been dead for a while.
## Considerations for Using the Data
### Discussion of Biases
This is historical data. As such, outdated views might be present in the data.
### Other Known Limitations
The data comes from an OCR process. The text is thus not perfect, especially so in the earlier decades.
## Additional Information
### Dataset Curators
This huggingface version of the data has been created by Simon Hengchen.
### Licensing Information
Creative Commons Attribution Share Alike 4.0: https://creativecommons.org/licenses/by-sa/4.0/
### Citation Information
You should always cite the original kubhist2 release, provided below as bibtex. If you want to additionally refer to this specific version, please also add a link to the huggingface page: https://huggingface.co/datasets/ChangeIsKey/kubhist2.
```bibtex
@misc{Kubhist2,
title = {The Kubhist Corpus, v2},
url = {https://spraakbanken.gu.se/korp/?mode=kubhist},
author = {Spr{\aa}kbanken},
year = {Downloaded in 2019},
organization = {Department of Swedish, University of Gothenburg}
}
```
### Acknowledgments
This dataset has been created in the context of the [ChangeIsKey!](https://www.changeiskey.org/) project funded by Riksbankens Jubileumsfond under reference number M21-0021, Change is Key! program.
The compute dedicated to the creation of the dataset has been provided by [iguanodon.ai](https://iguanodon.ai).
Many thanks got to Språkbanken Text for creating and curating this resource. |
lonestar108/enlightenedllm | 2023-10-01T19:49:16.000Z | [
"task_categories:text-generation",
"language:en",
"license:openrail",
"religion",
"spirituality",
"region:us"
] | lonestar108 | null | null | null | 0 | 75 | ---
language:
- en
license: openrail
task_categories:
- text-generation
pretty_name: Enlightened LLM
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 458879
num_examples: 2070
- name: test
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num_examples: 574
- name: validation
num_bytes: 50830
num_examples: 229
download_size: 337213
dataset_size: 633163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
tags:
- religion
- spirituality
---
# Dataset Card for "enlightenedllm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lyogavin/longer_training_max100k_v3 | 2023-09-09T04:31:13.000Z | [
"region:us"
] | lyogavin | null | null | null | 2 | 75 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: source
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3294652388.329473
num_examples: 18964
download_size: 476508613
dataset_size: 3294652388.329473
---
# Dataset Card for "longer_training_max100k_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BAAI/COIG-PC-core | 2023-09-25T10:33:33.000Z | [
"language:zh",
"license:unknown",
"region:us"
] | BAAI | null | null | null | 5 | 75 | ---
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: |
北京智源人工智能研究院（以下简称“我们”或“研究院”）通过BAAI DataHub（data.baai.ac.cn）和COIG-PC HuggingFace仓库（https://huggingface.co/datasets/BAAI/COIG-PC）向您提供开源数据集（以下或称“数据集”），您可通过下载的方式获取您所需的开源数据集，并在遵守各原始数据集使用规则前提下，基于学习、研究、商业等目的使用相关数据集。
在您获取（包括但不限于访问、下载、复制、传播、使用等处理数据集的行为）开源数据集前，您应认真阅读并理解本《COIG-PC开源数据集使用须知与免责声明》（以下简称“本声明”）。一旦您获取开源数据集，无论您的获取方式为何，您的获取行为均将被视为对本声明全部内容的认可。
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如您在使用开源数据集的过程中发现开源数据集涉及或者可能涉及任何违法与不良信息，您应立即停止对数据集中涉及违法与不良信息部分的使用，并及时通过“6. 投诉与通知”中载明的联系我们。
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extra_gated_fields:
Name: text
Affiliation: text
Country: text
I agree to use this model for non-commercial use ONLY: checkbox
extra_gated_button_content: "Acknowledge license"
license: unknown
language:
- zh
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
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splits:
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num_bytes: 1053129000
num_examples: 744592
download_size: 416315627
dataset_size: 1053129000
---
# COIG Prompt Collection
## License
**Default Licensing for Sub-Datasets Without Specific License Declaration**: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declaration, the Apache License 2.0 (Apache-2.0) will be the applicable licensing terms by default.
**Precedence of Declared Licensing for Sub-Datasets**: For any sub-dataset within the COIG-PC Dataset that has an explicitly declared license, the terms and conditions of the declared license shall take precedence and govern the usage of that particular sub-dataset.
Users and developers utilizing the COIG-PC Dataset must ensure compliance with the licensing terms as outlined above. It is imperative to review and adhere to the specified licensing conditions of each sub-dataset, as they may vary.
## What is COIG-PC?
The COIG-PC Dataset is a meticulously curated and comprehensive collection of Chinese tasks and data, designed to facilitate the fine-tuning and optimization of language models for Chinese natural language processing (NLP). The dataset aims to provide researchers and developers with a rich set of resources to improve the capabilities of language models in handling Chinese text, which can be utilized in various fields such as text generation, information extraction, sentiment analysis, machine translation, among others.
If you think COIG-PC is too huge, please refer to [COIG-PC-Lite](https://huggingface.co/datasets/BAAI/COIG-PC-Lite) which is a subset of COIG-PC with only 200 samples from each task file.
## Why COIG-PC?
The COIG-PC Dataset is an invaluable resource for the domain of natural language processing (NLP) for various compelling reasons:
**Addressing Language Complexity**: Chinese is known for its intricacy, with a vast array of characters and diverse grammatical structures. A specialized dataset like COIG-PC, which is tailored for the Chinese language, is essential to adequately address these complexities during model training.
**Comprehensive Data Aggregation**: The COIG-PC Dataset is a result of an extensive effort in integrating almost all available Chinese datasets in the market. This comprehensive aggregation makes it one of the most exhaustive collections for Chinese NLP.
**Data Deduplication and Normalization**: The COIG-PC Dataset underwent rigorous manual processing to eliminate duplicate data and perform normalization. This ensures that the dataset is free from redundancy, and the data is consistent and well-structured, making it more user-friendly and efficient for model training.
**Fine-tuning and Optimization**: The dataset’s instruction-based phrasing facilitates better fine-tuning and optimization of language models. This structure allows models to better understand and execute tasks, which is particularly beneficial in improving performance on unseen or novel tasks.
The COIG-PC Dataset, with its comprehensive aggregation, meticulous selection, deduplication, and normalization of data, stands as an unmatched resource for training and optimizing language models tailored for the Chinese language and culture. It addresses the unique challenges of Chinese language processing and serves as a catalyst for advancements in Chinese NLP.
## Who builds COIG-PC?
The bedrock of COIG-PC is anchored in the dataset furnished by stardust.ai, which comprises an aggregation of data collected from the Internet.
And COIG-PC is the result of a collaborative effort involving engineers and experts from over twenty distinguished universities both domestically and internationally. Due to space constraints, it is not feasible to list all of them; however, the following are a few notable institutions among the collaborators:
- Beijing Academy of Artificial Intelligence, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/baai.png" alt= “BAAI” height="100" width="150">
- Peking University, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/pku.png" alt= “PKU” height="100" width="200">
- The Hong Kong University of Science and Technology (HKUST), China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/hkust.png" alt= “HKUST” height="100" width="200">
- The University of Waterloo, Canada
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/waterloo.png" alt= “Waterloo” height="100" width="150">
- The University of Sheffield, United Kingdom
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/sheffield.png" alt= “Sheffield” height="100" width="200">
- Beijing University of Posts and Telecommunications, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/bupt.png" alt= “BUPT” height="100" width="200">
- [Multimodal Art Projection](https://huggingface.co/m-a-p)
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/map.png" alt= “M.A.P” height="100" width="200">
- stardust.ai, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/stardust.png" alt= “stardust.ai” height="100" width="200">
- LinkSoul.AI, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/linksoul.png" alt= “linksoul.ai” height="100" width="200">
For the detailed list of engineers involved in the creation and refinement of COIG-PC, please refer to the paper that will be published subsequently. This paper will provide in-depth information regarding the contributions and the specifics of the dataset’s development process.
## How to use COIG-PC?
COIG-PC is structured in a **.jsonl** file format. Each line in the file represents a single data record and is structured in JSON (JavaScript Object Notation) format. Below is a breakdown of the elements within each line:
**instruction**: This is a text string that provides the instruction for the task. For example, it might tell the model what to do with the input data.
**input**: This is the input data that the model needs to process. In the context of translation, it would be the text that needs to be translated.
**output**: This contains the expected output data after processing the input. In the context of translation, it would be the translated text.
**split**: Indicates the official split of the original dataset, which is used to categorize data for different phases of model training and evaluation. It can be 'train', 'test', 'valid', etc.
**task_type**: Contains major and minor categories for the dataset. Major categories are broader, while minor categories can be more specific subcategories.
**domain**: Indicates the domain or field to which the data belongs.
**other**: This field can contain additional information or metadata regarding the data record. If there is no additional information, it may be set to null.
### Example
Here is an example of how a line in the COIG-PC dataset might be structured:
```
{
"instruction": "请把下面的中文句子翻译成英文",
"input": "我爱你。",
"output": "I love you.",
"split": "train",
"task_type": {
"major": ["翻译"],
"minor": ["翻译", "中译英"]
},
"domain": ["通用"],
"other": null
}
```
In this example:
**instruction** tells the model to translate the following Chinese sentence into English.
**input** contains the Chinese text "我爱你" which means "I love you".
**output** contains the expected translation in English: "I love you".
**split** indicates that this data record is part of the training set.
**task_type** specifies that the major category is "Translation" and the minor categories are "Translation" and "Chinese to English".
**domain** specifies that this data record belongs to the general domain.
**other** is set to null as there is no additional information for this data record.
## Update: Aug. 30, 2023
- v1.0: First version of COIG-PC-core.
## COIG-PC Citation
If you want to cite COIG-PC-core dataset, you could use this:
```
```
## Contact Us
To contact us feel free to create an Issue in this repository.
|
renumics/speech_commands_enrichment_only | 2023-09-28T12:25:09.000Z | [
"task_categories:audio-classification",
"task_ids:keyword-spotting",
"annotations_creators:other",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:extended|speech_commands",
"language:en",
"license:cc-by-4... | renumics | null | null | null | 0 | 75 | ---
annotations_creators:
- other
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|speech_commands
task_categories:
- audio-classification
task_ids:
- keyword-spotting
pretty_name: SpeechCommands
config_names:
- v0.01
- v0.02
tags:
- spotlight
- enriched
- renumics
- enhanced
- audio
- classification
- extended
dataset_info:
- config_name: enrichment_only
features:
- name: label_string
dtype: string
- name: probability
dtype: float64
- name: probability_vector
sequence: float32
- name: prediction
dtype: int64
- name: prediction_string
dtype: string
- name: embedding_reduced
sequence: float32
splits:
- name: train
num_bytes: 8763867
num_examples: 51093
- name: validation
num_bytes: 1165942
num_examples: 6799
- name: test
num_bytes: 528408
num_examples: 3081
download_size: 0
dataset_size: 10458217
- config_name: raw_and_enrichment_combined
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype:
class_label:
names:
'0': 'yes'
'1': 'no'
'2': up
'3': down
'4': left
'5': right
'6': 'on'
'7': 'off'
'8': stop
'9': go
'10': zero
'11': one
'12': two
'13': three
'14': four
'15': five
'16': six
'17': seven
'18': eight
'19': nine
'20': bed
'21': bird
'22': cat
'23': dog
'24': happy
'25': house
'26': marvin
'27': sheila
'28': tree
'29': wow
'30': _silence_
- name: is_unknown
dtype: bool
- name: speaker_id
dtype: string
- name: utterance_id
dtype: int8
- name: logits
sequence: float64
- name: embedding
sequence: float32
- name: label_string
dtype: string
- name: probability
dtype: float64
- name: probability_vector
sequence: float32
- name: prediction
dtype: int64
- name: prediction_string
dtype: string
- name: embedding_reduced
sequence: float32
splits:
- name: train
num_bytes: 1803565876.375
num_examples: 51093
- name: validation
num_bytes: 240795605.125
num_examples: 6799
- name: test
num_bytes: 109673146.875
num_examples: 3081
download_size: 0
dataset_size: 2154034628.375
configs:
- config_name: enrichment_only
data_files:
- split: train
path: enrichment_only/train-*
- split: validation
path: enrichment_only/validation-*
- split: test
path: enrichment_only/test-*
- config_name: raw_and_enrichment_combined
data_files:
- split: train
path: raw_and_enrichment_combined/train-*
- split: validation
path: raw_and_enrichment_combined/validation-*
- split: test
path: raw_and_enrichment_combined/test-*
---
# Dataset Card for SpeechCommands
## Dataset Description
- **Homepage:** [Renumics Homepage](https://renumics.com/?hf-dataset-card=speech-commands-enrichment_only)
- **GitHub** [Spotlight](https://github.com/Renumics/spotlight)
- **Dataset Homepage** [tensorflow.org/datasets](https://www.tensorflow.org/datasets/catalog/speech_commands)
- **Paper:** [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)
- **Leaderboard:** [More Information Needed]
### Dataset Summary
📊 [Data-centric AI](https://datacentricai.org) principles have become increasingly important for real-world use cases.
At [Renumics](https://renumics.com/?hf-dataset-card=speech-commands-enriched) we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
1. Enable new researchers to quickly develop a profound understanding of the dataset.
2. Popularize data-centric AI principles and tooling in the ML community.
3. Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
📚 This dataset is an enriched version of the [SpeechCommands Dataset](https://huggingface.co/datasets/speech_commands).
### Explore the Dataset
There are two configurations of the dataset: **Enrichment only** provides the enrichments calculated by Renumics using the MIT AST transformer, while **raw_and_enrichment_combined** provides a concatenated dataset of the original speech commands and the enrichment.
The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) enables that with just a few lines of code:
Install datasets and Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip):
```python
!pip install renumics-spotlight datasets[audio]
```
> **_Notice:_** On Linux, non-Python dependency on libsndfile package must be installed manually. See [Datasets - Installation](https://huggingface.co/docs/datasets/installation#audio) for more information.
Load the dataset from huggingface in your notebook and start exploring with a simple view:
```python
import datasets
from renumics import spotlight
from renumics.spotlight.layouts import debug_classification
dataset = datasets.load_dataset("renumics/speech_commands_enrichment_only", "raw_and_enrichment_combined")
joined_dataset = datasets.concatenate_datasets([dataset["train"], dataset["validation"], dataset["test"]])
layout = debug_classification(label='label_string', prediction='prediction', embedding='embedding_reduced',
features=["label", "prediction", "probability"], inspect={'audio': spotlight.Audio})
dtypes = {
"audio": spotlight.Audio,
"embedding_reduced": spotlight.Embedding
}
spotlight.show(
joined_dataset,
dtype=dtypes,
layout= layout
)
```
You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
As a plug and play option, you can check out the Huggingface space: [Huggingface Space for speech enrichment](https://huggingface.co/spaces/renumics/speech_commands_enrichment_space)
Alternatively, you can run the notebook exploration.ipynb locally.
### SpeechCommands Dataset
This is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. It is covered in more detail at [https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
64,727 audio files.
Version 0.02 of the data set (configuration `"v0.02"`) was released on April 11th 2018 and
contains 105,829 audio files.
### Supported Tasks and Leaderboards
* `keyword-spotting`: the dataset can be used to train and evaluate keyword
spotting systems. The task is to detect preregistered keywords by classifying utterances
into a predefined set of words. The task is usually performed on-device for the
fast response time. Thus, accuracy, model size, and inference time are all crucial.
### Languages
The language data in SpeechCommands is in English (BCP-47 `en`).
## Dataset Structure
### Data Instances
Example of a core word (`"label"` is a word, `"is_unknown"` is `False`):
```python
{
"file": "no/7846fd85_nohash_0.wav",
"audio": {
"path": "no/7846fd85_nohash_0.wav",
"array": array([ -0.00021362, -0.00027466, -0.00036621, ..., 0.00079346,
0.00091553, 0.00079346]),
"sampling_rate": 16000
},
"label": 1, # "no"
"is_unknown": False,
"speaker_id": "7846fd85",
"utterance_id": 0
}
```
Example of an auxiliary word (`"label"` is a word, `"is_unknown"` is `True`)
```python
{
"file": "tree/8b775397_nohash_0.wav",
"audio": {
"path": "tree/8b775397_nohash_0.wav",
"array": array([ -0.00854492, -0.01339722, -0.02026367, ..., 0.00274658,
0.00335693, 0.0005188]),
"sampling_rate": 16000
},
"label": 28, # "tree"
"is_unknown": True,
"speaker_id": "1b88bf70",
"utterance_id": 0
}
```
Example of background noise (`_silence_`) class:
```python
{
"file": "_silence_/doing_the_dishes.wav",
"audio": {
"path": "_silence_/doing_the_dishes.wav",
"array": array([ 0. , 0. , 0. , ..., -0.00592041,
-0.00405884, -0.00253296]),
"sampling_rate": 16000
},
"label": 30, # "_silence_"
"is_unknown": False,
"speaker_id": "None",
"utterance_id": 0 # doesn't make sense here
}
```
### Data Fields
* `file`: relative audio filename inside the original archive.
* `audio`: dictionary containing a relative audio filename,
a decoded audio array, and the sampling rate. Note that when accessing
the audio column: `dataset[0]["audio"]` the audio is automatically decoded
and resampled to `dataset.features["audio"].sampling_rate`.
Decoding and resampling of a large number of audios 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]`.
* `label`: either word pronounced in an audio sample or background noise (`_silence_`) class.
Note that it's an integer value corresponding to the class name.
* `is_unknown`: if a word is auxiliary. Equals to `False` if a word is a core word or `_silence_`,
`True` if a word is an auxiliary word.
* `speaker_id`: unique id of a speaker. Equals to `None` if label is `_silence_`.
* `utterance_id`: incremental id of a word utterance within the same speaker.
### Data Splits
The dataset has two versions (= configurations): `"v0.01"` and `"v0.02"`. `"v0.02"`
contains more words (see section [Source Data](#source-data) for more details).
| | train | validation | test |
|----- |------:|-----------:|-----:|
| v0.01 | 51093 | 6799 | 3081 |
| v0.02 | 84848 | 9982 | 4890 |
Note that in train and validation sets examples of `_silence_` class are longer than 1 second.
You can use the following code to sample 1-second examples from the longer ones:
```python
def sample_noise(example):
# Use this function to extract random 1 sec slices of each _silence_ utterance,
# e.g. inside `torch.utils.data.Dataset.__getitem__()`
from random import randint
if example["label"] == "_silence_":
random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]
return example
```
## Dataset Creation
### Curation Rationale
The primary goal of the dataset is to provide a way to build and test small
models that can detect a single word from a set of target words and differentiate it
from background noise or unrelated speech with as few false positives as possible.
### Source Data
#### Initial Data Collection and Normalization
The audio files were collected using crowdsourcing, see
[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
for some of the open source audio collection code that was used. The goal was to gather examples of
people speaking single-word commands, rather than conversational sentences, so
they were prompted for individual words over the course of a five minute
session.
In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
from unrecognized ones.
The `_silence_` label contains a set of longer audio clips that are either recordings or
a mathematical simulation of noise.
#### Who are the source language producers?
The audio files were collected using crowdsourcing.
### Annotations
#### Annotation process
Labels are the list of words prepared in advances.
Speakers were prompted for individual words over the course of a five minute
session.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. 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
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons BY 4.0 License ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/legalcode]).
### Citation Information
```
@article{speechcommandsv2,
author = { {Warden}, P.},
title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.03209},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
year = 2018,
month = apr,
url = {https://arxiv.org/abs/1804.03209},
}
```
### Contributions
[More Information Needed] |
mixamrepijey/gorilla-hf | 2023-10-03T11:11:44.000Z | [
"license:apache-2.0",
"region:us"
] | mixamrepijey | null | null | null | 0 | 75 | ---
license: apache-2.0
---
|
generics_kb | 2023-06-07T12:35:34.000Z | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base",
"arxiv:2005.00660",
"region:u... | null | The GenericsKB contains 3.4M+ generic sentences about the world, i.e., sentences expressing general truths such as "Dogs bark," and "Trees remove carbon dioxide from the atmosphere." Generics are potentially useful as a knowledge source for AI systems requiring general world knowledge. The GenericsKB is the first large-scale resource containing naturally occurring generic sentences (as opposed to extracted or crowdsourced triples), and is rich in high-quality, general, semantically complete statements. Generics were primarily extracted from three large text sources, namely the Waterloo Corpus, selected parts of Simple Wikipedia, and the ARC Corpus. A filtered, high-quality subset is also available in GenericsKB-Best, containing 1,020,868 sentences. We recommend you start with GenericsKB-Best. | @InProceedings{huggingface:dataset,
title = {GenericsKB: A Knowledge Base of Generic Statements},
authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
year={2020},
publisher = {Allen Institute for AI},
} | null | 0 | 74 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: genericskb
pretty_name: GenericsKB
tags:
- knowledge-base
dataset_info:
- config_name: generics_kb_best
features:
- name: source
dtype: string
- name: term
dtype: string
- name: quantifier_frequency
dtype: string
- name: quantifier_number
dtype: string
- name: generic_sentence
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 99897719
num_examples: 1020868
download_size: 94850525
dataset_size: 99897719
- config_name: generics_kb
features:
- name: source
dtype: string
- name: term
dtype: string
- name: quantifier_frequency
dtype: string
- name: quantifier_number
dtype: string
- name: generic_sentence
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 348158966
num_examples: 3433000
download_size: 343284785
dataset_size: 348158966
- config_name: generics_kb_simplewiki
features:
- name: source_name
dtype: string
- name: sentence
dtype: string
- name: sentences_before
sequence: string
- name: sentences_after
sequence: string
- name: concept_name
dtype: string
- name: quantifiers
sequence: string
- name: id
dtype: string
- name: bert_score
dtype: float64
- name: headings
sequence: string
- name: categories
sequence: string
splits:
- name: train
num_bytes: 10039355
num_examples: 12765
download_size: 16437369
dataset_size: 10039355
- config_name: generics_kb_waterloo
features:
- name: source_name
dtype: string
- name: sentence
dtype: string
- name: sentences_before
sequence: string
- name: sentences_after
sequence: string
- name: concept_name
dtype: string
- name: quantifiers
sequence: string
- name: id
dtype: string
- name: bert_score
dtype: float64
splits:
- name: train
num_bytes: 4277214701
num_examples: 3666725
download_size: 0
dataset_size: 4277214701
config_names:
- generics_kb
- generics_kb_best
- generics_kb_simplewiki
- generics_kb_waterloo
---
# Dataset Card for Generics KB
## 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:** [Homepage](https://allenai.org/data/genericskb)
- **Repository:** [Repository](https://drive.google.com/drive/folders/1vqfVXhJXJWuiiXbUa4rZjOgQoJvwZUoT)
- **Paper:** [Paper](https://arxiv.org/pdf/2005.00660.pdf)
- **Point of Contact:**[Sumithra Bhakthavatsalam](sumithrab@allenai.org)
[Chloe Anastasiades](chloea@allenai.org)
[Peter Clark](peterc@allenai.org)
Alternatively email_at info@allenai.org
### Dataset Summary
Dataset contains a large (3.5M+ sentence) knowledge base of *generic sentences*. This is the first large resource to contain *naturally occurring* generic sentences, rich in high-quality, general, semantically complete statements. All GenericsKB sentences are annotated with their topical term, surrounding context (sentences), and a (learned) confidence. We also release GenericsKB-Best (1M+ sentences), containing the best-quality generics in GenericsKB augmented with selected, synthesized generics from WordNet and ConceptNet. This demonstrates that GenericsKB can be a useful resource for NLP applications, as well as providing data for linguistic studies of generics and their semantics.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
The GENERICSKB contains 3,433,000 sentences. GENERICS-KB-BEST comprises of GENERICSKB generics with a score > 0.234, augmented with short generics synthesized from three other resources for all the terms (generic categories) in GENERICSKB- BEST. GENERICSKB-BEST contains 1,020,868 generics (774,621 from GENERICSKB plus 246,247 synthesized).
SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org). The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains.
###### Sample SimpleWikipedia/ Waterloo config look like this
```
{'source_name': 'SimpleWikipedia', 'sentence': 'Sepsis happens when the bacterium enters the blood and make it form tiny clots.', 'sentences_before': [], 'sentences_after': [], 'concept_name': 'sepsis', 'quantifiers': {}, 'id': 'SimpleWikipedia--tmp-sw-rs1-with-bug-fixes-initialprocessing-inputs-articles-with-clean-sentences-jsonl-c27816b298e1e0b5326916ee4e2fd0f1603caa77-100-Bubonic-plague--Different-kinds-of-the-same-disease--Septicemic-plague-0-0-039fbe9c11adde4ff9a829376ca7e0ed-1560874903-47882-/Users/chloea/Documents/aristo/commonsense/kbs/simplewikipedia/commonsense-filtered-good-rs1.jsonl-1f33b1e84018a2b1bfdf446f9a6491568b5585da-1561086091.8220549', 'bert_score': 0.8396177887916565}
```
###### Sample instance for Generics KB datasets look like this:
```
{'source': 'Waterloo', 'term': 'aardvark', 'quantifier_frequency': '', 'quantifier_number': '', 'generic_sentence': 'Aardvarks are very gentle animals.', 'score': '0.36080607771873474'}
{'source': 'TupleKB', 'term': 'aardvark', 'quantifier_frequency': '', 'quantifier_number': '', 'generic_sentence': 'Aardvarks dig burrows.', 'score': '1.0'}
```
### Data Fields
The fields in GenericsKB-Best.tsv and GenericsKB.tsv are as follows:
- `SOURCE`: denotes the source of the generic
- `TERM`: denotes the category that is the topic of the generic.
- `GENERIC SENTENCE`: is the sentence itself.
- `SCORE`: Is the BERT-trained score, measuring the degree to which the generic represents a "useful, general truth" about the world (as judged by crowdworkers). Score ranges from 0 (worst) to 1 (best). Sentences with scores below 0.23 (corresponding to an "unsure" vote by crowdworkers) are in GenericsKB, but are not part of GenericsKB-Best due to their unreliability.
- `QUANTIFIER_FREQUENCY`:For generics with explicit quantifiers (all, most, etc.) the quantifier is listed - Frequency contains values such as 'usually', 'often', 'frequently'
- `QUANTIFIER_NUMBER`: For generics with explicit quantifiers (all, most, etc.) with values such as 'all'|'any'|'most'|'much'|'some' etc...
The SimpleWiki/Waterloo generics from GenericsKB.tsv, but expanded to also include their surrounding context (before/after sentences). The Waterloo generics are the majority of GenericsKB. This zip file is 1.4GB expanding to 5.5GB.
There is a json representation for every generic statement in the Generics KB. The generic statement is stored under the `sentence` field within the `knowledge` object. There is also a `bert_score` associated with each sentence which is the BERT-based classifier's score for the 'genericness' of the statement. This score is meant to reflect how much generalized world knowledge/commonsense the statement captures vs only being contextually meaningful.
Detailed description of each of the fields:
- `source_name`: The name of the corpus the generic statement was picked from.
- `sentence`: The generic sentence.
- `sentences_before`: Provides context information surrounding the generic statement from the original corpus.Up to five sentences preceding the generic sentence in the original corpus.
- `sentences_after`: Up to five sentences following the generic sentence in the original corpus.
- `concept_name`: A concept that is the subject of the generic statement.
- `quantifiers`: The quantifiers for the key concept of the generic statement. There can be multiple quantifiers to allow for statements such as "All bats sometimes fly", where 'all' and 'sometimes' are both quantifiers reflecting number and frequency respectively.
- `id`: Unique identifier for a generic statement in the kb.
- `bert_score`: Score for the generic statement from the BERT-based generics classifier.
<br>**Additional fields that apply only to SimpleWiki dataset**
- `headings`: A breadcrumb of section/subsection headings from the top down to the location of the generic statement in the corpus. It applies to SimpleWikipedia which has a hierarchical structure.
- `categories`:The listed categories under which the source article falls. Applies to SimpleWikipedia.
### Data Splits
There are no splits.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data was crawled. SimpleWikipedia is a filtered scrape of SimpleWikipedia pages (simple.wikipedia.org). The Waterloo corpus is 280GB of English plain text, gathered by Charles Clarke (Univ. Waterloo) using a webcrawler in 2001 from .edu domains.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Bert was used to decide whether the sentence is useful or not. Every sentence has a bert score.
#### Who are the annotators?
No annotations were made.
### 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
The GenericsKB is available under the Creative Commons - Attribution 4.0 International - licence.
As an informal summary, from https://creativecommons.org/licenses/by/4.0/, you are free to:
Share ― copy and redistribute the material in any medium or format
Adapt ― remix, transform, and build upon the material for any purpose, even commercially.
under the following terms:
Attribution ― You must give appropriate credit, provide a link to the license, and
indicate if changes were made. You may do so in any reasonable manner,
but not in any way that suggests the licensor endorses you or your use.
No additional restrictions ― You may not apply legal terms or technological measures
that legally restrict others from doing anything the license permits.
For details, see https://creativecommons.org/licenses/by/4.0/ or the or the included
file "Creative Commons ― Attribution 4.0 International ― CC BY 4.0.pdf" in this folder.
### Citation Information
```
@InProceedings{huggingface:dataset,
title = {GenericsKB: A Knowledge Base of Generic Statements},
authors={Sumithra Bhakthavatsalam, Chloe Anastasiades, Peter Clark},
year={2020},
publisher = {Allen Institute for AI},
}
```
### Contributions
Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset. |
parsinlu_reading_comprehension | 2023-08-16T17:04:40.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:20... | null | A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers. | @article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
} | null | 1 | 74 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia|google
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: PersiNLU (Reading Comprehension)
dataset_info:
features:
- name: question
dtype: string
- name: url
dtype: string
- name: context
dtype: string
- name: answers
sequence:
- name: answer_start
dtype: int32
- name: answer_text
dtype: string
config_name: parsinlu-repo
splits:
- name: train
num_bytes: 747679
num_examples: 600
- name: test
num_bytes: 674711
num_examples: 570
- name: validation
num_bytes: 163161
num_examples: 125
download_size: 4105495
dataset_size: 1585527
---
# Dataset Card for PersiNLU (Reading Comprehension)
## 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/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email](d.khashabi@gmail.com)
### Dataset Summary
A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{
'question': 'پیامبر در چه سالی به پیامبری رسید؟',
'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF',
'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.',
'answers': [
{'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'}
]
}
```
### Data Fields
- `question`: the question, mined using Google auto-complete.
- `passage`: the passage that contains the answer.
- `url`: the url from which the passage was mined.
- `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1`
### Data Splits
The train/test split contains 600/575 samples.
## Dataset Creation
### Curation Rationale
The question were collected via Google auto-complete.
The answers were annotated by native speakers.
For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset. |
SetFit/amazon_counterfactual_en | 2022-02-11T13:03:45.000Z | [
"arxiv:2104.06893",
"region:us"
] | SetFit | null | null | null | 0 | 74 | # Amazon Counterfactual Statements
This dataset is the *en-ext* split from [SetFit/amazon_counterfactual](https://huggingface.co/datasets/SetFit/amazon_counterfactual). As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing.
The dataset is described in [amazon-multilingual-counterfactual-dataset](https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset) / [Paper](https://arxiv.org/pdf/2104.06893.pdf)
It contains statements from Amazon reviews about events that did not or cannot take place. |
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}
} | null | 3 | 74 | 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] |
mteb/raw_biorxiv | 2022-09-27T19:15:43.000Z | [
"language:en",
"region:us"
] | mteb | null | null | null | 1 | 74 | ---
language:
- en
--- |
SZTAKI-HLT/HunSum-1 | 2023-01-24T16:21:00.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"multilinguality:monolingual",
"language:hu",
"license:cc-by-nc-sa-4.0",
"region:us"
] | SZTAKI-HLT | null | null | null | 2 | 74 | ---
language:
- hu
multilinguality:
- monolingual
task_categories:
- summarization
task_ids:
- news-articles-summarization
pretty_name: HunSum-1
license: cc-by-nc-sa-4.0
---
# Dataset Card for HunSum-1
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Description
### Dataset Summary
The HunSum-1 Dataset is a Hungarian-language dataset containing over 1.1M unique news articles with lead and other metadata. The dataset contains articles from 9 major Hungarian news websites.
### Supported Tasks and Leaderboards
- 'summarization'
- 'title generation'
## Dataset Structure
### Data Fields
- `uuid`: a string containing the unique id
- `article`: a string containing the body of the news article
- `lead`: a string containing the lead of the article
- `title`: a string containing the title of the article
- `url`: a string containing the URL for the article
- `domain`: a string containing the domain of the url
- `date_of_creation`: a timestamp containing the date when the article was created
- `tags`: a sequence containing the tags of the article
### Data Splits
The HunSum-1 dataset has 3 splits: _train_, _validation_, and _test_.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 1,144,255 |
| Validation | 1996 |
| Test | 1996 |
## Citation
If you use our dataset, please cite the following paper:
```
@inproceedings {HunSum-1,
title = {{HunSum-1: an Abstractive Summarization Dataset for Hungarian}},
booktitle = {XIX. Magyar Számítógépes Nyelvészeti Konferencia (MSZNY 2023)},
year = {2023},
publisher = {Szegedi Tudományegyetem, Informatikai Intézet},
address = {Szeged, Magyarország},
author = {Barta, Botond and Lakatos, Dorina and Nagy, Attila and Nyist, Mil{\'{a}}n Konor and {\'{A}}cs, Judit},
pages = {231--243}
}
``` |
buildthething/me | 2023-02-03T14:47:47.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:bigscience-openrail-m",
"region:us"
] | buildthething | null | null | null | 0 | 74 | ---
license: bigscience-openrail-m
task_categories:
- text-to-image
language:
- en
pretty_name: me data
size_categories:
- n<1K
--- |
mstz/german | 2023-04-16T17:28:54.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"breast",
"tabular_classification",
"binary_classification",
"region:us"
] | mstz | null | null | null | 0 | 74 | ---
language:
- en
tags:
- breast
- tabular_classification
- binary_classification
pretty_name: German
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- encoding
- loan
license: cc
---
# German
The [German dataset](https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset on loan grants to customers.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| loan | Binary classification | Has the loan request been accepted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/german", "loan")["train"]
```
# Features
|**Feature** |**Type** |
|------------------------------------|-----------|
|`checking_account_status` | `int8` |
|`account_life_in_months` | `int8` |
|`credit_status` | `int8` |
|`loan_purpose` | `string` |
|`current_credit` | `int32` |
|`current_savings` | `int8` |
|`employed_since` | `int8` |
|`installment_rate_percentage` | `int8` |
|`sex` | `int8` |
|`marital_status` | `string` |
|`guarantors` | `int8` |
|`years_living_in_current_residence` | `int8` |
|`age` | `int8` |
|`installment_plans` | `string` |
|`housing_status` | `int8` |
|`nr_credit_accounts_in_bank` | `int8` |
|`job_status` | `int8` |
|`number_of_people_in_support` | `int8` |
|`has_registered_phone_number` | `int8` |
|`is_foreign` | `int8` | |
mstz/higgs | 2023-04-16T17:31:30.000Z | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"higgs",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | @article{baldi2014searching,
title={Searching for exotic particles in high-energy physics with deep learning},
author={Baldi, Pierre and Sadowski, Peter and Whiteson, Daniel},
journal={Nature communications},
volume={5},
number={1},
pages={4308},
year={2014},
publisher={Nature Publishing Group UK London}
} | null | 0 | 74 | ---
language:
- en
tags:
- higgs
- tabular_classification
- binary_classification
- UCI
pretty_name: Higgs
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- higgs
license: cc
---
# Higgs
The [Higgs dataset](https://www.nature.com/articles/ncomms5308/) from "[Searching for exotic particles in high-energy physics with deep learning](https://www.nature.com/articles/ncomms5308/)".
Try to classify particles as Higgs bosons.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| higgs | Binary classification | Is the particle a Higgs boson? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/higgs")["train"]
```
# Features
|**Feature** |**Type** |
|---------------------------|-----------|
|`lepton_pT` |`[float64]`|
|`lepton_eta` |`[float64]`|
|`lepton_phi` |`[float64]`|
|`missing_energy_magnitude` |`[float64]`|
|`missing_energy_phi` |`[float64]`|
|`jet1pt` |`[float64]`|
|`jet1eta` |`[float64]`|
|`jet1phi` |`[float64]`|
|`jet1b` |`[float64]`|
|`jet2pt` |`[float64]`|
|`jet2eta` |`[float64]`|
|`jet2phi` |`[float64]`|
|`jet2b` |`[float64]`|
|`jet3pt` |`[float64]`|
|`jet3eta` |`[float64]`|
|`jet3phi` |`[float64]`|
|`jet3b` |`[float64]`|
|`jet4pt` |`[float64]`|
|`jet4eta` |`[float64]`|
|`jet4phi` |`[float64]`|
|`jet4b` |`[float64]`|
|`m_jj` |`[float64]`|
|`m_jjj` |`[float64]`|
|`m_lv` |`[float64]`|
|`m_jlv` |`[float64]`|
|`m_bb` |`[float64]`|
|`m_wbb` |`[float64]`|
|`m_wwbb` |`[float64]`| |
mstz/spect | 2023-04-16T18:02:28.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"spect",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_spect_heart_95,
author = {Cios,Krzysztof, Kurgan,Lukasz & Goodenday,Lucy},
title = {{SPECT Heart}},
year = {2001},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5P304}}
} | null | 0 | 74 | ---
language:
- en
tags:
- spect
- tabular_classification
- binary_classification
- UCI
pretty_name: Ozone
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- spect
- spectf
license: cc
---
# Ozone
The [Ozone dataset](https://archive.ics.uci.edu/ml/datasets/Ozone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| spect | Binary classification | Is there an ozone layer?|
| spectf | Binary classification | Is there an ozone layer?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/spect", "spect")["train"]
``` |
distil-whisper/common_voice_13_0 | 2023-09-25T10:30:13.000Z | [
"task_categories:automatic-speech-recognition",
"language:en",
"license:cc0-1.0",
"region:us"
] | distil-whisper | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} | null | 0 | 74 | ---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- en
-pretty_name: Common Voice 13
---
# Distil Whisper: Common Voice 13
This is a variant of the [Common Voice 13](https://huggingface.co/datasets/mozilla_foundation/common_voice_13) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/mozilla_foundation/common_voice_13).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/common_voice_13_0", "en")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/common_voice_13_0", "en", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under cc0-1.0.
|
TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k | 2023-05-31T02:01:37.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | TigerResearch | null | null | null | 7 | 74 | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于leetcode-solutions数据集，加工生成的代码类sft数据集
<p align="center" width="40%">
原始来源：[https://www.kaggle.com/datasets/erichartford/leetcode-solutions](https://www.kaggle.com/datasets/erichartford/leetcode-solutions)
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k')
```
|
yulongmannlp/dev_para | 2023-06-26T00:32:27.000Z | [
"region:us"
] | yulongmannlp | Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | null | 0 | 74 | Entry not found |
eckendoerffer/justice_fr | 2023-09-30T05:38:31.000Z | [
"size_categories:100K<n<1M",
"language:fr",
"license:cc-by-sa-4.0",
"justice",
"law",
"legal",
"region:us"
] | eckendoerffer | null | null | null | 3 | 74 | ---
license: cc-by-sa-4.0
language:
- fr
pretty_name: Law & decision from French justice system
tags:
- justice
- law
- legal
size_categories:
- 100K<n<1M
---
# Dataset Card for French Legal Dataset
## Dataset Description
The dataset contains a comprehensive collection of French legal books, codes, and appellate court decisions. It encompasses the following:
- 150,938 rows -> 140,000 articles of laws, decrees, and orders from the 78 French books and codes, covering all legal domains. The total number of pages is approximately 35,000.
- 191,741 rows -> 53,000 appellate court decisions spanning from 2013 to the present day. The dataset includes a wide range of cases and legal opinions. The total number of pages is approximately 150,000.
- 1,621 rows -> 1,621 definitions and abbreviations commonly used in legal texts, aiding in understanding and interpreting the legal terminology.s used in legal texts.
Total: 344,300 rows
Line breaks are included in the legal texts. They are necessary to maintain the indentations and paragraphs mentioned in other laws and court decisions.
### Languages
The dataset is in French only.
## Dataset Structure
```
{
"instruction":"Code du travail > Partie l\u00e9gislative > Livre III : Les institutions repr\u00e9sentatives du personnel > Titre Ier : Comit\u00e9 social et \u00e9conomique > Chapitre IV : Composition, \u00e9lections et mandat > Section 1 : Composition > Article L2314-2 (Loi)",
"input":"Je souhaite l'Article L2314-2 du Code du travail",
"output":"Voici l'Article L2314-2 du Code du travail: Sous r\u00e9serve des dispositions applicables dans les entreprises de moins de trois cents salari\u00e9s, pr\u00e9vues \u00e0 l'article L. 2143-22, chaque organisation syndicale repr\u00e9sentative dans l'entreprise ou l'\u00e9tablissement peut d\u00e9signer un repr\u00e9sentant syndical au comit\u00e9. Il assiste aux s\u00e9ances avec voix consultative. Il est choisi parmi les membres du personnel de l'entreprise et doit remplir les conditions d'\u00e9ligibilit\u00e9 au comit\u00e9 social et \u00e9conomique fix\u00e9es \u00e0 l'article L. 2314-19."
},
```
### Data Fields
- `instruction`:
- French books and codes -> hierarchy from law text:
"Code pénal > Partie législative > Livre II : Des crimes et délits contre les personnes > Titre II : Des atteintes à la personne humaine > Chapitre Ier : Des atteintes à la vie de la personne > Section 2 : Des atteintes involontaires à la vie > Article 221-6"
- Court decisions -> location, chamber, decision number, decision date, part:
"Cour d'appel de Paris I5, Cour de cassation Chambre commerciale financière et économique, décision 18-13.763 du 14/04/2021, partie 1"
- `input`:
- French books and codes -> questions with multiple variations, such as: "What does Article XX of Code XX say?"
- Court decisions -> empty
- `output`:
- French books and codes -> laws text
- Court decisions -> decisions text
The text has been limited/split to approximately 820 words per row, with an average of 1500 tokens (French -> Falcon tokenizer). The goal is to not exceed 2048 tokens, with a margin of error.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
- All French codes (PDF): https://www.legifrance.gouv.fr/liste/code?etatTexte=VIGUEUR&etatTexte=VIGUEUR_DIFF
- Court decisions from JUDILIBRE API: https://piste.gouv.fr/index.php?option=com_apiportal&view=apitester&usage=api&apitab=tests&apiName=JUDILIBRE&apiId=b6d2f389-c3ec-4eb3-9075-bc24d0783781&managerId=2&type=rest&apiVersion=1.0.0&Itemid=265&swaggerVersion=2.0&lang=fr
#### Who are the source language producers?
Comming directly from French justice system.
## Additional Information
### Licensing Information
The dataset is available under the Creative Commons Attribution-ShareAlike License
|
DFKI-SLT/conll2012_ontonotesv5 | 2023-07-13T15:02:27.000Z | [
"region:us"
] | DFKI-SLT | OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above. | @inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston. OntoNotes Release 5.0 LDC2013T19. Web Download. Philadelphia: Linguistic Data Consortium, 2013. | null | 0 | 74 | Entry not found |
jxu9001/tagged_addresses_v4 | 2023-08-02T23:31:21.000Z | [
"region:us"
] | jxu9001 | null | null | null | 0 | 74 | ---
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: tokens
sequence: string
- name: tags
sequence: int64
splits:
- name: train
num_bytes: 10944348
num_examples: 105594
- name: validation
num_bytes: 1363481
num_examples: 13199
- name: test
num_bytes: 1370992
num_examples: 13200
download_size: 3795824
dataset_size: 13678821
---
# Dataset Card for "tagged_addresses_v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml/alpagasus_cleaned_ar_reviewed | 2023-10-01T13:04:41.000Z | [
"region:us"
] | arbml | null | null | null | 0 | 74 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_en
dtype: string
- name: index
dtype: string
- name: instruction_en
dtype: string
- name: output
dtype: string
- name: output_en
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 3037648
num_examples: 2959
download_size: 0
dataset_size: 3037648
---
# Dataset Card for "alpagasus_cleaned_ar_reviewed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sibozhu/wukong | 2023-09-12T08:26:40.000Z | [
"region:us"
] | sibozhu | null | null | null | 0 | 74 | Entry not found |
asun17904/imdb-test | 2023-09-17T16:15:11.000Z | [
"region:us"
] | asun17904 | null | null | null | 0 | 74 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: test
num_bytes: 19590411.0
num_examples: 15000
download_size: 12828803
dataset_size: 19590411.0
---
# Dataset Card for "imdb-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
euclaise/WritingPromptsX | 2023-09-22T14:37:38.000Z | [
"size_categories:1M<n<10M",
"license:cc0-1.0",
"region:us"
] | euclaise | null | null | null | 0 | 74 | ---
dataset_info:
features:
- name: post_title
dtype: string
- name: body
dtype: string
- name: score
dtype: int64
- name: gilded
dtype: int64
- name: post_score
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2040557544
num_examples: 1245546
download_size: 1016138545
dataset_size: 2040557544
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc0-1.0
size_categories:
- 1M<n<10M
---
# Dataset Card for "WritingPromptsX"
Comments from r/WritingPrompts, up to 12-2022, from PushShift. Inspired by [WritingPrompts](https://huggingface.co/datasets/euclaise/writingprompts), but a bit more complete. |
nlewins/ceb_en | 2023-09-29T16:10:21.000Z | [
"region:us"
] | nlewins | null | null | null | 0 | 74 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: ceb
dtype: string
- name: en
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 903970.0650306748
num_examples: 3187
- name: test
num_bytes: 155510.0
num_examples: 540
download_size: 559232
dataset_size: 1059480.065030675
---
# Dataset Card for "ceb_en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ar_sarcasm | 2023-03-16T14:13:22.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other-semeval_2017",
"source_datasets:extended|other-astd",
"language:ar... | null | ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD)
and adds sarcasm and dialect labels to them. The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic. | @inproceedings{abu-farha-magdy-2020-arabic,
title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
author = "Abu Farha, Ibrahim and Magdy, Walid",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resource Association",
url = "https://www.aclweb.org/anthology/2020.osact-1.5",
pages = "32--39",
language = "English",
ISBN = "979-10-95546-51-1",
} | null | 4 | 73 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- ar
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-semeval_2017
- extended|other-astd
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: ArSarcasm
tags:
- sarcasm-detection
dataset_info:
features:
- name: dialect
dtype:
class_label:
names:
'0': egypt
'1': gulf
'2': levant
'3': magreb
'4': msa
- name: sarcasm
dtype:
class_label:
names:
'0': non-sarcastic
'1': sarcastic
- name: sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: original_sentiment
dtype:
class_label:
names:
'0': negative
'1': neutral
'2': positive
- name: tweet
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 1829167
num_examples: 8437
- name: test
num_bytes: 458218
num_examples: 2110
download_size: 750717
dataset_size: 2287385
---
# Dataset Card for ArSarcasm
## 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](https://github.com/iabufarha/ArSarcasm)
- **Paper:** https://www.aclweb.org/anthology/2020.osact-1.5/
### Dataset Summary
ArSarcasm is a new Arabic sarcasm detection dataset.
The dataset was created using previously available Arabic sentiment analysis
datasets ([SemEval 2017](https://www.aclweb.org/anthology/S17-2088.pdf)
and [ASTD](https://www.aclweb.org/anthology/D15-1299.pdf)) and adds sarcasm and
dialect labels to them.
The dataset contains 10,547 tweets, 1,682 (16%) of which are sarcastic.
For more details, please check the paper
[From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://www.aclweb.org/anthology/2020.osact-1.5/)
### Supported Tasks and Leaderboards
You can get more information about an Arabic sarcasm tasks and leaderboard
[here](https://sites.google.com/view/ar-sarcasm-sentiment-detection/).
### Languages
Arabic (multiple dialects)
## Dataset Structure
### Data Instances
```javascript
{'dialect': 1, 'original_sentiment': 0, 'sarcasm': 0, 'sentiment': 0, 'source': 'semeval', 'tweet': 'نصيحه ما عمرك اتنزل لعبة سوبر ماريو مش زي ما كنّا متوقعين الله يرحم ايامات السيقا والفاميلي #SuperMarioRun'}
```
### Data Fields
- tweet: the original tweet text
- sarcasm: 0 for non-sarcastic, 1 for sarcastic
- sentiment: 0 for negative, 1 for neutral, 2 for positive
- original_sentiment: 0 for negative, 1 for neutral, 2 for positive
- source: the original source of tweet: SemEval or ASTD
- dialect: 0 for Egypt, 1 for Gulf, 2 for Levant, 3 for Magreb, 4 for Modern Standard Arabic (MSA)
### Data Splits
The training set contains 8,437 tweets, while the test set contains 2,110 tweets.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset was created using previously available Arabic sentiment analysis datasets (SemEval 2017 and ASTD) and adds sarcasm and dialect labels to them.
#### Who are the source language producers?
SemEval 2017 and ASTD
### Annotations
#### Annotation process
For the annotation process, we used Figure-Eight
crowdsourcing platform. Our main objective was to annotate the
data for sarcasm detection, but due to the challenges imposed by dialectal variations, we decided to add the annotation for dialects. We also include a new annotation for
sentiment labels in order to have a glimpse of the variability and subjectivity between different annotators. Thus, the
annotators were asked to provide three labels for each tweet
as the following:
- Sarcasm: sarcastic or non-sarcastic.
- Sentiment: positive, negative or neutral.
- Dialect: Egyptian, Gulf, Levantine, Maghrebi or Modern Standard Arabic (MSA).
#### Who are the annotators?
Figure-Eight crowdsourcing platform
### 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
- Ibrahim Abu-Farha
- Walid Magdy
### Licensing Information
MIT
### Citation Information
```
@inproceedings{abu-farha-magdy-2020-arabic,
title = "From {A}rabic Sentiment Analysis to Sarcasm Detection: The {A}r{S}arcasm Dataset",
author = "Abu Farha, Ibrahim and Magdy, Walid",
booktitle = "Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resource Association",
url = "https://www.aclweb.org/anthology/2020.osact-1.5",
pages = "32--39",
language = "English",
ISBN = "979-10-95546-51-1",
}
```
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. |
arabic_billion_words | 2023-06-01T14:59:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<... | null | Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML. | @article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
} | null | 11 | 73 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Arabic Billion Words
dataset_info:
- config_name: Alittihad
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1601790302
num_examples: 349342
download_size: 348259999
dataset_size: 1601790302
- config_name: Almasryalyoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1056197870
num_examples: 291723
download_size: 242604438
dataset_size: 1056197870
- config_name: Almustaqbal
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1545659336
num_examples: 446873
download_size: 350826797
dataset_size: 1545659336
- config_name: Alqabas
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2631729746
num_examples: 817274
download_size: 595274646
dataset_size: 2631729746
- config_name: Echoroukonline
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 464386206
num_examples: 139732
download_size: 108184378
dataset_size: 464386206
- config_name: Ryiadh
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3101294859
num_examples: 858188
download_size: 691264971
dataset_size: 3101294859
- config_name: Sabanews
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 198019614
num_examples: 92149
download_size: 38214558
dataset_size: 198019614
- config_name: SaudiYoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2723291416
num_examples: 888068
download_size: 605537923
dataset_size: 2723291416
- config_name: Techreen
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1103458209
num_examples: 314597
download_size: 252976781
dataset_size: 1103458209
- config_name: Youm7
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3004689464
num_examples: 1172136
download_size: 617708074
dataset_size: 3004689464
config_names:
- Alittihad
- Almasryalyoum
- Almustaqbal
- Alqabas
- Echoroukonline
- Ryiadh
- Sabanews
- SaudiYoum
- Techreen
- Youm7
---
# Dataset Card for Arabic Billion Words 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:** http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus
- **Repository:**
- **Paper:** https://arxiv.org/pdf/1611.04033
- **Leaderboard:**
- **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com)
### Dataset Summary
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
This is an example of the "Almasryalyoum" configuration subset:
```python
{
"url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300",
"head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد",
"date": "19/5/2007",
"text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره.
وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري.
يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري.
وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""",
}
```
### Data Fields
The data fields are:
- "url": string, original url of the article,
- "head_line": string, headline of the article,
- "date": string, date of the article,
- "text": string, text content of the article,
### Data Splits
There is only one "training" split for all configuration subsets, containing the following number of examples:
| | Number of examples |
|:---------------|-------------------:|
| Alittihad | 349342 |
| Almasryalyoum | 291723 |
| Almustaqbal | 446873 |
| Alqabas | 817274 |
| Echoroukonline | 139732 |
| Ryiadh | 858188 |
| Sabanews | 92149 |
| SaudiYoum | 888068 |
| Techreen | 314597 |
| Youm7 | 1172136 |
## 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{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
code_x_glue_tc_nl_code_search_adv | 2023-07-27T15:51:10.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"arxiv:2102.04664",
... | null | The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English. | @article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
} | null | 2 | 73 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
pretty_name: CodeXGlueTcNlCodeSearchAdv
dataset_info:
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
- name: docstring_summary
dtype: string
- name: parameters
dtype: string
- name: return_statement
dtype: string
- name: argument_list
dtype: string
- name: identifier
dtype: string
- name: nwo
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 820716084
num_examples: 251820
- name: validation
num_bytes: 23468834
num_examples: 9604
- name: test
num_bytes: 47433760
num_examples: 19210
download_size: 966025624
dataset_size: 891618678
---
# Dataset Card for "code_x_glue_tc_nl_code_search_adv"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
- **Paper:** https://arxiv.org/abs/2102.04664
### Dataset Summary
CodeXGLUE NL-code-search-Adv dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/NL-code-search-Adv
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes from a given **English** natural language query.
### Languages
- Python **programming** language
- English **natural** language
## Dataset Structure
### Data Instances
An example of 'validation' looks as follows.
```
{
"argument_list": "",
"code": "def Func(arg_0, arg_1='.', arg_2=True, arg_3=False, **arg_4):\n \"\"\"Downloads Dailymotion videos by URL.\n \"\"\"\n\n arg_5 = get_content(rebuilt_url(arg_0))\n arg_6 = json.loads(match1(arg_5, r'qualities\":({.+?}),\"'))\n arg_7 = match1(arg_5, r'\"video_title\"\\s*:\\s*\"([^\"]+)\"') or \\\n match1(arg_5, r'\"title\"\\s*:\\s*\"([^\"]+)\"')\n arg_7 = unicodize(arg_7)\n\n for arg_8 in ['1080','720','480','380','240','144','auto']:\n try:\n arg_9 = arg_6[arg_8][1][\"url\"]\n if arg_9:\n break\n except KeyError:\n pass\n\n arg_10, arg_11, arg_12 = url_info(arg_9)\n\n print_info(site_info, arg_7, arg_10, arg_12)\n if not arg_3:\n download_urls([arg_9], arg_7, arg_11, arg_12, arg_1=arg_1, arg_2=arg_2)",
"code_tokens": ["def", "Func", "(", "arg_0", ",", "arg_1", "=", "'.'", ",", "arg_2", "=", "True", ",", "arg_3", "=", "False", ",", "**", "arg_4", ")", ":", "arg_5", "=", "get_content", "(", "rebuilt_url", "(", "arg_0", ")", ")", "arg_6", "=", "json", ".", "loads", "(", "match1", "(", "arg_5", ",", "r'qualities\":({.+?}),\"'", ")", ")", "arg_7", "=", "match1", "(", "arg_5", ",", "r'\"video_title\"\\s*:\\s*\"([^\"]+)\"'", ")", "or", "match1", "(", "arg_5", ",", "r'\"title\"\\s*:\\s*\"([^\"]+)\"'", ")", "arg_7", "=", "unicodize", "(", "arg_7", ")", "for", "arg_8", "in", "[", "'1080'", ",", "'720'", ",", "'480'", ",", "'380'", ",", "'240'", ",", "'144'", ",", "'auto'", "]", ":", "try", ":", "arg_9", "=", "arg_6", "[", "arg_8", "]", "[", "1", "]", "[", "\"url\"", "]", "if", "arg_9", ":", "break", "except", "KeyError", ":", "pass", "arg_10", ",", "arg_11", ",", "arg_12", "=", "url_info", "(", "arg_9", ")", "print_info", "(", "site_info", ",", "arg_7", ",", "arg_10", ",", "arg_12", ")", "if", "not", "arg_3", ":", "download_urls", "(", "[", "arg_9", "]", ",", "arg_7", ",", "arg_11", ",", "arg_12", ",", "arg_1", "=", "arg_1", ",", "arg_2", "=", "arg_2", ")"],
"docstring": "Downloads Dailymotion videos by URL.",
"docstring_summary": "Downloads Dailymotion videos by URL.",
"docstring_tokens": ["Downloads", "Dailymotion", "videos", "by", "URL", "."],
"func_name": "",
"id": 0,
"identifier": "dailymotion_download",
"language": "python",
"nwo": "soimort/you-get",
"original_string": "",
"parameters": "(url, output_dir='.', merge=True, info_only=False, **kwargs)",
"path": "src/you_get/extractors/dailymotion.py",
"repo": "",
"return_statement": "",
"score": 0.9997601509094238,
"sha": "b746ac01c9f39de94cac2d56f665285b0523b974",
"url": "https://github.com/soimort/you-get/blob/b746ac01c9f39de94cac2d56f665285b0523b974/src/you_get/extractors/dailymotion.py#L13-L35"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### default
| field name | type | description |
|-----------------|-----------------------|-----------------------------------------------------------------------------------|
|id |int32 | Index of the sample |
|repo |string | repo: the owner/repo |
|path |string | path: the full path to the original file |
|func_name |string | func_name: the function or method name |
|original_string |string | original_string: the raw string before tokenization or parsing |
|language |string | language: the programming language |
|code |string | code/function: the part of the original_string that is code |
|code_tokens |Sequence[string] | code_tokens/function_tokens: tokenized version of code |
|docstring |string | docstring: the top-level comment or docstring, if it exists in the original string|
|docstring_tokens |Sequence[string] | docstring_tokens: tokenized version of docstring |
|sha |string | sha of the file |
|url |string | url of the file |
|docstring_summary|string | Summary of the docstring |
|parameters |string | parameters of the function |
|return_statement |string | return statement |
|argument_list |string | list of arguments of the function |
|identifier |string | identifier |
|nwo |string | nwo |
|score |datasets.Value("float"]| score for this search |
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|251820| 9604|19210|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{DBLP:journals/corr/abs-2102-04664,
author = {Shuai Lu and
Daya Guo and
Shuo Ren and
Junjie Huang and
Alexey Svyatkovskiy and
Ambrosio Blanco and
Colin B. Clement and
Dawn Drain and
Daxin Jiang and
Duyu Tang and
Ge Li and
Lidong Zhou and
Linjun Shou and
Long Zhou and
Michele Tufano and
Ming Gong and
Ming Zhou and
Nan Duan and
Neel Sundaresan and
Shao Kun Deng and
Shengyu Fu and
Shujie Liu},
title = {CodeXGLUE: {A} Machine Learning Benchmark Dataset for Code Understanding
and Generation},
journal = {CoRR},
volume = {abs/2102.04664},
year = {2021}
}
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
iarfmoose/question_generator | 2021-11-29T05:22:03.000Z | [
"region:us"
] | iarfmoose | null | null | null | 4 | 73 | This dataset is made up of data taken from SQuAD v2.0, RACE, CoQA, and MSMARCO. Some examples have been filtered out of the original datasets and others have been modified.
There are two fields; question and text. The question field contains the question, and the text field contains both the answer and the context in the following format:
"\<answer> (answer text) \<context> (context text)"
The <answer> and <context> are included as special tokens in the question generator's tokenizer.
This dataset is intended to be used with the [question_generator repo](https://github.com/AMontgomerie/question_generator) to train the question generator model.
|
google/wit | 2022-07-04T10:47:07.000Z | [
"task_categories:text-retrieval",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"lang... | google | Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models. | @article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
} | null | 14 | 73 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- af
- ar
- ast
- azb
- be
- bg
- bn
- br
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- hr
- hu
- hy
- id
- it
- iw
- ja
- ka
- ko
- la
- lt
- lv
- mk
- ml
- ms
- nl
- nn
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sr
- sv
- th
- tr
- uk
- ur
- vi
- vo
- zh
license:
- cc-by-sa-3.0
multilinguality:
- multilingual
paperswithcode_id: wit
pretty_name: Wikipedia-based Image Text
size_categories:
- 10M<n<100M
source_datasets:
- original
- extended|wikipedia
task_categories:
- text-retrieval
- image-to-text
task_ids:
- text-retrieval-other-text-image-retrieval
- image-captioning
---
# Dataset Card for WIT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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:** [WIT homepage](https://github.com/google-research-datasets/wit)
- **Repository:** [WIT repository](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)
- **Leaderboard:** [WIT leaderboard](https://www.kaggle.com/c/wikipedia-image-caption)
- **Point of Contact:** [WIT e-mail](mailto:wit-dataset@google.com)
### Dataset Summary
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
A few unique advantages of WIT:
* The largest multimodal dataset (time of this writing) by the number of image-text examples.
* A massively multilingual (first of its kind) with coverage for over 100+ languages.
* A collection of diverse set of concepts and real world entities.
* Brings forth challenging real-world test sets.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": get_datasets_user_agent()},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("wit")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.
- `text-retrieval`: The goal in this task is to build a model that retrieves the text closest to an image.
In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption.
### Languages
The dataset contains examples from all Wikipedia languages, with the following stats:
Image-Text | # Lang | Uniq. Images | # Lang
------------ | ------ | ------------- | ------
total > 1M | 9 | images > 1M | 6
total > 500K | 10 | images > 500K | 12
total > 100K | 36 | images > 100K | 35
total > 50K | 15 | images > 50K | 17
total > 14K | 38 | images > 13K | 38
## Dataset Structure
### Data Instances
```
{
'language': 'en',
'page_url': 'https://en.wikipedia.org/wiki/Oxydactylus',
'image_url': 'https://upload.wikimedia.org/wikipedia/commons/5/5f/Oxydactylus_longipes_fm.jpg',
'page_title': 'Oxydactylus',
'section_title': None,
'hierarchical_section_title': 'Oxydactylus',
'caption_reference_description': None,
'caption_attribution_description': 'English: Mounted skeleton of Oxydactylus longipes in the Field Museum of Natural History.',
'caption_alt_text_description': None,
'mime_type': 'image/jpeg',
'original_height': 3564,
'original_width': 2748,
'is_main_image': True,
'attribution_passes_lang_id': True,
'page_changed_recently': True,
'context_page_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene, existing for approximately 14 million years. The name is from the Ancient Greek οξύς and δάκτυλος.\nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.',
'context_section_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene (28.4–13.7 mya), existing for approximately 14 million years. The name is from the Ancient Greek οξύς (oxys, "sharp")and δάκτυλος (daktylos, "finger").\n \nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.'
}
```
### Data Fields
- `language`: Language code depicting wikipedia language of the page
- `page_url`: URL to wikipedia page
- `image_url`: URL to wikipedia image
- `page_title`: Wikipedia page's title
- `section_title`: Section's title
- `hierarchical_section_title`: Hierarchical section's title
- `caption_reference_description`: This is the caption that is visible on the wiki page directly below the image.
- `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article.
- `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
- `mime_type`: Mime type associated to the image.
- `original_height`: Image height
- `original_width`: Image width
- `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
- `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description).
- `page_changed_recently`: [More Information Needed]
- `context_page_description`: Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
- `context_section_description`: Text within the image's section.
<p align='center'>
<img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
<b>Figure: WIT annotation example. </b>
</p>
Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
### Data Splits
All data is held in `train` split, with a total of 37046386 rows.
## Dataset Creation
### Curation Rationale
From the [repository](https://github.com/google-research-datasets/wit#motivation):
> Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.
>
> To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.
>
> The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).
### Source Data
#### Initial Data Collection and Normalization
From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
> We started with all Wikipedia content pages (i.e., ignoring other
pages that have discussions, comments and such). These number about ∼124M pages across 279 languages.
#### Who are the source language producers?
Text was extracted from Wikipedia.
### Annotations
#### Annotation process
WIT was constructed using an automatic process. However it was human-validated.
From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
> To further verify the quality of the WIT dataset we performed a
study using (crowd-sourced) human annotators. As seen in Fig. 3,
we asked raters to answer 3 questions. Given an image and the page
title, raters first evaluate the quality of the attribution description
and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
the image, "Maybe" if it is sufficiently explanatory and "No" if it is
irrelevant or the image is inappropriate.
#### 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
From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
> Lastly we found that certain image-text pairs occurred very
frequently. These were often generic images that did not have
much to do with the main article page. Common examples
included flags, logos, maps, insignia and such. To prevent
biasing the data, we heavily under-sampled all such images
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
```
### Contributions
Thanks to [@thomasw21](https://github.com/thomasw21), [@nateraw](https://github.com/nateraw) and [hassiahk](https://github.com/hassiahk) for adding this dataset. |
joelniklaus/lextreme | 2023-04-29T07:02:17.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"task_ids:named-entity-recognition",
"annotations_creators:other",
"language_creators:found",
"multilinguality... | joelniklaus | The LEXTREME Benchmark is a collection of multilingual datasets for evaluating model performance
across a diverse set of legal NLU tasks. | @misc{niklaus2023lextreme,
title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain},
author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis},
year={2023},
eprint={2301.13126},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 15 | 73 | ---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: "LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding"
size_categories:
- 10K<n<100K
source_datasets:
- extended
task_categories:
- text-classification
- token-classification
task_ids:
- multi-class-classification
- multi-label-classification
- topic-classification
- named-entity-recognition
---
# Dataset Card for LEXTREME: A Multilingual Legal Benchmark for Natural Language Understanding
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
The dataset consists of 11 diverse multilingual legal NLU datasets. 6 datasets have one single configuration and 5 datasets have two or three configurations. This leads to a total of 18 tasks (8 single-label text classification tasks, 5 multi-label text classification tasks and 5 token-classification tasks).
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/lextreme", "swiss_judgment_prediction")
```
### Supported Tasks and Leaderboards
The dataset supports the tasks of text classification and token classification.
In detail, we support the folliwing tasks and configurations:
| task | task type | configurations | link |
|:---------------------------|--------------------------:|---------------------------------:|-------------------------------------------------------------------------------------------------------:|
| Brazilian Court Decisions | Judgment Prediction | (judgment, unanimity) | [joelito/brazilian_court_decisions](https://huggingface.co/datasets/joelito/brazilian_court_decisions) |
| Swiss Judgment Prediction | Judgment Prediction | default | [joelito/swiss_judgment_prediction](https://huggingface.co/datasets/swiss_judgment_prediction) |
| German Argument Mining | Argument Mining | default | [joelito/german_argument_mining](https://huggingface.co/datasets/joelito/german_argument_mining) |
| Greek Legal Code | Topic Classification | (volume, chapter, subject) | [greek_legal_code](https://huggingface.co/datasets/greek_legal_code) |
| Online Terms of Service | Unfairness Classification | (unfairness level, clause topic) | [online_terms_of_service](https://huggingface.co/datasets/joelito/online_terms_of_service) |
| Covid 19 Emergency Event | Event Classification | default | [covid19_emergency_event](https://huggingface.co/datasets/joelito/covid19_emergency_event) |
| MultiEURLEX | Topic Classification | (level 1, level 2, level 3) | [multi_eurlex](https://huggingface.co/datasets/multi_eurlex) |
| LeNER BR | Named Entity Recognition | default | [lener_br](https://huggingface.co/datasets/lener_br) |
| LegalNERo | Named Entity Recognition | default | [legalnero](https://huggingface.co/datasets/joelito/legalnero) |
| Greek Legal NER | Named Entity Recognition | default | [greek_legal_ner](https://huggingface.co/datasets/joelito/greek_legal_ner) |
| MAPA | Named Entity Recognition | (coarse, fine) | [mapa](https://huggingface.co/datasets/joelito/mapa) |
### Languages
The following languages are supported: bg , cs , da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present for each configuration (train, validation and test).
### 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
How can I contribute a dataset to lextreme?
Please follow the following steps:
1. Make sure your dataset is available on the huggingface hub and has a train, validation and test split.
2. Create a pull request to the lextreme repository by adding the following to the lextreme.py file:
- Create a dict _{YOUR_DATASET_NAME} (similar to _BRAZILIAN_COURT_DECISIONS_JUDGMENT) containing all the necessary information about your dataset (task_type, input_col, label_col, etc.)
- Add your dataset to the BUILDER_CONFIGS list: `LextremeConfig(name="{your_dataset_name}", **_{YOUR_DATASET_NAME})`
- Test that it works correctly by loading your subset with `load_dataset("lextreme", "{your_dataset_name}")` and inspecting a few examples.
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{niklaus2023lextreme,
title={LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain},
author={Joel Niklaus and Veton Matoshi and Pooja Rani and Andrea Galassi and Matthias Stürmer and Ilias Chalkidis},
year={2023},
eprint={2301.13126},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
nlp-guild/intent-recognition-biomedical | 2022-09-22T16:13:44.000Z | [
"license:mit",
"region:us"
] | nlp-guild | null | null | null | 0 | 73 | ---
license: mit
---
[source](https://github.com/wangle1218/KBQA-for-Diagnosis/tree/main/nlu/bert_intent_recognition/data) |
loubnabnl/humaneval_infilling | 2022-10-21T10:37:13.000Z | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"arxiv:2207.14255",
"region:us"
] | loubnabnl | An evaluation benchamrk for infilling tasks on HumanEval dataset for code generation. | @article{bavarian2022efficient,
title={Efficient Training of Language Models to Fill in the Middle},
author={Bavarian, Mohammad and Jun, Heewoo and Tezak, Nikolas and Schulman, John and McLeavey, Christine and Tworek, Jerry and Chen, Mark},
journal={arXiv preprint arXiv:2207.14255},
year={2022}
} | null | 0 | 73 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: OpenAI HumanEval-Infilling
tags:
- code-generation
---
# HumanEval-Infilling
## Dataset Description
- **Repository:** https://github.com/openai/human-eval-infilling
- **Paper:** https://arxiv.org/pdf/2207.14255
## Dataset Summary
[HumanEval-Infilling](https://github.com/openai/human-eval-infilling) is a benchmark for infilling tasks, derived from [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark for the evaluation of code generation models.
## Dataset Structure
To load the dataset you need to specify a subset. By default `HumanEval-SingleLineInfilling` is loaded.
```python
from datasets import load_dataset
ds = load_dataset("humaneval_infilling", "HumanEval-RandomSpanInfilling")
DatasetDict({
test: Dataset({
features: ['task_id', 'entry_point', 'prompt', 'suffix', 'canonical_solution', 'test'],
num_rows: 1640
})
})
```
## Subsets
This dataset has 4 subsets: HumanEval-MultiLineInfilling, HumanEval-SingleLineInfilling, HumanEval-RandomSpanInfilling, HumanEval-RandomSpanInfillingLight.
The single-line, multi-line, random span infilling and its light version have 1033, 5815, 1640 and 164 tasks, respectively.
## Citation
```
@article{bavarian2022efficient,
title={Efficient Training of Language Models to Fill in the Middle},
author={Bavarian, Mohammad and Jun, Heewoo and Tezak, Nikolas and Schulman, John and McLeavey, Christine and Tworek, Jerry and Chen, Mark},
journal={arXiv preprint arXiv:2207.14255},
year={2022}
}
``` |
Multimodal-Fatima/COCO_captions_validation | 2023-03-17T21:22:46.000Z | [
"region:us"
] | Multimodal-Fatima | null | null | null | 0 | 73 | ---
dataset_info:
features:
- name: image
dtype: image
- name: filepath
dtype: string
- name: sentids
list: int32
- name: filename
dtype: string
- name: imgid
dtype: int32
- name: split
dtype: string
- name: sentences_tokens
list:
list: string
- name: sentences_raw
list: string
- name: sentences_sentid
list: int32
- name: cocoid
dtype: int32
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B
sequence: string
- name: blip_caption_beam_5
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_LAION-ViT-H-14-2B
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random
list:
- name: attribute
dtype: string
- name: box
sequence: float64
- name: captions_module
sequence: string
- name: captions_module_filter
sequence: string
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float64
- name: size
dtype: string
- name: tag
dtype: string
splits:
- name: validation
num_bytes: 1047402581.0
num_examples: 5000
download_size: 915680119
dataset_size: 1047402581.0
---
# Dataset Card for "COCO_captions_validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mstz/hill | 2023-04-16T17:31:39.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"hill",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_hill-valley_166,
author = {Graham,Lee & Oppacher,Franz},
title = {{Hill-Valley}},
year = {2008},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5JC8P}}
} | null | 1 | 73 | ---
language:
- en
tags:
- hill
- tabular_classification
- binary_classification
- UCI
pretty_name: Hill
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- hill
license: cc
---
# Hill
The [Hill dataset](https://archive.ics.uci.edu/ml/datasets/Hill) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Do the plotted coordinates draw a hill?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------------------------|
| hill | Binary classification | Do the plotted coordinates draw a hill? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/hill")["train"]
```
# Features
Features are the coordinates of the drawn point. Feature `X{i}` is the `y` coordinate of the point `(i, X{i})`. |
mstz/musk | 2023-04-16T17:34:46.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"musk",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_musk_(version_1)_74,
author = {Chapman,David & Jain,Ajay},
title = {{Musk (Version 1)}},
year = {1994},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5ZK5B}}
} | null | 0 | 73 | ---
language:
- en
tags:
- musk
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Musk
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- musk
license: cc
---
# Musk
The [Musk dataset](https://archive.ics.uci.edu/ml/datasets/Musk) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|------------------------|
| musk | Binary classification | Is the molecule a musk?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/musk", "musk")["train"]
``` |
dominguesm/Canarim-Instruct-PTBR-Dataset | 2023-08-14T10:27:07.000Z | [
"language:pt",
"license:cc-by-4.0",
"doi:10.57967/hf/0983",
"region:us"
] | dominguesm | null | null | null | 12 | 73 | ---
language: pt
license: cc-by-4.0
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 113100060
num_examples: 316413
- name: test
num_bytes: 687328
num_examples: 1519
download_size: 63510092
dataset_size: 113787388
---
## 🐥 🇧🇷 Canarim Instruct Dataset
<p align="center">
<img width="250" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/Canarim-Instruct-PTBR/main/assets/canarim.png">
</p>
<p align="center">
<a href="https://github.com/DominguesM/Canarim-Instruct-PTBR">[🐱 Github]</a>
</p>
<hr>
## What's Canarim?
Canarim is a dataset with over 300,000 instructions in Portuguese, ranging from simple instructions like "Descreva os efeitos do aquecimento global" to more complex instructions like "Nesta tarefa, você precisa ser capaz de resumir uma determinada lista de pontos-chave" where additional context is provided.
## Why it's called Canarim?
Canarim is a dataset with over 300,000 instructions in Portuguese, ranging from simple instructions like "Canarim (is pronounced: kɑnɑrɪm) or canary is a bird very present in Brazilian daily life, living for up to 30 years. Every Brazilian at some point in their life has come across this bird, which is why I chose this name for my project.
"Canarim" is spoken in some regions of Brazil (mainly by grandparents), and it could be translated as "canarinho," which means "little canary" in English.
## Source Data
This dataset was created through translation and adaptation from the following sources:
* [**dominguesm/alpaca-data-pt-br**](https://huggingface.co/datasets/dominguesm/alpaca-data-pt-br) (*51759 rows*)
* [**cahya/instructions-pt**](https://huggingface.co/datasets/cahya/instructions-pt) (*57692 rows*)
* [**HuggingFaceH4/self_instruct**](https://huggingface.co/datasets/HuggingFaceH4/self_instruct) (*74350 rows*)
* [**HuggingFaceH4/helpful_instructions (self_instruct)**](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions/viewer/self_instruct/train) (*82612 rows*)
* [**HuggingFaceH4/helpful_instructions (super_natural_instructions)**](https://huggingface.co/datasets/HuggingFaceH4/helpful_instructions/viewer/super_natural_instructions/train) (*50000 rows*)
Totaling 316k lines in the training split and 1519 lines in the test split, all lines must contain an "instruction" and an "output", and may or may not also contain a "context".
There may be translation errors in the data.
## Prompt
Below is the prompt template I am using with this dataset:
```python
def generate_prompt(instruction, input=None):
if input:
return f"""Abaixo está uma instrução que descreve uma tarefa, emparelhada com uma entrada que fornece mais contexto. Escreva uma resposta que conclua adequadamente a solicitação.
### Instruções:
{instruction}
### Entrada:
{input}
### Resposta:"""
else:
return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que conclua adequadamente a solicitação.
### Instruções:
{instruction}
### Resposta:"""
```
## Examples of prompts
```json
{
"instruction": "Sugira uma estratégia de marketing eficaz para um programa de TV.",
"input": "",
"output": "Uma estratégia de marketing eficaz para um programa de TV é criar um trailer de teaser a ser lançado antes da estréia do programa na televisão e outras plataformas digitais. O trailer de teaser deve ser curto, dinâmico e atraente, a fim de desencadear a curiosidade e o interesse nos espectadores. Ele também deve conter uma linha de tags convincente ou "gancho" que resume a premissa do programa. Além disso, criar uma hashtag associada ao programa de conteúdo da TV e pedir aos espectadores que se juntem à conversa nas mídias sociais."
}
```
```json
{
"instruction": "Compare os dois assuntos a seguir",
"input": "Inteligência Artificial e Machine Learning",
"output": "Inteligência Artificial (IA) e Machine Learning (ML) são campos da ciência da computação que envolvem o desenvolvimento de máquinas inteligentes que podem aprender com dados e executar tarefas de forma autônoma. A IA se concentra no comportamento semelhante ao humano e envolve o desenvolvimento de algoritmos e estruturas de dados que permitem que as máquinas aprendam através do reconhecimento de padrões e do raciocínio lógico. A ML, por outro lado, é um subconjunto de IA que se concentra em ensinar computadores a otimizar tarefas com um certo grau de precisão."
}
```
## Citation
If you want to cite **Canarim Instruct PTBR dataset**, you could use this:
```
@misc {maicon_domingues_2023,
author = { {Maicon Domingues} },
title = { Canarim-Instruct-PTBR-Dataset (Revision c2de751) },
year = 2023,
url = { https://huggingface.co/datasets/dominguesm/Canarim-Instruct-PTBR-Dataset },
doi = { 10.57967/hf/0983 },
publisher = { Hugging Face }
}
``` |
mstz/wall_following | 2023-04-16T18:03:59.000Z | [
"task_categories:tabular-classification",
"size_categories:1K<n<5K",
"language:en",
"license:cc",
"wall_following",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_wall-following_robot_navigation_data_194,
author = {Freire,Ananda, Veloso,Marcus & Barreto,Guilherme},
title = {{Wall-Following Robot Navigation Data}},
year = {2010},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C57C8W}}
} | null | 0 | 73 | ---
language:
- en
tags:
- wall_following
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: WallFollowing
size_categories:
- 1K<n<5K
task_categories:
- tabular-classification
configs:
- wall_following
license: cc
---
# WallFollowing
The [WallFollowing dataset](https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| wall_following | Multiclass classification.| |
| wall_following_0 | Binary classification. | Is the instance of class 0? |
| wall_following_1 | Binary classification. | Is the instance of class 1? |
| wall_following_2 | Binary classification. | Is the instance of class 2? |
| wall_following_3 | Binary classification. | Is the instance of class 3? | |
taeshahn/ko-lima | 2023-06-30T09:21:43.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2305.11206",
"region:us"
] | taeshahn | A high-quality korean dataset for efficient instruction tuning. | @InProceedings{huggingface:dataset,
title = {Ko-LIMA: Korean LIMA Dataset},
author={Hahn, Taeseung},
year={2023}
} | null | 7 | 73 | ---
license: cc-by-nc-sa-4.0
---
# Dataset Card for Ko-LIMA
## Dataset Description
Ko-LIMA는 Meta에서 공개한 [LIMA: Less Is More for Alignment](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023)의 [학습 데이터](https://huggingface.co/datasets/GAIR/lima)를 한국어로 번역한 데이터셋입니다. 번역에는 [DeepL API](https://www.deepl.com/docs-api)를 활용하였고, SK(주) Tech Collaborative Lab으로부터 비용을 지원받았습니다. 전체 텍스트 중에서 code block이나 수식을 나타내는 특수문자 사이의 텍스트는 원문을 유지하는 형태로 번역을 진행하였으며, `train` 데이터셋 1,030건과 `test` 데이터셋 300건으로 구성된 총 1,330건의 데이터를 활용하실 수 있습니다. 현재 동일한 번역 문장을 `plain`, `vicuna` 두 가지 포멧으로 제공합니다.
데이터셋 관련하여 문의가 있으신 경우 [메일](mailto:taes.hahn@gmail.com)을 통해 연락주세요! 🥰
This is Korean LIMA dataset, which is translated from the [LIMA dataset](https://huggingface.co/datasets/GAIR/lima) that Meta's [LIMA model](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023) was trained on. The translation has proceeded through [DeepL API](https://www.deepl.com/docs-api) with financial support from Tech Collaborative Lab in SK Inc.
Please feel free to contact me if you have any question on the dataset.
I'm best reached via [email](mailto:taes.hahn@gmail.com).
#### Changelog
- [29 June 2023] New format added: `vicuna` format with the same translation is now available.
- [16 June 2023] Enhanced Translation: keep the text enclosed in special characters, e.g. `$` and `` ` ``, intact without translation.
- [14 June 2023] First upload.
### Usage
```python
>>> from datasets import load_dataset
>>> ko_lima = load_dataset('taeshahn/ko-lima', 'plain') # or load_dataset('taeshahn/ko-lima')
>>> ko_lima_vicuna = load_dataset('taeshahn/ko-lima', 'vicuna')
```
```python
>>> ko_lima['train'][1025]
{
'conversations': [
'저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?',
'항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.',
'인용된 자료의 출처는 무엇인가요?',
'출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
],
'source': 'multi_turn'
}
```
```python
>>> ko_lima_vicuna['train'][1025]
{
'id': '1026-multi_turn',
'conversations': [
{
'from': '사람',
'value': '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?'
},
{
'from': '어시스턴트',
'value': '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.'
},
{
'from': '사람',
'value': '인용된 자료의 출처는 무엇인가요?'
},
{
'from': '어시스턴트',
'value': '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.'
}
]
}
```
### Citation Information
```
@InProceedings{kolimadataset,
title = {Ko-LIMA: Korean LIMA Dataset for Efficient Instruction-tuning},
author = {Hahn, Taeseung},
year = {2023}
}
```
|
xiyuez/im-feeling-curious | 2023-06-24T17:09:22.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:odc-by",
"region:us"
] | xiyuez | null | null | null | 2 | 73 | ---
license: odc-by
task_categories:
- question-answering
- text-generation
language:
- en
pretty_name: i'm feeling curious dataset
size_categories:
- 1K<n<10K
---
This public dataset is an extract from Google's "i'm feeling curious" feature. To learn more about this feature, search for "i'm feeling curious" on Google.
Tasks: Answering open-domain questions, generating random facts.
Limitations: May contain commercial content, false information, bias, or outdated information.
Language: English only.
This public extract is licensed under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/.
There is no canonical train/test split.
This extract contains 2761 unique rows, which may increase as more data is crawled. Near-duplicates have been removed.
While we aimed to filter non-natural language content and duplicates, some may remain. The data may also contain toxic, biased, copyrighted or erroneous content. Google has done initial filtering, but we have not verified the data.
Use this dataset at your own risk. We provide no warranty or liability.
Google is a registered trademark of Google LLC. This project is not affiliated with, endorsed or sponsored by Google.
|
TJUNLP/M3KE | 2023-06-19T04:07:29.000Z | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:multiple-choice",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"arxiv:2305.10263",
"region:us"
] | TJUNLP | A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models. | @misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen Su and Qun Liu and Deyi Xiong},
year={2023},
eprint={2305.10263},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 2 | 73 | ---
license: apache-2.0
task_categories:
- text-classification
- question-answering
- multiple-choice
language:
- zh
size_categories:
- 10K<n<100K
arxiv:
- 2305.10263
---
M3KE, or Massive Multi-Level Multi-Subject Knowledge Evaluation, is a benchmark developed to assess the knowledge acquired by large Chinese language models by evaluating their multitask accuracy in both zero- and few-shot settings. The benchmark comprises 20,477 questions spanning 71 tasks. For further information about M3KE, please consult our [paper](https://arxiv.org/abs/2305.10263) or visit our [GitHub](https://github.com/tjunlp-lab/M3KE) page.
## Load the data
```python
from datasets import load_dataset
ds = load_dataset(
path="TJUNLP/M3KE",
name="Computer Programming Language-Natural Sciences-Other"
)
print(ds)
"""
DatasetDict({
test: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 236
})
dev: Dataset({
features: ['id', 'question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 5
})
})
"""
print(ds["test"][0])
"""
{'id': 0, 'question': '下面判断正确的是？', 'A': 'char str[10]={"china"}; 等价于 char str[10];str[]="china";', 'B': 'char *s="china"; 等价于 char *s;s="china"; ', 'C': 'char *a="china"; 等价于 char *a;*a="china";', 'D': 'char c[6]="china",d[6]="china"; 等 价 于 char c[6]=d[6]="china"; ', 'answer': ''}
"""
```
```
@misc{liu2023m3ke,
title={M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models},
author={Chuang Liu and Renren Jin and Yuqi Ren and Linhao Yu and Tianyu Dong and Xiaohan Peng and Shuting Zhang and Jianxiang Peng and Peiyi Zhang and Qingqing Lyu and Xiaowen Su and Qun Liu and Deyi Xiong},
year={2023},
eprint={2305.10263},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
TokenBender/code_instructions_122k_alpaca_style | 2023-07-20T19:59:33.000Z | [
"license:apache-2.0",
"region:us"
] | TokenBender | null | null | null | 22 | 73 | ---
license: apache-2.0
---
|
lamini/spider_text_to_sql | 2023-08-28T06:57:19.000Z | [
"region:us"
] | lamini | null | null | null | 5 | 73 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 9388343
num_examples: 7000
- name: validation
num_bytes: 1090039
num_examples: 1034
download_size: 1054303
dataset_size: 10478382
---
# Dataset Card for "spider_text_to_sql"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PurCL/malware-top-100 | 2023-08-31T21:13:38.000Z | [
"region:us"
] | PurCL | null | null | null | 0 | 73 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: binary_name
dtype: string
- name: labels
sequence: string
- name: functions
dtype: string
splits:
- name: train
num_bytes: 5667834326.115244
num_examples: 3728
- name: test
num_bytes: 1667814982.765135
num_examples: 1097
- name: valid
num_bytes: 1001905263.1196207
num_examples: 659
download_size: 2454551882
dataset_size: 8337554571.999999
---
# Dataset Card for "malware-top-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dongyoung4091/shp_with_features_20k_flan_t5_large_flan_t5_zeroshot | 2023-09-08T15:45:01.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 73 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: post_id
dtype: string
- name: domain
dtype: string
- name: upvote_ratio
dtype: float64
- name: history
dtype: string
- name: c_root_id_A
dtype: string
- name: c_root_id_B
dtype: string
- name: created_at_utc_A
dtype: int64
- name: created_at_utc_B
dtype: int64
- name: score_A
dtype: int64
- name: score_B
dtype: int64
- name: human_ref_A
dtype: string
- name: human_ref_B
dtype: string
- name: labels
dtype: int64
- name: seconds_difference
dtype: float64
- name: score_ratio
dtype: float64
- name: helpfulness_A
dtype: float64
- name: helpfulness_B
dtype: float64
- name: specificity_A
dtype: float64
- name: specificity_B
dtype: float64
- name: intent_A
dtype: float64
- name: intent_B
dtype: float64
- name: factuality_A
dtype: float64
- name: factuality_B
dtype: float64
- name: easy-to-understand_A
dtype: float64
- name: easy-to-understand_B
dtype: float64
- name: relevance_A
dtype: float64
- name: relevance_B
dtype: float64
- name: readability_A
dtype: float64
- name: readability_B
dtype: float64
- name: enough-detail_A
dtype: float64
- name: enough-detail_B
dtype: float64
- name: biased:_A
dtype: float64
- name: biased:_B
dtype: float64
- name: fail-to-consider-individual-preferences_A
dtype: float64
- name: fail-to-consider-individual-preferences_B
dtype: float64
- name: repetetive_A
dtype: float64
- name: repetetive_B
dtype: float64
- name: fail-to-consider-context_A
dtype: float64
- name: fail-to-consider-context_B
dtype: float64
- name: too-long_A
dtype: float64
- name: too-long_B
dtype: float64
- name: __index_level_0__
dtype: int64
- name: log_score_A
dtype: float64
- name: log_score_B
dtype: float64
- name: zeroshot_helpfulness_A
dtype: int64
- name: zeroshot_helpfulness_B
dtype: int64
- name: zeroshot_specificity_A
dtype: int64
- name: zeroshot_specificity_B
dtype: int64
- name: zeroshot_intent_A
dtype: int64
- name: zeroshot_intent_B
dtype: int64
- name: zeroshot_factuality_A
dtype: int64
- name: zeroshot_factuality_B
dtype: int64
- name: zeroshot_easy-to-understand_A
dtype: int64
- name: zeroshot_easy-to-understand_B
dtype: int64
- name: zeroshot_relevance_A
dtype: int64
- name: zeroshot_relevance_B
dtype: int64
- name: zeroshot_readability_A
dtype: int64
- name: zeroshot_readability_B
dtype: int64
- name: zeroshot_enough-detail_A
dtype: int64
- name: zeroshot_enough-detail_B
dtype: int64
- name: zeroshot_biased:_A
dtype: int64
- name: zeroshot_biased:_B
dtype: int64
- name: zeroshot_fail-to-consider-individual-preferences_A
dtype: int64
- name: zeroshot_fail-to-consider-individual-preferences_B
dtype: int64
- name: zeroshot_repetetive_A
dtype: int64
- name: zeroshot_repetetive_B
dtype: int64
- name: zeroshot_fail-to-consider-context_A
dtype: int64
- name: zeroshot_fail-to-consider-context_B
dtype: int64
- name: zeroshot_too-long_A
dtype: int64
- name: zeroshot_too-long_B
dtype: int64
splits:
- name: train
num_bytes: 22674534
num_examples: 9459
- name: test
num_bytes: 22627412
num_examples: 9459
download_size: 12130286
dataset_size: 45301946
---
# Dataset Card for "shp_with_features_20k_flan_t5_large_flan_t5_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benhachem/KHATT | 2023-09-12T13:05:06.000Z | [
"task_categories:image-to-text",
"size_categories:1K<n<10K",
"language:ar",
"OCR",
"Optical Character Recognition ",
"Arabic OCR",
"arabic ",
"ocr",
"Textline images",
"region:us"
] | benhachem | KHATT (KFUPM Handwritten Arabic TexT) database is a database of unconstrained handwritten Arabic Text written by 1000 different writers. This research database’s development was undertaken by a research group from KFUPM, Dhahran, S audi Arabia headed by Professor Sabri Mahmoud in collaboration with Professor Fink from TU-Dortmund, Germany and Dr. Märgner from TU-Braunschweig, Germany. | @article{Pattern Recognition,
Author = {bri A. Mahmoud, Irfan Ahmad, Wasfi G. Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Märgner, Gernot A. Fink},
Title = { {KHATT: An Open Arabic Offline Handwritten Text Database} },
Year = {2013},
doi = {10.1016/j.patcog.2013.08.009},
} | null | 0 | 73 | ---
task_categories:
- image-to-text
language:
- ar
tags:
- OCR
- 'Optical Character Recognition '
- Arabic OCR
- 'arabic '
- ocr
- Textline images
size_categories:
- 1K<n<10K
---
# KFUPM Handwritten Arabic TexT (KHATT) database
### Version 1.0 (September 2012 Release)
The database contains handwritten Arabic text images and its ground-truth developed for
research in the area of Arabic handwritten text. It contains the line images and their ground-truth. It was used for the pilot experimentation as reported in the paper: <ins> S. A. Mahmoud, I. Ahmad, M. Alshayeb, W. G. Al-Khatib, M. T. Parvez, G. A. Fink, V. Margner, and H. EL Abed, “KHATT: Arabic Offline
Handwritten Text Database </ins>, In Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), Bari, Italy, 2012, pp. 447-452, IEEE Computer Society.
|
dongyoung4091/shp-generated_flan_t5_large_external_rm1 | 2023-09-12T09:13:50.000Z | [
"region:us"
] | dongyoung4091 | null | null | null | 0 | 73 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: external_rm1
dtype: float64
splits:
- name: train
num_bytes: 27036265
num_examples: 25600
download_size: 1847255
dataset_size: 27036265
---
# Dataset Card for "shp-generated_flan_t5_large_external_rm1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vitaliy-sharandin/pollution-by-region | 2023-09-20T16:05:17.000Z | [
"region:us"
] | vitaliy-sharandin | null | null | null | 0 | 73 | ---
dataset_info:
features:
- name: Entity
dtype: string
- name: Code
dtype: string
- name: Annual CO₂ emissions
dtype: float64
- name: Year
dtype: timestamp[ns, tz=UTC]
- name: dt
dtype: timestamp[ns, tz=UTC]
splits:
- name: train
num_bytes: 1409806
num_examples: 31349
download_size: 395907
dataset_size: 1409806
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pollution-by-region"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ticoAg/zhihu_3k_rlhf_train | 2023-09-21T09:53:46.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:zh",
"license:apache-2.0",
"region:us"
] | ticoAg | null | null | null | 0 | 73 | ---
license: apache-2.0
task_categories:
- question-answering
language:
- zh
size_categories:
- 1K<n<10K
---
# Note
> some rm data from public dataset
- format
```json
{
"history": [
"query1", "answer1",
"query2", "answer2"
],
"prompt": "query",
"input": "input for query",
"output": [
"output rank1",
"output rank2",
"output rank3"
]
}
```
Thanks
- [beyond/rlhf-reward-single-round-trans_chinese](https://huggingface.co/datasets/beyond/rlhf-reward-single-round-trans_chinese) :
- [dikw/hh_rlhf_cn](https://huggingface.co/datasets/dikw/hh_rlhf_cn)
- [liyucheng/zhihu_rlhf_3k](https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k) |
ShrinivasSK/small-hi-kn | 2023-09-30T17:19:03.000Z | [
"region:us"
] | ShrinivasSK | null | null | null | 0 | 73 | Entry not found |
alexandrainst/nst-da | 2023-10-05T14:27:00.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"size_categories:100K<n<1M",
"language:da",
"license:cc0-1.0",
"region:us"
] | alexandrainst | null | null | null | 0 | 73 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: age
dtype: int64
- name: sex
dtype: string
- name: dialect
dtype: string
- name: recording_datetime
dtype: string
splits:
- name: train
num_bytes: 55199435558.0
num_examples: 182605
- name: test
num_bytes: 8894080220.0
num_examples: 54747
download_size: 5358057252
dataset_size: 64093515778.0
size_categories:
- 100K<n<1M
license: cc0-1.0
task_categories:
- automatic-speech-recognition
- text-to-speech
language:
- da
pretty_name: NST-da
---
# Dataset Card for NST-da
## Dataset Description
- **Repository:** <https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/>
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:dan.nielsen@alexandra.dk)
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
### Dataset Summary
This dataset is an upload of the [NST Danish ASR Database (16 kHz) – reorganized](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-55/).
The training and test splits are the original ones.
### Supported Tasks and Leaderboards
Training automatic speech recognition is the intended task for this dataset. No leaderboard is active at this point.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 5.36 GB
- **Size of the generated dataset:** 64.09 GB
- **Total amount of disk used:** 69.45 GB
An example from the dataset looks as follows.
```
{
'audio': {
'path': 'dk14x404-05072000-1531_u0008121.wav',
'array': array([ 0.00265503, 0.00248718, 0.00253296, ..., -0.00030518,
-0.00035095, -0.00064087]),
'sampling_rate': 16000
},
'text': 'Desuden er der en svømmeprøve, en fremmedsprogstest samt en afsluttende samtale.',
'speaker_id': 404,
'age': 24,
'sex': 'Female',
'dialect': 'Storkøbenhavn',
'recording_datetime': '2000-07-05T15:31:14'
}
```
### Data Fields
The data fields are the same among all splits.
- `audio`: an `Audio` feature.
- `text`: a `string` feature.
- `speaker_id`: an `int64` feature.
- `age`: an `int64` feature.
- `sex`: a `string` feature.
- `dialect`: a `string` feature.
- `recording_datetime`: a `string` feature.
### Dataset Statistics
There are 183,205 samples in the training split, and 54,747 samples in the test split.
#### Speakers
There are 539 unique speakers in the training dataset and 56 unique speakers in the test dataset, where 54 of them are also present in the training set.
#### Age Distribution
#### Dialect Distribution
#### Sex Distribution
#### Transcription Length Distribution
## Dataset Creation
### Curation Rationale
There are not many large-scale ASR datasets in Danish.
### Source Data
The data originates from the now bankrupt company Nordisk språkteknologi (NST), whose data was transferred to the National Library of Norway, who subsequently released it into the public domain.
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) reorganised the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/). |
pk11/longmult | 2023-10-05T22:03:07.000Z | [
"license:cc-by-4.0",
"region:us"
] | pk11 | null | null | null | 0 | 73 | ---
license: cc-by-4.0
dataset_info:
config_name: longmult
features:
- name: text
dtype: string
- name: split
dtype:
class_label:
names:
'0': train
'1': test
splits:
- name: train
num_bytes: 413269584
num_examples: 434267
- name: test
num_bytes: 0
num_examples: 0
download_size: 425048713
dataset_size: 413269584
---
|
stepkurniawan/qa_sustainability_wiki | 2023-10-05T20:51:30.000Z | [
"license:mit",
"region:us"
] | stepkurniawan | null | null | null | 0 | 73 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: ground_truths
dtype: string
splits:
- name: train
num_bytes: 195625.12855377008
num_examples: 647
- name: test
num_bytes: 48981.87144622991
num_examples: 162
download_size: 149066
dataset_size: 244607.0
---
The purpose of this dataset is to have a question - answer (ground truth) in a table format. The question and answer are all created by using langchain x gpt-4
since it will take a long time for me to create it manually. However, as a due diligent, I have checked randomly more than 50% of the questions and answers,
and judged that it is safe to use.
The source of this questions and answer is from a private wiki page called Sustainable Methods Wiki, created by Prof. Henrik .v. Wahrden.
Link: https://sustainabilitymethods.org/index.php/Main_Page
|
allegro_reviews | 2022-11-18T17:41:41.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"region:us"
] | null | Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted
from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale
from one (negative review) to five (positive review).
We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden.
You can evaluate your model using the online evaluation tool available on klejbenchmark.com. | @inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
pages = "1191--1201",
} | null | 1 | 72 | ---
annotations_creators:
- found
language_creators:
- found
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-scoring
- text-scoring
paperswithcode_id: allegro-reviews
pretty_name: Allegro Reviews
dataset_info:
features:
- name: text
dtype: string
- name: rating
dtype: float32
splits:
- name: train
num_bytes: 4899539
num_examples: 9577
- name: test
num_bytes: 514527
num_examples: 1006
- name: validation
num_bytes: 515785
num_examples: 1002
download_size: 2314847
dataset_size: 5929851
---
# Dataset Card for [Dataset Name]
## 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://klejbenchmark.com/
- **Repository:**
https://github.com/allegro/klejbenchmark-allegroreviews
- **Paper:**
KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz)
- **Leaderboard:**
https://klejbenchmark.com/leaderboard/
- **Point of Contact:**
klejbenchmark@allegro.pl
### Dataset Summary
Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).
We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com.
### Supported Tasks and Leaderboards
Product reviews sentiment analysis.
https://klejbenchmark.com/leaderboard/
### Languages
Polish
## Dataset Structure
### Data Instances
Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text).
### Data Fields
- text: a product review of at least 50 words
- rating: product rating of a scale of one (negative review) to five (positive review)
### Data Splits
Data is splitted in train/dev/test split.
## Dataset Creation
### Curation Rationale
This dataset is one of nine evaluation tasks to improve polish language processing.
### Source Data
#### Initial Data Collection and Normalization
The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl).
#### Who are the source language producers?
Customers of an e-commerce marketplace.
### 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
Allegro Machine Learning Research team klejbenchmark@allegro.pl
### Licensing Information
Dataset licensed under CC BY-SA 4.0
### Citation Information
@inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
pages = "1191--1201",
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. |
allegro/summarization-polish-summaries-corpus | 2021-09-24T14:17:27.000Z | [
"region:us"
] | allegro | null | null | null | 2 | 72 | Entry not found |
mstz/house16 | 2023-04-16T17:31:52.000Z | [
"task_categories:tabular-classification",
"size_categories:10k<n<100K",
"language:en",
"license:cc",
"house16",
"tabular_classification",
"binary_classification",
"region:us"
] | mstz | null | null | null | 0 | 72 | ---
language:
- en
tags:
- house16
- tabular_classification
- binary_classification
pretty_name: House16
size_categories:
- 10k<n<100K
task_categories:
- tabular-classification
configs:
- house16
license: cc
---
# House16
The [House16 dataset](https://www.openml.org/search?type=data&sort=runs&id=821&status=active) from the [OpenML repository](https://www.openml.org/).
# Configurations and tasks
| **Configuration** | **Task** |
|-------------------|---------------------------|
| house16 | Binary classification |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/house16", "house16")["train"]
``` |
BAAI/COIG | 2023-07-12T15:38:35.000Z | [
"language:zh",
"license:apache-2.0",
"arxiv:2204.07705",
"arxiv:2212.10560",
"arxiv:2212.09689",
"arxiv:2304.07987",
"region:us"
] | BAAI | We propose the Chinese Open Instruction Generalist (COIG) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively. | @misc{zhang2023chinese,
title={Chinese Open Instruction Generalist: A Preliminary Release},
author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu},
year={2023},
eprint={2304.07987},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 316 | 72 | ---
license: apache-2.0
arxiv: 2304.07987
language:
- zh
---
# This is the Chinese Open Instruction Generalist project
We propose the Chinese Open Instruction Generalist (**COIG**) project to maintain a harmless, helpful, and diverse set of Chinese instruction corpora. We welcome all researchers in the community to contribute to the corpus set and collaborate with us. We only release the first chip of COIG to help the Chinese LLMs' development in the exploration stage and appeal to more researchers joining us in building COIG. We introduce a manually verified translated general instruction corpus, a manually annotated exam instruction corpus, a human value alignment instruction corpus, a multi-round counterfactual correction chat corpus, and a leetcode instruction corpus. We provide these new instruction corpora to assist the community with instruction tuning on Chinese LLMs. These instruction corpora are also template workflows for how new Chinese instruction corpora can be built and expanded effectively.
It is best to download the individual data files directly that you wish to use instead of using HF load_datasets. All datasets can be downloaded from: https://huggingface.co/datasets/BAAI/COIG/tree/main
This dataset card is modified from [OIG](https://huggingface.co/datasets/laion/OIG).
### Translated Instructions (66,858)
There are 66,858 instructions in total, which are composed of 1,616 task descriptions in [Super-NaturalInstructions](https://arxiv.org/abs/2204.07705) along with a single instance for each of them, 175 seed tasks in [Self-Instruct](https://arxiv.org/abs/2212.10560), and 66,007 instructions from [Unnatural Instructions](https://arxiv.org/abs/2212.09689). To reduce the cost and further improve the quality of the instruction corpus, we separate the translation procedure into three phases: automatic translation, manual verification, and manual correction. These strict quality verification procedures assure the reliability of the translated corpus.
### Exam Instructions (63,532)
The Chinese National College Entrance Examination, Middle School Entrance Examinations, and Civil Servant Examination are the main Chinese commonsense tests. These exams contain various question formats and detailed analysis that can be used as the Chain-of-Thought (**CoT**) corpus. We extract six informative elements from original exam questions, including instruction, question context, question, answer, answer analysis, and coarse-grained subject. There are six main coarse-grained subjects: Chinese, English, Politics, Biology, History, and Geology. There are very few Math, Physics, and Chemistry questions in the corpus because these questions are often with complex symbols which are hard to annotate. For many choice questions, we recommend that the researchers utilize this corpus to further post-process it using prompts or post-process it to blank-filling questions to increase the instructions' diversity further.
### Human Value Alignment Instructions (34,471)
To respect and reflect the major difference caused by different cultural backgrounds, different from other tasks in COIG that leverage one unified collection of instruction-following samples, we categorize the value alignment data into two separate series:
- A set of samples that present shared human values in the Chinese-speaking world. In total, we choose 50 instructions as the augmentation seeds, and produce 3k resulting instructions following samples for general-purpose value alignment in the Chinese-speaking world.
- Some additional sets of samples that present regional-culture or country-specific human values.
### Counterfactural Correction Multi-round Chat (13,653)
The Counterfactual Correction Multi-round Chat dataset (CCMC) is constructed based on the [CN-DBpedia knowledge graph dataset](https://link.springer.com/chapter/10.1007/978-3-319-60045-1_44) with the aim of alleviating and resolving the pain points of hallucination and factual inconsistency in current LLMs. The CCMC dataset includes 5 rounds of role-playing chat between a student and a teacher, and the corresponding knowledge they refer to. The dataset contains ~13,000 dialogues with an average of 5 rounds per dialogue, resulting in ~65,000 rounds of chat.
### Leetcode Instructions (11,737)
Given that the code-related tasks potentially contribute to the ability emergence of LLMs, we argue that code-related tasks aligned with the Chinese natural language should be considered in our datasets. Therefore, we build the Leetcode instructions from a **CC-BY-SA-4.0** license [collection](https://github.com/doocs/leetcode) of 2,589 programming questions. The questions contain problem descriptions, multiple programming languages, and explanations (834 questions do not have explanations).
## Support this project
Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:
Submit Github issues, track issues and help create datasets that need improvement. https://github.com/BAAI-Zlab/COIG
## Update: May 27, 2023
- v0.3: Update counterfactural_correction_multi_round_chat.tar.gz and make sure all round responses can be decoded as json.
- v0.2: Update exam_instructions.jsonl, translated_instructions.jsonl and human_value_alignment_instructions_part2.json.
- v0.1: Release the five datasets of COIG.
## Disclaimer
These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.
## License
The COIG dataset that is authored by BAAI is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as unnatural instructions data which is licensed under MIT License, or web-crawled data which is used under fair use principles.
## BibTeX & Citation
```
@misc{zhang2023chinese,
title={Chinese Open Instruction Generalist: A Preliminary Release},
author={Ge Zhang and Yemin Shi and Ruibo Liu and Ruibin Yuan and Yizhi Li and Siwei Dong and Yu Shu and Zhaoqun Li and Zekun Wang and Chenghua Lin and Wenhao Huang and Jie Fu},
year={2023},
eprint={2304.07987},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
lca0503/GPTspeech_encodec_v2 | 2023-06-15T06:54:51.000Z | [
"region:us"
] | lca0503 | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: file_id
dtype: string
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
sequence: int64
- name: src_encodec_2
sequence: int64
- name: src_encodec_3
sequence: int64
- name: src_encodec_4
sequence: int64
- name: src_encodec_5
sequence: int64
- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
sequence: int64
- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
sequence: int64
- name: tgt_encodec_3
sequence: int64
- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
sequence: int64
splits:
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num_examples: 704563
- name: validation
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num_examples: 12855
- name: test
num_bytes: 700741253
num_examples: 12463
download_size: 4503561741
dataset_size: 44139741479
---
# Dataset Card for "GPTspeech_encodec_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/v1.1_id0.2_context_instruction_tuning | 2023-08-16T11:38:01.000Z | [
"region:us"
] | tyzhu | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
- name: context
dtype: string
- name: template_used
dtype: string
splits:
- name: train
num_bytes: 1154915040.1878934
num_examples: 437288
- name: eval_context
num_bytes: 38006832.85245361
num_examples: 13944
- name: eval_id_context
num_bytes: 10843981
num_examples: 5976
download_size: 237906027
dataset_size: 1203765854.040347
---
# Dataset Card for "v1.1_id0.2_context_instruction_tuning"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nthakur/msmarco-passage-sampled-10k | 2023-08-17T14:46:16.000Z | [
"task_categories:text-retrieval",
"source_datasets:Tevatron/msmarco-passage",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | nthakur | null | null | null | 0 | 72 | ---
language:
- en
license: cc-by-sa-3.0
task_categories:
- text-retrieval
source_datasets:
- Tevatron/msmarco-passage
---
# nthakur/msmarco-passage-sampled-10k
This is a 10k randomly sampled training pairs of the Tevatron [msmarco-passage](https://huggingface.co/datasets/Tevatron/msmarco-passage) for debugging and training models on a smaller subset of MSMARCO training data.
## Citing & Authors
Have a look at [Tevatron](https://github.com/texttron/tevatron).
<!--- Describe where people can find more information --> |
Linhz/qag_vinewsqa | 2023-08-24T16:36:48.000Z | [
"region:us"
] | Linhz | null | null | null | 0 | 72 | Entry not found |
Yijia-Xiao/pii-cord19 | 2023-09-12T22:27:19.000Z | [
"region:us"
] | Yijia-Xiao | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: cleaned_output
dtype: string
splits:
- name: train
num_bytes: 1422043675
num_examples: 821007
download_size: 806733402
dataset_size: 1422043675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pii-cord19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mychen76/stack-exchange-paired-500k | 2023-09-01T23:55:09.000Z | [
"region:us"
] | mychen76 | null | null | null | 0 | 72 | StackExchange Paired 500K is a subset of lvwerra/stack-exchange-paired
which is a processed version of the HuggingFaceH4/stack-exchange-preferences. The following steps were applied:
Parse HTML to Markdown with markdownify
Create pairs (response_j, response_k) where j was rated better than k
Sample at most 10 pairs per question
Shuffle the dataset globally
This dataset is designed to be used for preference learning.
---
license: mit
---
|
ShreeyaVenneti/self_promotion_interview_transcripts | 2023-09-13T06:21:08.000Z | [
"region:us"
] | ShreeyaVenneti | null | null | null | 0 | 72 | Entry not found |
collabora/ai4bharat-shrutilipi | 2023-09-06T10:05:33.000Z | [
"region:us"
] | collabora | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 154227946446.427
num_examples: 228217
- name: validation
num_bytes: 43011087729.583
num_examples: 63411
download_size: 201153828670
dataset_size: 197239034176.01
---
# Dataset Card for "ai4bharat-shrutilipi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
codefuse-ai/CodeExercise-Python-27k | 2023-09-08T09:38:34.000Z | [
"license:cc-by-nc-sa-4.0",
"arxiv:2303.17760",
"region:us"
] | codefuse-ai | null | null | null | 29 | 72 | ---
license: cc-by-nc-sa-4.0
viewer: false
---
# Dataset Card for CodeFuse-CodeExercise-Python-27k
<div align='center'>
[[中文]](#chinese) [[English]](#english)
</div>
<a id="english"></a>
### Dataset Description
This dataset consists of 27K Python programming exercises (in English), covering hundreds of Python-related topics including basic syntax and data structures, algorithm applications, database queries, machine learning, and more.
*Please note that this dataset was generated with the help of ChatGPT (gpt-turbo-3.5) and [Camel](https://arxiv.org/abs/2303.17760), and has not undergone strict validation. There may be errors or semantic duplicates in the questions or answers, so please use it with caution.*
## Field Structure
```
{
"dataset": #Dataset name,
"id": #Sample ID,
"chat_rounds": [
{
"role": "human",
"content": #instruction,
"chat_round_id": 0
},
{
"role": "bot",
"content": #output,
"chat_round_id": 1
}]
}
```
### Examples
```
{
"data_name": "CodeExercise-Py",
"id": 27219,
"chat_rounds": [
{
"role": "human",
"content": "Write a Python program to rotate an array by a given number of steps.",
"chat_round_id": 0
},
{
"role": "bot",
"content": "Sure! Here's a Python program that rotates an array by a given number of steps.\n\n```python\ndef rotate_array(arr, steps):\n \"\"\"\n Rotate an array by the given number of steps.\n\n Parameters:\n arr (list): The input array.\n steps (int): The number of steps to rotate the array. Positive steps rotate it to the right,\n while negative steps rotate it to the left.\n\n Returns:\n list: The rotated array.\n\n Examples:\n >>> rotate_array([1, 2, 3, 4, 5], 2)\n [4, 5, 1, 2, 3]\n\n >>> rotate_array([1, 2, 3, 4, 5], -1)\n [2, 3, 4, 5, 1]\n \"\"\"\n arr_length = len(arr)\n steps = steps % arr_length # Handle steps larger than array length\n\n if steps == 0: # No rotation needed\n return arr\n\n rotated_arr = arr[-steps:] + arr[:-steps] # Split the list and concatenate in the desired order\n\n return rotated_arr\n\n\n# Example usage\narr = [1, 2, 3, 4, 5]\nsteps = 2\nrotated_arr = rotate_array(arr, steps)\nprint(rotated_arr)\n```\n\nThe `rotate_array()` function takes an input array `arr` and the number of steps `steps` to rotate the array. It first calculates the actual number of rotations by performing a modulo operation on `steps` with the length of the input array. This ensures that even if `steps` is larger than the array length, we always perform the appropriate number of rotations.\n\nIf `steps` is 0, the function returns the input array as no rotation is needed.\n\nOtherwise, the function splits the input array into two parts based on the number of steps: the last `steps` elements (using negative indexing) and the remaining elements. These two parts are then concatenated in the desired order to obtain the rotated array.\n\nFinally, the program demonstrates an example usage by rotating an array `[1, 2, 3, 4, 5]` by 2 steps (to the right) and printing the resulting rotated array.",
"chat_round_id": 1
}]
}
```
## Creation
* Step 1: Curate a seed set of Python knowledge points.
* Step 2: Embed each seed into a fixed task template to obtain a fixed template's "Task Prompt," which serves as a prompt for ChatGPT to generate exercise questions related to the given knowledge point.
* Step 3: Use Camel to refine the "Task Prompt" obtained in step 2, in order to achieve more accurate and diverse descriptions.
* Step 4: Input the obtained Task Prompt into ChatGPT to generate exercise questions (instructions) corresponding to the knowledge point.
* Step 5: For each exercise question (instruction), leverage ChatGPT to generate the corresponding answer.
* Step 6: Assemble each question with its answer and remove duplicates.
<a id="chinese"></a>
## 简介
该数据集由2.7万道Python编程练习题（英文）组成，覆盖基础语法与数据结构、算法应用、数据库查询、机器学习等数百个Python相关知识点。
注意：该数据集是借助ChatGPT (gpt-turbo-3.5)和[Camel](https://arxiv.org/abs/2303.17760)生成，未经严格校验，题目或答案可能存在错误或语义重复，使用时请注意。
## 数据字段
```
{
"dataset": #数据集名称,
"id": #样本ID,
"chat_rounds": [
{
"role": "human",
"content": #指令内容,
"chat_round_id": 0
},
{
"role": "bot",
"content": #答案内容,
"chat_round_id": 1
}]
}
```
### 样例
```
{
"data_name": "CodeExercise-Py",
"id": 27219,
"chat_rounds": [
{
"role": "human",
"content": "Write a Python program to rotate an array by a given number of steps.",
"chat_round_id": 0
},
{
"role": "bot",
"content": "Sure! Here's a Python program that rotates an array by a given number of steps.\n\n```python\ndef rotate_array(arr, steps):\n \"\"\"\n Rotate an array by the given number of steps.\n\n Parameters:\n arr (list): The input array.\n steps (int): The number of steps to rotate the array. Positive steps rotate it to the right,\n while negative steps rotate it to the left.\n\n Returns:\n list: The rotated array.\n\n Examples:\n >>> rotate_array([1, 2, 3, 4, 5], 2)\n [4, 5, 1, 2, 3]\n\n >>> rotate_array([1, 2, 3, 4, 5], -1)\n [2, 3, 4, 5, 1]\n \"\"\"\n arr_length = len(arr)\n steps = steps % arr_length # Handle steps larger than array length\n\n if steps == 0: # No rotation needed\n return arr\n\n rotated_arr = arr[-steps:] + arr[:-steps] # Split the list and concatenate in the desired order\n\n return rotated_arr\n\n\n# Example usage\narr = [1, 2, 3, 4, 5]\nsteps = 2\nrotated_arr = rotate_array(arr, steps)\nprint(rotated_arr)\n```\n\nThe `rotate_array()` function takes an input array `arr` and the number of steps `steps` to rotate the array. It first calculates the actual number of rotations by performing a modulo operation on `steps` with the length of the input array. This ensures that even if `steps` is larger than the array length, we always perform the appropriate number of rotations.\n\nIf `steps` is 0, the function returns the input array as no rotation is needed.\n\nOtherwise, the function splits the input array into two parts based on the number of steps: the last `steps` elements (using negative indexing) and the remaining elements. These two parts are then concatenated in the desired order to obtain the rotated array.\n\nFinally, the program demonstrates an example usage by rotating an array `[1, 2, 3, 4, 5]` by 2 steps (to the right) and printing the resulting rotated array.",
"chat_round_id": 1
}]
}
```
## 数据生成过程
* 第一步: 整理Python知识点，作为初始种子集
* 第二步：将每个种子嵌入到固定的任务模版中，获得固定模版的"Task Prompt"，该任务模版的主题是提示ChatGPT生成给定知识点的练习题问题。
* 第三步：调用Camel对第二步获得的"Task Prompt"进行润色，以获得更加描述准确且多样的Task Prompt
* 第四步：将获得的Task Prompt输入给ChatGPT，令其生成对应知识点的练习题问题（指令）
* 第五步：对每个练习题问题（指令），借助ChatGPT生成对应的问题答案
* 第六步：组装每个问题和其答案，并进行去重操作 |
maximegmd/medmcqa_alpaca_format | 2023-09-12T11:29:11.000Z | [
"region:us"
] | maximegmd | null | null | null | 0 | 72 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 120644997
num_examples: 182822
- name: test
num_bytes: 1077057
num_examples: 6150
- name: validation
num_bytes: 2009220
num_examples: 4183
download_size: 79503290
dataset_size: 123731274
---
# Dataset Card for "medmcqa_alpaca_format"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
skaltenp/textworld_turn_top_demonstrations | 2023-09-18T10:06:22.000Z | [
"region:us"
] | skaltenp | null | null | null | 0 | 72 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: demonstration
sequence:
sequence: string
- name: moves
dtype: int64
- name: score
dtype: int64
splits:
- name: train
num_bytes: 16265368
num_examples: 4440
- name: valid
num_bytes: 787252
num_examples: 222
- name: test
num_bytes: 2640791
num_examples: 514
download_size: 3417454
dataset_size: 19693411
---
# Dataset Card for "textworld_turn_top_demonstrations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vitaliy-sharandin/climate-global-temp-country | 2023-09-21T15:48:20.000Z | [
"region:us"
] | vitaliy-sharandin | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: Year
dtype: int64
- name: China
dtype: float64
- name: India
dtype: float64
- name: Poland
dtype: float64
- name: United States
dtype: float64
- name: World
dtype: float64
- name: dt
dtype: timestamp[ns, tz=UTC]
splits:
- name: train
num_bytes: 3472
num_examples: 62
download_size: 7056
dataset_size: 3472
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "climate-global-temp-country"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mestopholis/test-prompts | 2023-09-23T20:46:04.000Z | [
"region:us"
] | Mestopholis | null | null | null | 0 | 72 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### 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] |
vitaliy-sharandin/pollution-krakow-no2-co | 2023-09-26T14:05:28.000Z | [
"region:us"
] | vitaliy-sharandin | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: NO2
dtype: float64
- name: CO
dtype: float64
- name: dt
dtype: timestamp[ns]
splits:
- name: train
num_bytes: 6816
num_examples: 284
download_size: 9084
dataset_size: 6816
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "pollution-krakow-no2-co"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Narya-ai/relevancy-dataset | 2023-09-28T14:08:11.000Z | [
"region:us"
] | Narya-ai | null | null | null | 0 | 72 | ---
dataset_info:
features:
- name: full_query
dtype: string
- name: article_text
dtype: string
- name: prompt
dtype: string
- name: parameters
struct:
- name: candidate_labels
sequence: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 2318409
num_examples: 211
download_size: 992453
dataset_size: 2318409
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "relevancy-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ShrinivasSK/small-hi-te | 2023-09-30T17:18:34.000Z | [
"region:us"
] | ShrinivasSK | null | null | null | 0 | 72 | Entry not found |
Alexandre-Numind/ValIE | 2023-10-04T11:26:25.000Z | [
"region:us"
] | Alexandre-Numind | null | null | null | 0 | 72 | Entry not found |
Fraol/TrainDedupedRefDatasetWMetricFinal2 | 2023-10-08T04:38:56.000Z | [
"region:us"
] | Fraol | null | null | null | 0 | 72 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: path_name
dtype: string
- name: file_name
dtype: string
- name: ref_type
dtype: string
- name: hash
dtype: string
- name: class_name
dtype: string
- name: method_name
dtype: string
- name: row_number
dtype: int64
- name: cbo
dtype: float64
- name: wmc
dtype: float64
- name: lcom*
dtype: float64
- name: loc
dtype: float64
- name: astc2
dtype: string
- name: source_after
dtype: string
- name: cbo_after
dtype: float64
- name: wmc_after
dtype: float64
- name: lcom*_after
dtype: float64
- name: loc_after
dtype: float64
- name: astc1
dtype: string
- name: issue_name
dtype: string
- name: issue_localize
dtype: string
splits:
- name: train
num_bytes: 427412313
num_examples: 15000
- name: test
num_bytes: 83826934
num_examples: 3000
download_size: 116099161
dataset_size: 511239247
---
# Dataset Card for "TrainDedupedRefDatasetWMetricFinal2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arabic_speech_corpus | 2022-11-18T18:29:09.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"region:us"
] | null | This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton.
The corpus was recorded in south Levantine Arabic
(Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .flac format and is not converted to a float32 array. To convert, the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
``` | @phdthesis{halabi2016modern,
title={Modern standard Arabic phonetics for speech synthesis},
author={Halabi, Nawar},
year={2016},
school={University of Southampton}
} | null | 15 | 71 | ---
pretty_name: Arabic Speech Corpus
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ar
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: arabic-speech-corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-index:
- config: clean
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
eval_split: test
col_mapping:
file: path
text: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
dataset_info:
features:
- name: file
dtype: string
- name: text
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: phonetic
dtype: string
- name: orthographic
dtype: string
config_name: clean
splits:
- name: train
num_bytes: 1002365
num_examples: 1813
- name: test
num_bytes: 65784
num_examples: 100
download_size: 1192302846
dataset_size: 1068149
---
# Dataset Card for Arabic Speech 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:** [Arabic Speech Corpus](http://en.arabicspeechcorpus.com/)
- **Repository:** [Needs More Information]
- **Paper:** [Modern standard Arabic phonetics for speech synthesis](http://en.arabicspeechcorpus.com/Nawar%20Halabi%20PhD%20Thesis%20Revised.pdf)
- **Leaderboard:** [Paperswithcode Leaderboard][Needs More Information]
- **Point of Contact:** [Nawar Halabi](mailto:nawar.halabi@gmail.com)
### Dataset Summary
This Speech corpus has been developed as part of PhD work carried out by Nawar Halabi at the University of Southampton. The corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio. Synthesized speech as an output using this corpus has produced a high quality, natural voice.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`.
An example from the dataset is:
```
{
'file': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav',
'audio': {'path': '/Users/username/.cache/huggingface/datasets/downloads/extracted/baebe85e2cb67579f6f88e7117a87888c1ace390f4f14cb6c3e585c517ad9db0/arabic-speech-corpus/wav/ARA NORM 0002.wav',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000},
'orthographic': 'waraj~aHa Alt~aqoriyru Al~a*iy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla ha*aA Aloqarono',
'phonetic': "sil w a r a' jj A H a tt A q r ii0' r u0 ll a * i0 < a E a' dd a h u0 m a' E h a d u0 < a b H aa' ^ i0 h A D A' b a t i0 tt i1' b t i0 f i0 l < a k aa d ii0 m ii0' y a t i0 SS II0 n ii0' y a t i0 l u0 l E u0 l uu0' m i0 sil < a' n t a s t a m i0' rr a d a r a j aa' t u0 l H a r aa' r a t i0 w a m u0 s t a w a y aa' t u0 rr U0 T UU0' b a t i0 f i0 l Ah i0 r t i0 f aa' E i0 T A' w A l a h aa' * a l q A' r n sil",
'text': '\ufeffwaraj~aHa Alt~aqoriyru Al~aTHiy >aEad~ahu maEohadu >aboHaA^i haDabapi Alt~ibiti fiy Alo>akaAdiymiy~api AlS~iyniy~api liloEuluwmi - >ano tasotamir~a darajaAtu AloHaraArapi wamusotawayaAtu Alr~uTuwbapi fiy Alo<irotifaAEi TawaAla haTHaA Aloqarono'
}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- 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]`.
- text: the transcription of the audio file.
- phonetic: the transcription in phonentics format.
- orthographic: the transcriptions written in orthographic format.
### Data Splits
| | Train | Test |
| ----- | ----- | ---- |
| dataset | 1813 | 100 |
## Dataset Creation
### Curation Rationale
The corpus was created with Speech Synthesis as the main application in mind. Although it has been used as part of a larger corpus for speech recognition and speech denoising. Here are some explanations why the corpus was built the way it is:
* Corpus size: Budget limitations and the research goal resulted in the decision not to gather more data. The goal was to show that high quality speech synthesis is possible with smaller corpora.
* Phonetic diversity: Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency.
* Content: News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal.
* Non-sense utterances: The corpus contains a large set of utterances that are generated computationally to compensate for the diphones missing in the main part of the corpus. The usefullness of non-sense utterances was not proven in the PhD thesis.
* The talent: The voice talent had a Syrian dialect from Damascus and spoke in formal Arabic.
Please refer to [PhD thesis](#Citation-Information) for more detailed information.
### Source Data
#### Initial Data Collection and Normalization
News, sports, economics, fully diacritised content from the internet was gathered. The choice of utterances was random to avoid copyright issues. Because of corpus size, acheiving diversity of content type was difficult and was not the goal. We were restricted to content which was fully diacritised to make the annotation process easier.
Just like with many corpora, the phonetic diversity was acheived using greedy methods. Start with a core set of utterances and add more utterances which contribute to adding more phonetic diversity the most iterativly. The measure of diversity is based on the diphone frequency.
Please refer to [PhD thesis](#Citation-Information).
#### Who are the source language producers?
Please refer to [PhD thesis](#Citation-Information).
### Annotations
#### Annotation process
Three annotators aligned audio with phonemes with the help of HTK forced alignment. They worked on overlapping parts as well to assess annotator agreement and the quality of the annotations. The entire corpus was checked by human annotators.
Please refer to [PhD thesis](#Citation-Information).
#### Who are the annotators?
Nawar Halabi and two anonymous Arabic language teachers.
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset. The voice talent agreed in writing for their voice to be used in speech technologies as long as they stay anonymous.
## 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 corpus was recorded in south Levantine Arabic (Damascian accent) using a professional studio by Nawar Halabi.
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@phdthesis{halabi2016modern,
title={Modern standard Arabic phonetics for speech synthesis},
author={Halabi, Nawar},
year={2016},
school={University of Southampton}
}
```
### Contributions
This dataset was created by:
* Nawar Halabi [@nawarhalabi](https://github.com/nawarhalabi) main creator and annotator.
* Two anonymous Arabic langauge teachers as annotators.
* One anonymous voice talent.
* Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. |
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",
"ar... | 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 = ""
} | null | 8 | 71 | ---
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:
- 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: 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. |
winvoker/turkish-sentiment-analysis-dataset | 2023-07-19T13:15:13.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"language:tr",
"license:cc-by-sa-4.0",
"region:us"
] | winvoker | null | null | null | 20 | 71 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- tr
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Turkish Sentiment Dataset
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset
This dataset contains positive , negative and notr sentences from several data sources given in the references. In the most sentiment models , there are only two labels; positive and negative. However , user input can be totally notr sentence. For such cases there were no data I could find. Therefore I created this dataset with 3 class. Positive and negative sentences are listed below. Notr examples are extraced from turkish wiki dump. In addition, added some random text inputs like "Lorem ipsum dolor sit amet.".
There are 492.782 labeled sentences. %10 of them used for testing.
# Türkçe Duygu Analizi Veriseti
Bu veriseti , farklı kaynaklardan derlenmiş pozitif , negatif ve nötr sınıflardan örnekler içerir. Bir çok verisetinde sadece pozitif ve negatif bulunur. Fakat kullanıcı input'u nötr olabilir. Bu tarz durumlar için türkçe bir dataset bulmakta zorlandım. Dolayısıyla , 3 sınıftan oluşan bu dataseti oluşturdum. Pozitif ve negatif örnekleri aldığın kaynaklar referans kısmında listelenmiştir. Nötr cümleler ise wikipedia datasından alınmıştır. Ek olarak bazı rastgele inputlar nötr olarak eklenmiştir. Örneğin: "Lorem ipsum dolor sit amet.".
There are 492.782 labeled sentences. %10 of them used for testing.
# References
- https://www.kaggle.com/burhanbilenn/duygu-analizi-icin-urun-yorumlari
- https://github.com/fthbrmnby/turkish-text-data
- https://www.kaggle.com/mustfkeskin/turkish-wikipedia-dump
- https://github.com/ezgisubasi/turkish-tweets-sentiment-analysis
- http://humirapps.cs.hacettepe.edu.tr/
You can reach me via LinkedIn. https://www.linkedin.com/in/batuhanayhan/ |
nthngdy/oscar-mini | 2022-12-06T11:05:51.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"source_datasets:oscar",
"language:af",
"language:am",
"language:ar",
"language:arz",
"language:as",
"language:az",
"language:azb"... | nthngdy | The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.\ | @inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{\'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{\'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{\"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
} | null | 2 | 71 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- af
- am
- ar
- arz
- as
- az
- azb
- ba
- be
- bg
- bn
- bo
- br
- ca
- ce
- ceb
- ckb
- cs
- cv
- cy
- da
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- gu
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mhr
- mk
- ml
- mn
- mr
- ms
- mt
- my
- nds
- ne
- nl
- nn
- 'no'
- or
- os
- pa
- pl
- pnb
- ps
- pt
- ro
- ru
- sa
- sah
- sd
- sh
- si
- sk
- sl
- sq
- sr
- sv
- sw
- ta
- te
- tg
- th
- tk
- tl
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- yi
- zh
license:
- cc0-1.0
multilinguality:
- multilingual
source_datasets:
- oscar
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: oscar
pretty_name: OSCAR
---
## WARNING: this dataset is an extract of the OSCAR dataset published here to simulate the use of the full dataset in low-resource contexts and debug codebases that would eventually use the original OSCAR dataset.
Using this dataset is equivalent to using a processed version of OSCAR legally speaking. I take no credit for the gathering of the original data and hence refer entirely to the original dataset in the card below.
# Dataset Card for "oscar"
## 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://oscar-corpus.com](https://oscar-corpus.com)
- **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
OSCAR or **O**pen **S**uper-large **C**rawled [**A**LMAnaCH](https://team.inria.fr/almanach/) co**R**pus is a huge multilingual corpus obtained by language classification and filtering of the [Common Crawl](https://commoncrawl.org/) corpus using the [goclassy](https://github.com/pjox/goclassy) architecture. Data is distributed by language in both original and deduplicated form.
### Supported Tasks and Leaderboards
OSCAR is mainly intended to pretrain language models and word represantations.
### Languages
All the data is distributed by language, both the original and the deduplicated versions of the data are available. 166 different languages are available. The table in subsection [Data Splits Sample Size](#data-splits-sample-size) provides the language code for each subcorpus as well as the number of words (space separated tokens), lines and sizes for both the original and the deduplicated versions of OSCAR.
## Dataset Structure
We show detailed information for all the configurations of the dataset.
## Dataset Creation
### Curation Rationale
OSCAR was constructed new pipeline derived from the [fastText's one](https://github.com/facebookresearch/fastText), called [_goclassy_](https://github.com/pjox/goclassy). Goclassy reuses the [fastText linear classifier](https://fasttext.cc) and the pre-trained fastText model for language recognition, but it completely rewrites and parallelises their pipeline in an asynchronous manner.
The order of operations is more or less the same as in the fastText pre-processing pipeline but instead of clustering multiple operations into a single blocking process, a worker is launched for each operation but bounding the number of possible parallel operations at a given time by the number of available threads instead of the number of CPUs. Goclassy is implemented in the [Go programming language](https://golang.org/) so it lets the [Go runtime](https://golang.org/src/runtime/mprof.go) handle the scheduling of the processes. Thus the goclassy's pipeline one does not have to wait for a whole WET file to download, decompress and classify in order to start downloading and processing the next one, a new file will start downloading and processing as soon as the scheduler is able to allocate a new process.
Filtering and cleaning processes at line level are done before feeding each line to the classifier. Lines shorter than 100 UTF-8 characters and lines containing invalid UTF-8 characters are discarted and are not classified. After all files are proccesed the deduplicated versions are constructed and everything is then splitted in shards and compressed.
### Source Data
#### Initial Data Collection and Normalization
[Common Crawl](https://commoncrawl.org/) is a non-profit foundation which produces and maintains an open repository of web crawled data that is both accessible and analysable. Common Crawl's complete web archive consists of petabytes of data collected over 8 years of web crawling. The repository contains raw web page HTML data (WARC files), metdata extracts (WAT files) and plain text extracts (WET files). The organisation's crawlers has always respected [nofollow](http://microformats.org/wiki/rel-nofollow) and [robots.txt](https://www.robotstxt.org/) policies.
Each monthly Common Crawl snapshot is in itself a massive multilingual corpus, where every single file contains data coming from multiple web pages written in a large variety of languages and covering all possible types of topics.
To construct OSCAR the WET files of Common Crawl were used. These contain the extracted plain texts from the websites mostly converted to UTF-8, as well as headers containing the metatada of each crawled document. Each WET file comes compressed in gzip format and is stored on Amazon Web Services. In the case of OSCAR, the **November 2018** snapshot was used. It surpasses 20TB of uncompressed data and contains more than 50 thousand plain text files where each file consists of the plain text from multiple websites along its metadata header.
#### Who are the source language producers?
The data comes from multiple web pages in a large variety of languages.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
N/A
#### Who are the annotators?
N/A
### Personal and Sensitive Information
Being constructed from Common Crawl, Personal and sensitive information might be present. This **must** be considered before training deep learning models with OSCAR, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
OSCAR is intended to bring more data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the pre-training of state-of-the-art language modeling architectures.
### Discussion of Biases
OSCAR is not properly filtered yet and this can be reflected on the models trained with it. Care is advised specially concerning biases of the resulting models.
### Other Known Limitations
The [fastText linear classifier](https://fasttext.cc) is limed both in performance and the variety of languages it can recognize, so the quality of some OSCAR sub-corpora might be lower than expected, specially for the lowest-resource langiuages. Some audits have already been done by [third parties](https://arxiv.org/abs/2010.14571).
## Additional Information
### Dataset Curators
The corpus was put together by [Pedro J. Ortiz](https://pjortiz.eu/), [Benoît Sagot](http://pauillac.inria.fr/~sagot/), and [Laurent Romary](https://cv.archives-ouvertes.fr/laurentromary), during work done at [Inria](https://www.inria.fr/en), particularly at the [ALMAnaCH team](https://team.inria.fr/almanach/).
### Licensing Information
These data are released under this licensing scheme
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these data under the Creative Commons CC0 license ("no rights reserved") http://creativecommons.org/publicdomain/zero/1.0/
To the extent possible under law, Inria has waived all copyright and related or neighboring rights to OSCAR
This work is published from: France.
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
We will comply to legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
```
@inproceedings{ortiz-suarez-etal-2020-monolingual,
title = "A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages",
author = "Ortiz Su{'a}rez, Pedro Javier and
Romary, Laurent and
Sagot, Benoit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.156",
pages = "1703--1714",
abstract = "We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.",
}
@inproceedings{OrtizSuarezSagotRomary2019,
author = {Pedro Javier {Ortiz Su{'a}rez} and Benoit Sagot and Laurent Romary},
title = {Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures},
series = {Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-7) 2019. Cardiff, 22nd July 2019},
editor = {Piotr Bański and Adrien Barbaresi and Hanno Biber and Evelyn Breiteneder and Simon Clematide and Marc Kupietz and Harald L{"u}ngen and Caroline Iliadi},
publisher = {Leibniz-Institut f{"u}r Deutsche Sprache},
address = {Mannheim},
doi = {10.14618/ids-pub-9021},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:mh39-90215},
pages = {9 -- 16},
year = {2019},
abstract = {Common Crawl is a considerably large, heterogeneous multilingual corpus comprised of crawled documents from the internet, surpassing 20TB of data and distributed as a set of more than 50 thousand plain text files where each contains many documents written in a wide variety of languages. Even though each document has a metadata block associated to it, this data lacks any information about the language in which each document is written, making it extremely difficult to use Common Crawl for monolingual applications. We propose a general, highly parallel, multithreaded pipeline to clean and classify Common Crawl by language; we specifically design it so that it runs efficiently on medium to low resource infrastructures where I/O speeds are the main constraint. We develop the pipeline so that it can be easily reapplied to any kind of heterogeneous corpus and so that it can be parameterised to a wide range of infrastructures. We also distribute a 6.3TB version of Common Crawl, filtered, classified by language, shuffled at line level in order to avoid copyright issues, and ready to be used for NLP applications.},
language = {en}
}
```
### Contributions
Thanks to [@pjox](https://github.com/pjox) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
copenlu/sufficient_facts | 2022-08-05T08:33:48.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|fever",
"source_datasets:extended|hover",
"source_datasets:extended|fever_gold_... | copenlu | SufficientFacts is a diagnostic test dataset for fact checking with insufficient evidence. | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | null | 3 | 71 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: sufficient_facts
size_categories:
- 1K<n<10K
source_datasets:
- extended|fever
- extended|hover
- extended|fever_gold_evidence
task_categories:
- text-classification
task_ids:
- fact-checking
---
# Dataset Card for sufficient_facts
## Table of Contents
- [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)
- [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/copenlu/sufficient_facts
- **Repository:** https://github.com/copenlu/sufficient_facts
- **Paper:** Will be uploaded soon...
- **Leaderboard:**
- **Point of Contact:** https://apepa.github.io/
### Dataset Summary
This is the dataset SufficientFacts, introduced in the paper "Fact Checking with Insufficient Evidence", accepted at the TACL journal in 2022.
Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, **SufficientFacts**, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21% accuracy), whereas it is easiest for omitted date modifiers (63% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.
### Languages
English
## Dataset Structure
The dataset consists of three files, each for one of the datasets -- FEVER, HoVer, and VitaminC.
Each file consists of json lines of the format:
```json
{
"claim": "Unison (Celine Dion album) was originally released by Atlantic Records.",
"evidence": [
[
"Unison (Celine Dion album)",
"The album was originally released on 2 April 1990 ."
]
],
"label_before": "REFUTES",
"label_after": "NOT ENOUGH",
"agreement": "agree_ei",
"type": "PP",
"removed": ["by Columbia Records"],
"text_orig": "[[Unison (Celine Dion album)]] The album was originally released on 2 April 1990 <span style=\"color:red;\">by Columbia Records</span> ."
}
```
### Data Instances
* FEVER: 600 consituent-level, 400 sentence-level;
* HoVer - 600 consituent-level, 400 sentence-level;
* VitaminC - 600 consituent-level.
### Data Fields
* `claim` - the claim that is being verified
* `evidence` - the augmented evidence for the claim, i.e. the evidence with some removed information
* `label_before` - the original label for the claim-evidence pair, before information was removed from the evidence
* `label_after` - the label for the augmented claim-evidence pair, after information was removed from the evidence, as annotated by crowd-source workers
* `type` - type of the information removed from the evidence. The types are fine-grained and their mapping to the general types -- 7 constituent and 1 sentence type can be found in [types.json](types.json) file.
* `removed` - the text of the removed information from the evidence
* `text_orig` - the original text of the evidence, as presented to crowd-source workers, the text of the removed information is inside `<span style=\"color:red;\"></span>` tags.
### Data Splits
| name |test_fever|test_hover|test_vitaminc|
|----------|-------:|-----:|-------:|
|test| 1000| 1000| 600|
Augmented from the test splits of the corresponding datasets.
### Annotations
#### Annotation process
The workers were provided with the following task description:
For each evidence text, some facts have been removed (marked in <span style="color:red;">red</span>).
You should annotate whether, <b>given the remaining facts in the evidence text, the evidence is still enough for verifying the claim.</b> <br></br>
<ul>
<li>You should select <i><b>'ENOUGH -- IRRELEVANT'</b></i>, if the <b>remaining information is still <i>enough</i></b> for verifying the claim because the <b>removed information is irrelevant</b> for identifying the evidence as SUPPORTS or REFUTES. See examples 1 and 2.</li>
<li>You should select <i><b>'ENOUGH -- REPEATED'</b></i>, if the <b>remaining information is still <i>enough</i></b> for verifying the claim because the <b>removed information is relevant but is also present (repeated) in the remaining (not red) text.</b> See example 3.</li>
<li>You should select <i><b>'NOT ENOUGH'</b></i> -- when <b>1) the removed information is <i>relevant</i></b> for verifying the claim <b> AND 2) it is <i>not present (repeated)</i> in the remaining text.</b> See examples 4, 5, and 6.</li>
<!--<li>You should select <i><b>'CHANGED INFO'</b></i> in the rare cases when the remaining evidence has <b>changed the support for the claim</b></li>-->
</ul>
<b>Note: You should not incorporate your own knowledge or beliefs! You should rely only on the evidence provided for the claim.</b>
The annotators were then given example instance annotations.
Finally, annotators were asked to complete a qualification test in order to be allowed to annotate instances for the task.
The resulting inter-annotator agreement for SufficientFacts is 0.81 Fleiss'k from three annotators.
#### Who are the annotators?
The annotations were performed by workers at Amazon Mechanical Turk.
## Additional Information
### Licensing Information
MIT
### Citation Information
```
@article{10.1162/tacl_a_00486,
author = {Atanasova, Pepa and Simonsen, Jakob Grue and Lioma, Christina and Augenstein, Isabelle},
title = "{Fact Checking with Insufficient Evidence}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {10},
pages = {746-763},
year = {2022},
month = {07},
abstract = "{Automating the fact checking (FC) process relies on information obtained from external sources. In this work, we posit that it is crucial for FC models to make veracity predictions only when there is sufficient evidence and otherwise indicate when it is not enough. To this end, we are the first to study what information FC models consider sufficient by introducing a novel task and advancing it with three main contributions. First, we conduct an in-depth empirical analysis of the task with a new fluency-preserving method for omitting information from the evidence at the constituent and sentence level. We identify when models consider the remaining evidence (in)sufficient for FC, based on three trained models with different Transformer architectures and three FC datasets. Second, we ask annotators whether the omitted evidence was important for FC, resulting in a novel diagnostic dataset, SufficientFacts1, for FC with omitted evidence. We find that models are least successful in detecting missing evidence when adverbial modifiers are omitted (21\\% accuracy), whereas it is easiest for omitted date modifiers (63\\% accuracy). Finally, we propose a novel data augmentation strategy for contrastive self-learning of missing evidence by employing the proposed omission method combined with tri-training. It improves performance for Evidence Sufficiency Prediction by up to 17.8 F1 score, which in turn improves FC performance by up to 2.6 F1 score.}",
issn = {2307-387X},
doi = {10.1162/tacl_a_00486},
url = {https://doi.org/10.1162/tacl\_a\_00486},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00486/2037141/tacl\_a\_00486.pdf},
}
```
### Contributions
Thanks to [@apepa](https://github.com/apepa) for adding this dataset. |
hugginglearners/netflix-shows | 2022-08-18T03:04:55.000Z | [
"license:cc0-1.0",
"region:us"
] | hugginglearners | null | null | null | 4 | 71 | ---
license:
- cc0-1.0
kaggle_id: infamouscoder/dataset-netflix-shows
---
# Dataset Card for Dataset: NetFlix Shows
## 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://kaggle.com/datasets/infamouscoder/dataset-netflix-shows
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The raw data is Web Scrapped through Selenium. It contains Unlabelled text data of around 9000 Netflix Shows and Movies along with Full details like Cast, Release Year, Rating, Description, etc.
### 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
This dataset was shared by [@infamouscoder](https://kaggle.com/infamouscoder)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
DTU54DL/common-accent | 2022-11-30T13:25:07.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | DTU54DL | null | null | null | 0 | 71 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- token-classification-other-acronym-identification
train-eval-index:
- col_mapping:
labels: tags
tokens: tokens
config: default
splits:
eval_split: test
task: token-classification
task_id: entity_extraction
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: accent
dtype: string
splits:
- name: train
num_bytes: 471755846.3910719
num_examples: 10000
- name: test
num_bytes: 19497172.25755167
num_examples: 451
download_size: 436911322
dataset_size: 491253018.6486236
---
# Dataset Card for [Dataset Name]
## 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:**
- **Repository:**
- **Paper:**
- **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 [@github-username](https://github.com/<github-username>) for adding this dataset. |
IlyaGusev/pikabu | 2023-03-12T14:50:29.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ru",
"region:us"
] | IlyaGusev | null | null | null | 11 | 71 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: title
dtype: string
- name: text_markdown
dtype: string
- name: timestamp
dtype: uint64
- name: author_id
dtype: int64
- name: username
dtype: string
- name: rating
dtype: int64
- name: pluses
dtype: int64
- name: minuses
dtype: int64
- name: url
dtype: string
- name: tags
sequence: string
- name: blocks
sequence:
- name: data
dtype: string
- name: type
dtype: string
- name: comments
sequence:
- name: id
dtype: int64
- name: timestamp
dtype: uint64
- name: parent_id
dtype: int64
- name: text_markdown
dtype: string
- name: text_html
dtype: string
- name: images
sequence: string
- name: rating
dtype: int64
- name: pluses
dtype: int64
- name: minuses
dtype: int64
- name: author_id
dtype: int64
- name: username
dtype: string
splits:
- name: train
num_bytes: 96105803658
num_examples: 6907622
download_size: 20196853689
dataset_size: 96105803658
task_categories:
- text-generation
language:
- ru
size_categories:
- 1M<n<10M
---
# Pikabu dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Data Instances](#data-instances)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Description
**Summary:** Dataset of posts and comments from [pikabu.ru](https://pikabu.ru/), a website that is Russian Reddit/9gag.
**Script:** [convert_pikabu.py](https://github.com/IlyaGusev/rulm/blob/master/data_processing/convert_pikabu.py)
**Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
**Languages:** Mostly Russian.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Dataset iteration:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/pikabu', split="train", streaming=True)
for example in dataset:
print(example["text_markdown"])
```
## Data Instances
```
{
"id": 69911642,
"title": "Что можно купить в Китае за цену нового iPhone 11 Pro",
"text_markdown": "...",
"timestamp": 1571221527,
"author_id": 2900955,
"username": "chinatoday.ru",
"rating": -4,
"pluses": 9,
"minuses": 13,
"url": "...",
"tags": ["Китай", "AliExpress", "Бизнес"],
"blocks": {"data": ["...", "..."], "type": ["text", "text"]},
"comments": {
"id": [152116588, 152116426],
"text_markdown": ["...", "..."],
"text_html": ["...", "..."],
"images": [[], []],
"rating": [2, 0],
"pluses": [2, 0],
"minuses": [0, 0],
"author_id": [2104711, 2900955],
"username": ["FlyZombieFly", "chinatoday.ru"]
}
}
```
You can use this little helper to unflatten sequences:
```python
def revert_flattening(records):
fixed_records = []
for key, values in records.items():
if not fixed_records:
fixed_records = [{} for _ in range(len(values))]
for i, value in enumerate(values):
fixed_records[i][key] = value
return fixed_records
```
## Source Data
* The data source is the [Pikabu](https://pikabu.ru/) website.
* An original dump can be found here: [pikastat](https://pikastat.d3d.info/)
* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/master/data_processing/convert_pikabu.py).
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
|
mstz/nbfi | 2023-04-07T14:33:02.000Z | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"nbfi",
"tabular_classification",
"binary_classification",
"region:us"
] | mstz | null | null | null | 0 | 71 | ---
language:
- en
tags:
- nbfi
- tabular_classification
- binary_classification
pretty_name: NBFI
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- default
license: cc
---
# NBFI
The [NBFI dataset](https://www.kaggle.com/datasets/meastanmay/nbfi-vehicle-loan-repayment-dataset) from the [Kaggle](https://www.kaggle.com/datasets).
Client default prediction.
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| default | Binary classification | Has the client defaulted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/nbfi")["train"]
```
# Features
|**Feature** |**Type** |
|-----------------------------------------------|---------------|
|`income` | `float32` |
|`owns_a_car` | `bool` |
|`owns_a_bike` | `bool` |
|`has_an_active_loan` | `bool` |
|`owns_a_house` | `bool` |
|`nr_children` | `int8` |
|`credit` | `float32` |
|`loan_annuity` | `float32` |
|`accompanied_by` | `string` |
|`income_type` | `string` |
|`education_level` | `float32` |
|`marital_status` | `float32` |
|`is_male` | `bool` |
|`type_of_contract` | `string` |
|`type_of_housing` | `string` |
|`residence_density` | `float32` |
|`age_in_days` | `int32` |
|`consecutive_days_of_employment` | `int16` |
|`nr_days_since_last_registration_change` | `int32` |
|`nr_days_since_last_document_change` | `int32` |
|`owned_a_house_for_nr_days` | `int32` |
|`has_provided_a_mobile_number` | `bool` |
|`has_provided_a_home_number` | `bool` |
|`was_reachable_at_work` | `bool` |
|`job` | `string` |
|`nr_family_members` | `int8` |
|`city_rating` | `int8` |
|`weekday_of_application` | `int8` |
|`hour_of_application` | `float32` |
|`same_residence_and_home` | `bool` |
|`same_work_and_home` | `bool` |
|`score_1` | `float32` |
|`score_2` | `float32` |
|`score_3` | `float32` |
|`nr_defaults_in_social_circle` | `int8` |
|`inquiries_in_last_year` | `float32` | |
mstz/sonar | 2023-04-16T18:02:16.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | null | null | 0 | 71 | ---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- UCI
pretty_name: Sonar
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- sonar
license: cc
---
# Sonar
The [Sonar dataset](https://archive-beta.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| sonar | Binary classification | Is the sonar detecting a rock? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/sonar")["train"]
``` |
mstz/ionosphere | 2023-04-16T17:32:10.000Z | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"ionosphere",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_ionosphere_52,
author = {Sigillito,V., Wing,S., Hutton,L. & Baker,K.},
title = {{Ionosphere}},
year = {1989},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5W01B}}
} | null | 0 | 71 | ---
language:
- en
tags:
- ionosphere
- tabular_classification
- binary_classification
- UCI
pretty_name: Ionosphere
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- ionosphere
license: cc
---
# Ionosphere
The [Ionosphere dataset](https://archive.ics.uci.edu/ml/datasets/Ionosphere) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their ionosphere threshold.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------------------------------------------|
| ionosphere | Binary classification | Does the received signal indicate electrons in the ionosphere?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/ionosphere")["train"]
``` |
mstz/post_operative | 2023-04-16T17:58:06.000Z | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"post_operative",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] | mstz | null | @misc{misc_post-operative_patient_82,
author = {Summers,Sharon & Woolery,Linda},
title = {{Post-Operative Patient}},
year = {1993},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5DG6Q}}
} | null | 0 | 71 | ---
language:
- en
tags:
- post_operative
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Page Blocks
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- post_operative
- post_operative_binary
license: cc
---
# Post Operative
The [PostOperative dataset](https://archive-beta.ics.uci.edu/dataset/82/post+operative+patient) from the [UCI repository](https://archive-beta.ics.uci.edu/).
Should the patient be discharged from the hospital, go to the ground floor, or to the ICU?
# Configurations and tasks
| **Configuration** | **Task** |
|-----------------------|---------------------------|
| post_operative | Multiclass classification.|
| post_operative_binary | Binary classification. | |
edarchimbaud/revenue-estimate-stocks | 2023-10-07T23:16:17.000Z | [
"region:us"
] | edarchimbaud | null | null | null | 2 | 71 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: date
dtype: string
- name: current_qtr
dtype: string
- name: no_of_analysts_current_qtr
dtype: int64
- name: next_qtr
dtype: string
- name: no_of_analysts_next_qtr
dtype: int64
- name: current_year
dtype: int64
- name: no_of_analysts_current_year
dtype: int64
- name: next_year
dtype: int64
- name: no_of_analysts_next_year
dtype: int64
- name: avg_estimate_current_qtr
dtype: string
- name: avg_estimate_next_qtr
dtype: string
- name: avg_estimate_current_year
dtype: string
- name: avg_estimate_next_year
dtype: string
- name: low_estimate_current_qtr
dtype: string
- name: low_estimate_next_qtr
dtype: string
- name: low_estimate_current_year
dtype: string
- name: low_estimate_next_year
dtype: string
- name: high_estimate_current_qtr
dtype: string
- name: high_estimate_next_qtr
dtype: string
- name: high_estimate_current_year
dtype: string
- name: high_estimate_next_year
dtype: string
- name: year_ago_sales_current_qtr
dtype: string
- name: year_ago_sales_next_qtr
dtype: string
- name: year_ago_sales_current_year
dtype: string
- name: year_ago_sales_next_year
dtype: string
- name: sales_growth_yearest_current_qtr
dtype: string
- name: sales_growth_yearest_next_qtr
dtype: string
- name: sales_growth_yearest_current_year
dtype: string
- name: sales_growth_yearest_next_year
dtype: string
splits:
- name: train
num_bytes: 5580221
num_examples: 19721
download_size: 732371
dataset_size: 5580221
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "revenue-estimate-sp500"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://edarchimbaud.substack.com
- **Repository:** https://github.com/edarchimbaud
- **Point of Contact:** contact@edarchimbaud.com
### Dataset Summary
The revenue-estimate-sp500 dataset provides revenue estimate data for companies in the S&P 500 index.
### Supported Tasks and Leaderboards
The dataset can be used to analyze and predict revenue estimates for companies in the S&P 500 index.
## Dataset Structure
### Data Instances
[N/A]
### Data Fields
- symbol (string): A string representing the ticker symbol or abbreviation used to identify the company.
- date (string): A string indicating the date of the recorded data.
- current_qtr (string): A string representing the current quarter.
- no_of_analysts_current_qtr (int64): An integer indicating the number of analysts providing estimates for the current quarter.
- next_qtr (string): A string representing the next quarter.
- no_of_analysts_next_qtr (int64): An integer indicating the number of analysts providing estimates for the next quarter.
- current_year (int64): An integer indicating the current year.
- no_of_analysts_current_year (int64): An integer indicating the number of analysts providing estimates for the current year.
- next_year (int64): An integer indicating the next year.
- no_of_analysts_next_year (int64): An integer indicating the number of analysts providing estimates for the next year.
- avg_estimate_current_qtr (string): A string representing the average estimate for the current quarter.
- avg_estimate_next_qtr (string): A string representing the average estimate for the next quarter.
- avg_estimate_current_year (string): A string representing the average estimate for the current year.
- avg_estimate_next_year (string): A string representing the average estimate for the next year.
- low_estimate_current_qtr (string): A string representing the low estimate for the current quarter.
- low_estimate_next_qtr (string): A string representing the low estimate for the next quarter.
- low_estimate_current_year (string): A string representing the low estimate for the current year.
- low_estimate_next_year (string): A string representing the low estimate for the next year.
- high_estimate_current_qtr (string): A string representing the high estimate for the current quarter.
- high_estimate_next_qtr (string): A string representing the high estimate for the next quarter.
- high_estimate_current_year (string): A string representing the high estimate for the current year.
- high_estimate_next_year (string): A string representing the high estimate for the next year.
- year_ago_sales_current_qtr (string): A string representing the year-ago sales for the current quarter.
- year_ago_sales_next_qtr (string): A string representing the year-ago sales for the next quarter.
- year_ago_sales_current_year (string): A string representing the year-ago sales for the current year.
- year_ago_sales_next_year (string): A string representing the year-ago sales for the next year.
- sales_growth_yearest_current_qtr (string): A string representing the sales growth estimate for the current quarter.
- sales_growth_yearest_next_qtr (string): A string representing the sales growth estimate for the next quarter.
- sales_growth_yearest_current_year (string): A string representing the sales growth estimate for the current year.
- sales_growth_yearest_next_year (string): A string representing the sales growth estimate for the next year.
### Data Splits
A single split, called train.
## Dataset Creation
### Curation Rationale
The revenue-estimate-sp500 dataset was created to provide revenue estimate data for companies in the S&P 500 index.
### Source Data
The data was collected and normalized from reliable sources.
## Additional Information
### Dataset Curators
The revenue-estimate-sp500 dataset was collected by https://edarchimbaud.substack.com.
### Licensing Information
The revenue-estimate-sp500 dataset is licensed under the MIT License.
### Citation Information
> https://edarchimbaud.substack.com, revenue-estimate-sp500 dataset, GitHub repository, https://github.com/edarchimbaud
### Contributions
Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset. |
izumi-lab/llm-japanese-dataset-vanilla | 2023-09-29T14:40:26.000Z | [
"size_categories:1M<n<10M",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2305.12720",
"arxiv:2309.03412",
"region:us"
] | izumi-lab | null | null | null | 5 | 71 | ---
license: cc-by-sa-4.0
language:
- ja
size_categories:
- 1M<n<10M
---
# llm-japanese-dataset-vanilla
LLM構築用の日本語チャットデータセット
[izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) から，日英翻訳のデータセット等を抜いたものです．
主に，日本語LLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます．
※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます．
## データの詳細
データの詳細は，[izumi-lab/llm-japanese-dataset](https://huggingface.co/datasets/izumi-lab/llm-japanese-dataset) に関する，以下の論文を参照してください．
- 日本語: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383)
- 英語: [https://arxiv.org/abs/2305.12720](https://arxiv.org/abs/2305.12720)
- GitHub: [https://github.com/masanorihirano/llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset)
- 最新情報: [llm.msuzuki.me](https://llm.msuzuki.me).
なお，Citationには，よろしければ，以下をご利用ください．
```
@preprint{Suzuki2023-llmvanilla,
title={{From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models}},
autor={Masahiro Suzuki and Masanori Hirano and Hiroki Sakaji},
doi={10.48550/arXiv.2309.03412},
archivePrefix={arXiv},
arxivId={2309.03412},
year={2023}
}
```
共同研究，データ提供，各種支援，その他問い合わせは，izumi-llm@socsim.org へ．
## How to use
```python
from datasets import load_dataset
dataset = load_dataset("izumi-lab/llm-japanese-dataset-vanilla", revision="0.1.0")
print(dataset.num_rows)
# {'train': 1811964}
dataset = load_dataset("izumi-lab/llm-japanese-dataset-vanilla", revision="1.0.0")
print(dataset.num_rows)
# {'train': 2515626}
```
v0.1.0 contains 1,811,964 data
v1.0.0 contains 2,515,626 data
For more details, see: https://github.com/masanorihirano/llm-japanese-dataset/tree/vanilla
## LICENSE
CC-BY-SA 4.0
(For more details, see: LICENSE, NOTICE.md, NOTICE2.md)
## Note
To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).
|
hamishivi/alpaca-farm-davinci-003-2048-token | 2023-06-09T06:14:36.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | hamishivi | null | null | null | 0 | 71 | ---
license: cc-by-nc-4.0
---
|
zjunlp/Mol-Instructions | 2023-10-10T07:54:24.000Z | [
"size_categories:100M<n<1B",
"language:en",
"license:cc-by-4.0",
"chemistry",
"biology",
"molecule",
"protein",
"instructions",
"arxiv:2306.08018",
"region:us"
] | zjunlp | Mol-Instructions datasets. | @misc{merity2016pointer,
title={},
author={},
year={2023},
} | null | 17 | 71 | ---
language:
- en
size_categories:
- 100M<n<1B
license: cc-by-4.0
tags:
- chemistry
- biology
- molecule
- protein
- instructions
---
<h1 align="center"> 🧪 Mol-Instructions </h1>
<h3 align="center"> An open, large-scale biomolecular instruction dataset for large language models. </h3>
> Please refer to our [repository](https://github.com/zjunlp/Mol-Instructions) and [paper](https://arxiv.org/abs/2306.08018) for more details.
## 📌 Contents
- [Overview](#1)
- [Data Stats](#1-1)
- [Data Construction](#1-2)
- [Data Release](#1-3)
- [Tasks](#2)
- [Molecule-oriented](#2-1)
- [Protein-oriented](#2-2)
- [Biomolecule text](#2-3)
- [Demo](#3)
- [Model Weight Release](#3-1)
- [Model Usage Guide](#3-2)
- [FAQ](#3-3)
- [Notices](#4)
- [Usage and License](#4-1)
- [Limitations](#4-2)
- [About](#5)
- [References](#5-1)
- [Acknowledgements](#5-2)
<h2 id="1">1. Overview</h2>
<h3 id="1-1"> 📊 1.1 Data Stats</h3>
**Mol-Instructions** comprises three cardinal components:
- 🔬 *Molecule-oriented instructions:* This component delves into the world of small molecules, emphasizing their inherent properties and behaviors. It sheds light on the fundamental challenges of diverse chemical reactions and molecular design, with 148,4K instructions across six tasks.
- 🧬 *Protein-oriented instructions:* Rooted in the biosciences, this component presents 505K instructions across five distinct categories of tasks. These tasks aim to predict the structure, function, and activity of proteins, and facilitate protein design based on textual directives.
- 🥼 *Biomolecular text instructions:* Predominantly designed to cater to NLP tasks within the fields of bioinformatics and chemoinformatics, this part encapsulates six information extraction and Q\&A tasks represented through 53K instructions.
<h3 id="1-2"> 🛠️ 1.2 Data Construction</h3>
- 🤖️ *Human-AI Collaboration Task Description Creation*: In real-world applications, task instructions must be able to accommodate the varied and dynamic nature of human needs and queries. We emulate this diversity by starting with a clear, human-crafted description for each task, which is then used as an input to GPT-3.5-turbo.
- 📖 *Information Derivation from Existing Data*: Biomolecular data often requires specialist laboratory experiments and expert analysis, making authoritative and recognized biochemistry databases an ideal source of our data. With suitable processing, these resources enable us to extract the required instruction data.
- 📜 *Template-based Conversion of Biological Data into Textual Format*: To facilitate the transformation of these structured annotations into a textual format, we design a diverse array of templates. Each resulting text-based annotation serves as a guideline for protein design.
- ✅ *Quality Control*: To expedite the model's ability to generate precise biomolecules, we implement stringent quality control measures for our biomolecular data.
<h3 id="1-3"> 🤗 1.3 Data Release</h3>
We release the dataset on Hugging Face at [zjunlp/Mol-Instructions](https://huggingface.co/datasets/zjunlp/Mol-Instructions).
<h2 id="2">2. Tasks</h2>
<h3 id="2-1"> 🔬 2.1 Molecule-oriented</h3>
<details>
<summary><b>Molecule description generation</b></summary>
- *Please give me some details about this molecule:*
[C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][=Branch1][C][=O][O][C@H1][Branch2][Ring1][=Branch1][C][O][C][=Branch1][C][=O][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][C][O][P][=Branch1][C][=O][Branch1][C][O][O][C][C@@H1][Branch1][=Branch1][C][=Branch1][C][=O][O][N]
```
The molecule is a 3-sn-phosphatidyl-L-serine in which the phosphatidyl acyl groups at positions 1 and 2 are specified as stearoyl and arachidonoyl respectively.
It is functionally related to an arachidonic acid and an octadecanoic acid.
```
</details>
<details>
<summary><b>Description-guided molecule design</b></summary>
- *Create a molecule with the structure as the one described:*
The molecule is a primary arylamine in which an amino functional group is substituted for one of the benzene hydrogens. It is a primary arylamine and a member of anilines.
```
[N][C][=C][C][=C][C][=C][Ring1][=Branch1]
```
</details>
<details>
<summary><b>Forward reaction prediction</b></summary>
- *With the provided reactants and reagents, propose a potential product:*
[O][=N+1][Branch1][C][O-1][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2].[Fe]
```
[N][C][=C][N][=C][Branch1][C][Cl][C][Branch1][C][I][=C][Ring1][Branch2]
```
</details>
<details>
<summary><b>Retrosynthesis</b></summary>
- *Please suggest potential reactants used in the synthesis of the provided product:*
[C][=C][C][C][N][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
[C][=C][C][C][N].[C][C][Branch1][C][C][Branch1][C][C][O][C][=Branch1][C][=O][O][C][=Branch1][C][=O][O][C][Branch1][C][C][Branch1][C][C][C]
```
</details>
<details>
<summary><b>Reagent prediction</b></summary>
- *Please provide possible reagents based on the following chemical reaction:*
[C][C][=C][C][=C][Branch1][C][N][C][=N][Ring1][#Branch1].[O][=C][Branch1][C][Cl][C][Cl]>>[C][C][=C][C][=C][Branch1][Branch2][N][C][=Branch1][C][=O][C][Cl][C][=N][Ring1][O]
```
[C][C][C][O][C][Ring1][Branch1].[C][C][N][Branch1][Ring1][C][C][C][C].[O]
```
</details>
<details>
<summary><b>Property prediction</b></summary>
- *Please provide the HOMO energy value for this molecule:*
[C][C][O][C][C][Branch1][C][C][C][Branch1][C][C][C]
```
-0.2482
```
</details>
<h3 id="2-2"> 🧬 2.2 Protein-oriented</h3>
<details>
<summary><b>Protein design</b></summary>
- *Design a protein sequence that exhibits the desired activity and specificity.*
1. The presence of Mg(2+) is necessary for the protein to function in the desired environment.
2. The AMP, (6S)-NADPHX binding site should be located in a region of the protein that is accessible to the ligand.
3. The designed protein should have ATP binding, NADPHX epimerase activity, metal ion binding, ADP-dependent NAD(P)H-hydrate dehydratase activity to facilitate nicotinamide nucleotide metabolic process.
4. For general function, the protein need meet that Catalyzes the epimerization of the S- and R-forms of NAD(P)HX, a damaged form of NAD(P)H that is a result of enzymatic or heat-dependent hydration
```
MSNELVLSREQVRRVDQRAIEAYGVPGIVLMENAGRGAAEIIRAACPSAQRVLIACGPGNNGGDGFVIARHLANAGWMVELLLACPADRITGDAQGNHEIIRRMNLPCAVMADARDLEAANDRFATADVIVDALLGTGASGPPREPIASLIRAINEAHRRVSAQPAPSVFAVDIPSGLDCDTGEAANPTVRADHTITFVARKIGFRNPAARDLLGRVHVVDIGAPRAAIQDALTGKSG
```
</details>
<details>
<summary><b>Catalytic activity prediction</b></summary>
- *Please evaluate the following protein sequence and provide an explanation of the enzyme's catalytic activity, including the chemical reaction it facilitates:* MDKVAVAGFLPEELCASLSLSPSFRGNQIFQWIGKGVDSFDAMTNLSAELRASLAEKAILRSTRVSDVLKADDGTVKLQIQTEDDLAVETVLLTDKAARKTACVSCQAGCAMGCAFCKTGTLGLARNLSAAEIVEQFLYLEKHAGALDNIVFMGMGEPLLNLDALRKAIAVLTDKRGRNLSSRRITVSTVGIVSGIYDLANNGPDVRLAVSLTTADETLRRELMPASLTNPLSDLRQAISYYIEKTGKRVTLEAVLLSGKNTSEKNADSLIAFAKGLDVHVNLIPWNPVEGLSFVTPDPEETAQFVSRLEKGGLNVTLRMHRGKSISGACGQLGKTNPYA
```
Based on the provided protein sequence, the enzyme appears to facilitate the chemical reaction: adenosine(37) in tRNA + 2 reduced [2Fe-2S]-[ferredoxin] + 2 S- adenosyl-L-methionine = 2-methyladenosine(37) in tRNA + 5'- deoxyadenosine + L-methionine + 2 oxidized [2Fe-2S]-[ferredoxin] + S- adenosyl-L-homocysteine.
```
</details>
<details>
<summary><b>Protein function prediction</b></summary>
- *Analyze the following amino acid sequence, and determine the function of the resulting protein, its subcellular localization, and any biological processes it may be part of:*
MNGTVNASAPSKMSEVAVERLSNDKALKVIFVLGGPGSGKGTQCAKIAKHFGFTHLSVGDLLRAEINSGSKNGTMIESMINEGKIVRSEVTIKLLQRAMHESGNDKFLIDGFPRNEENRAAFENLEKIEPEFVLFFDCPMEEMERRILNRNQGRDDDKMETIRKRFKVFIESTLPVIEFYNLKGKLYKIDACKPADEVFEDVKAIFSRFRAKEDSSQQTNICTAKRFELVMCLIKRLFREIKRMWSSFFCKAL
```
The protein characterized by the amino acid sequence demonstrates ATP binding, cytidylate kinase activity, uridylate kinase activity and is implicated in the 'de novo' pyrimidine nucleobase biosynthetic process, phosphorylation, pyrimidine nucleotide biosynthetic process. Its subcellular localization is primarily within the cytoplasm, nucleus.
```
</details>
<details>
<summary><b>Functional description generation</b></summary>
- *Examine the given protein sequence and share a brief overview of its attributes:*
MKIVLASNNQGKLAELKAMLAPLGVQLLRQAELGIPEAAEPFRTFVENALAKARHASALSGLPALADDAGLCVEAFGGLPGVDTAFYAVQFGYAKGDANNVKALLAQMAGITDRRAALVSTLVAVRSAEDPEPLIACGRVAGEVALEPMGSNGFGFDPVMFIPEFGQTFAQLPVEVKNANSHRGKATRQMMALMRERWIT
```
A concise description of the protein with the specified amino acid sequence includes: Pyrophosphatase that catalyzes the hydrolysis of nucleoside triphosphates to their monophosphate derivatives, with a high preference for the non-canonical purine nucleotides XTP (xanthosine triphosphate), dITP (deoxyinosine triphosphate) and ITP. Seems to function as a house-cleaning enzyme that removes non-canonical purine nucleotides from the nucleotide pool, thus preventing their incorporation into DNA/RNA and avoiding chromosomal lesions.
```
</details>
<details>
<summary><b>Domain/Motif prediction</b></summary>
- *Given this protein sequence, can you identify any common protein motifs or domains that it may contain?*
MANTKYIFITGGVVSSLGKGIAAASIGALLESRGLSVSLIKVDPYINVDPGTMSPFQHGEVFVTEDGTETDLDLGHYERFVRFKASKKNNFTAGKVYETVIRNERKGNYLGGTVQVIPHITNEIKKRIKKGGQNKDIAIVEVGGTVGDIESQPFVEALRQMALELPNSSWAFVHLTLVPFINASGELKTKPTQHSVKELRSLGISPDVLVCRSEQELPKDEKNKIALFCSVPAKSVISMHDVDTVYSIPILLNKQKVDDTILKKLNLKIKKPNLNDWKRVVKAKLLPEKEVNVSFVGKYTELKDSYKSINEALEHAGIQNKAKVNINFVEAEQITSQNVRKVLKKSDAILVPGGFGERGIEGMILACKYARENNVPYLGICLGMQIAIIEYARNVLKLKSANSTEFDSSTKFPVIGLITEWSDISGKKEKRTKNSDLGGTMRLGGQVCKLKKKSNSYKMYKKSEIIERHRHRYEVNPNYKDKMIEQGLDVVGTSIDGKLVEMIELPSHKWFLACQFHPEFTSNPRDGHPIFNSYIKSTITK
```
Our predictive analysis of the given protein sequence reveals possible domains or motifs. These include: Glutamine amidotransferase, CTP synthase N-terminal domains.
```
</details>
<h3 id="2-3"> 🥼 2.3 Biomolecule text</h3>
<details>
<summary><b>Chemical entity recognition</b></summary>
- *Find and list all the instances of the chemical entities in the following content:*
"Both the control and caramiphen groups with double cannulas had significantly shorter latencies to seizure onset than the corresponding groups with single cannula."
```
caramiphen
```
</details>
<details>
<summary><b>Chemical-disease interaction extraction</b></summary>
- *You are provided with a set of clinical trial summaries. Extract the chemical-disease relations from the summaries and present your findings in the format of (Subject, Object):*
"Eating disorders and the associated behavioural problems and drug abuse are uncommon in pregnancy. When they do occur they are often unrecognized because of denial but when significant may pose a risk to both the mother and her fetus. This case illustrates a number of problems that may be encountered in women with eating disorders in pregnancy, including prolonged and recurrent metabolic disturbances and diuretic abuse. In particular it illustrates the derangements of thyroid function seen in pregnant women with eating disorders and reminds us that when a cause for thyrotoxicosis remains obscure, thyroxine abuse should be considered and explored."
```
(thyroxine, thyrotoxicosis)
```
</details>
<details>
<summary><b>Chemical-protein interaction extraction</b></summary>
- *Your task is to extract the subject and object entities from the provided text and establish the corresponding relation triples. Please format your answers as (Subject, Relation, Object):*
"Opioid receptors are the membrane proteins that mediate the pain-relieving effect of opioid drugs, such as morphine and fentanyl as well as endogenous opioid peptides enkephalins and endorphins. Using cDNAs for the mu and the kappa opioid receptors, we mapped the chromosomal locations of their genes in mouse. Multilocus cross analysis located the mu receptor gene Oprm on Chr 10 and the kappa receptor gene Oprk1 on Chr 1. Both genes are near centromere, with no markers more centromeric. These data indicate that the two opioid receptors are different gene products, ruling out the possibility that they may be differential splicing products from the same gene."
```
(morphine, regulator, Opioid receptors), (fentanyl, regulator, Opioid receptors)
```
</details>
<details>
<summary><b>Multiple-choice question</b></summary>
- *Which of the following conditions does not show multifactorial inheritance? What of the following is the right choice?*
(A) Pyloric stenosis (B) Schizophrenia (C) Spina bifida (neural tube defects) (D) Marfan syndrome
```
The final answer is (D).
```
</details>
<details>
<summary><b>True or False question</b></summary>
- *Does feeding tube insertion and its timing improve survival?*
```
No, neither insertion of PEG tubes nor timing of insertion affect survival.
```
</details>
<details>
<summary><b>Open question</b></summary>
- *What virus causes chronic disease?*
```
Hepatitis C virus (HCV).
```
</details>
<h2 id="3">3. Demo</h2>
<h3 id="3-1"> 🤗 3.1 Model Weight Release</h3>
We release the model weights on Hugging Face at:
- 🔬 *Molecule-oriented instructions:* [zjunlp/llama-molinst-molecule-7b](https://huggingface.co/zjunlp/llama-molinst-molecule-7b)
- 🧬 *Protein-oriented instructions:* [zjunlp/llama-molinst-protein-7b](https://huggingface.co/zjunlp/llama-molinst-protein-7b)
- 🥼 *Biomolecular text instructions:* [zjunlp/llama-molinst-biotext-7b](https://huggingface.co/zjunlp/llama-molinst-biotext-7b)
<h3 id="3-2"> 📝 3.2 Model Usage Guide</h3>
For this part, please refer to our [repository](https://github.com/zjunlp/Mol-Instructions).
We have provided a web version demo based on [Gradio](https://gradio.app). To use it, you first need to download this repository:
```shell
>> git clone https://github.com/zjunlp/Mol-Instruction
>> cd demo
```
Step 1, install Gradio by running：`pip install gradio`.
Step 2, specify the parameters in the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file.
```shell
>> CUDA_VISIBLE_DEVICES=0 python generate.py \
--CLI False\
--protein False\
--load_8bit \
--base_model $BASE_MODEL_PATH \
--share_gradio True\
--lora_weights $FINETUNED_MODEL_PATH \
```
For models fine-tuned on *molecule-oriented* and *biomolecular text* instructions, please set `$FINETUNED_MODEL_PATH` to `'zjunlp/llama-molinst-molecule-7b'` or `'zjunlp/llama-molinst-biotext-7b'`.
For the model fine-tuned on *protein-oriented* instructions, you need to perform additional steps as described in [this folder](https://github.com/zjunlp/Mol-Instructions/tree/main/demo).
Step 3, run the [generate.sh](https://github.com/zjunlp/Mol-Instructions/blob/main/demo/generate.sh) file in the repository：
```shell
>> sh generate.sh
```
We offer two methods: the first one is command-line interaction, and the second one is web-based interaction, which provides greater flexibility.
1. Use the following command to enter **web-based interaction**:
```shell
>> python generate.py
```
The program will run a web server and output an address. Open the output address in a browser to use it.
2. Use the following command to enter **command-line interaction**:
```shell
>> python generate.py --CLI True
```
The disadvantage is the inability to dynamically change decoding parameters.
<h3 id="3-3"> 💡 3.3 FAQ</h3>
- *Question:* What action should be taken if the model encounters `<unk>` and subsequently repeats the input during decoding?
*Answer:* Consider reducing the value of the `max tokens`.
- *Question:* What should I do if the model encounters � during decoding?
*Answer:* If this symbol emerges in the middle of the decoded sentence, we recommend changing the input. If it shows up at the end of the sentence, you can tackle this issue by extending the output length.
- *Question:* Why do I receive varied results despite using identical decoding parameters?
*Answer:* This might occur if you have enabled `do_sample=True`. Another factor could be the order in which tasks are executed. A useful approach would be to use a for loop to generate multiple outputs with the same decoding parameters, enabling you to note the variance in each output.
- *Question:* What could be the reason for subpar answer quality?
*Answer:* Modifying the decoding parameters could help in improving the quality of the extraction or the answer.
<h2 id="4">4. Notices</h2>
<h3 id="4-1"> 🚨 4.1. Usage and License</h3>
Please note that all data and model weights of **Mol-Instructions** is exclusively licensed for research purposes. The accompanying dataset is licensed under CC BY 4.0, which permits solely non-commercial usage.
We emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly **forbidden**.
In terms of dataset maintenance, we pledge our commitment to provide necessary upkeep. This will ensure the continued relevance and usability of the dataset in light of evolving research landscapes. This commitment encompasses regular updates, error checks, and amendments in accordance with field advancements and user feedback.
<h3 id="4-2"> ❗️ 4.2. Limitations</h3>
The current state of the model, obtained via instruction tuning, is a preliminary demonstration. Its capacity to handle real-world, production-grade tasks remains limited. Moreover, there is a vast reservoir of rich instruction data that remains to be collected and exploited.
<h2 id="5">5. About</h2>
<h3 id="5-1"> 📚 5.1 References</h3>
If you use our repository, please cite the following related paper:
```
@article{molinst,
title={Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models},
author={Fang, Yin and Liang, Xiaozhuan and Zhang, Ningyu and Liu, Kangwei and Huang, Rui and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
journal={arXiv preprint arXiv:2306.08018},
year={2023}
}
```
<h3 id="5-2"> 🫱🏻🫲 5.2 Acknowledgements</h3>
We appreciate [LLaMA](https://github.com/facebookresearch/llama), [Huggingface Transformers Llama](https://github.com/huggingface/transformers/tree/main/src/transformers/models/llama), [Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [Alpaca-LoRA](https://github.com/tloen/alpaca-lora), [Chatbot Service](https://github.com/deep-diver/LLM-As-Chatbot) and many other related works for their open-source contributions. |
Patt/HellaSwag_TH_drop | 2023-07-20T15:26:47.000Z | [
"language:th",
"language:en",
"arxiv:1907.04307",
"region:us"
] | Patt | null | null | null | 0 | 71 | ---
language:
- th
- en
---
# Dataset Card for HellaSwag_TH_drop
### Dataset Description
This dataset is Thai translated version of [hellaswag](https://huggingface.co/datasets/hellaswag) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
The score was penalized by the length of original text compare to translated text. The row that any score < 0.5 was dropped.
### Languages
- EN
- TH |
roszcz/qrs-swipe-demo | 2023-08-03T17:06:49.000Z | [
"region:us"
] | roszcz | null | null | null | 0 | 71 | ---
dataset_info:
features:
- name: time
dtype: timestamp[us]
- name: label
dtype: string
- name: position
dtype: int64
- name: is_beat
dtype: bool
- name: signal
dtype:
array2_d:
shape:
- 1200
- 3
dtype: int16
- name: exam_uid
dtype: string
- name: to_review
dtype: bool
splits:
- name: train
num_bytes: 24130500
num_examples: 2000
download_size: 10055162
dataset_size: 24130500
---
# Dataset Card for "qrs-swipe-demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pie/conll2012_ontonotesv5 | 2023-09-28T18:14:28.000Z | [
"region:us"
] | pie | null | null | null | 0 | 71 | Entry not found |
ChrisHayduk/Llama-2-SQL-Dataset | 2023-09-29T03:03:30.000Z | [
"region:us"
] | ChrisHayduk | null | null | null | 6 | 71 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 33020750.12130776
num_examples: 70719
- name: eval
num_bytes: 3669127.878692238
num_examples: 7858
download_size: 10125848
dataset_size: 36689878.0
---
# Dataset Card for "Llama-2-SQL-Dataset"
This dataset is deprecated in favor of [ChrisHayduk/Llama-2-SQL-and-Code-Dataset](https://huggingface.co/datasets/ChrisHayduk/Llama-2-SQL-and-Code-Dataset) |
rahular/simple-wikipedia | 2023-08-17T17:09:41.000Z | [
"region:us"
] | rahular | null | null | null | 0 | 71 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 144689943
num_examples: 769764
download_size: 86969379
dataset_size: 144689943
---
# simple-wikipedia
Processed, text-only dump of the Simple Wikipedia (English). Contains 23,886,673 words. |
ashhadahsan/amazon_theme | 2023-10-02T17:14:23.000Z | [
"region:us"
] | ashhadahsan | null | null | null | 0 | 71 | ---
dataset_info:
features:
- name: Transcript
dtype: string
- name: Review Theme
dtype: string
splits:
- name: train
num_bytes: 285435
num_examples: 780
download_size: 0
dataset_size: 285435
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "amazon_theme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf | 2023-09-20T14:40:04.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | null | 0 | 71 | ---
pretty_name: Evaluation run of meta-llama/Llama-2-7b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 127 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 14 run(s). Each run can\
\ be found as a specific split in each configuration, the split being named using\
\ the timestamp of the run.The \"train\" split is always pointing to the latest\
\ results.\n\nAn additional configuration \"results\" store all the aggregated results\
\ of the run (and is used to compute and display the agregated metrics on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-20T14:39:46.791628](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf/blob/main/results_2023-09-20T14-39-46.791628.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0012583892617449664,\n\
\ \"em_stderr\": 0.00036305608931194434,\n \"f1\": 0.055925964765100665,\n\
\ \"f1_stderr\": 0.0013181664771628632,\n \"acc\": 0.4057988012013119,\n\
\ \"acc_stderr\": 0.00970458141675358\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931194434,\n\
\ \"f1\": 0.055925964765100665,\n \"f1_stderr\": 0.0013181664771628632\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0712661106899166,\n \
\ \"acc_stderr\": 0.007086462127954491\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-7b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_19T16_35_46.942696
path:
- '**/details_harness|arc:challenge|25_2023-08-19T16:35:46.942696.parquet'
- split: 2023_08_24T09_19_51.585793
path:
- '**/details_harness|arc:challenge|25_2023-08-24T09:19:51.585793.parquet'
- split: 2023_08_29T17_54_59.197645
path:
- '**/details_harness|arc:challenge|25_2023-08-29T17:54:59.197645.parquet'
- split: 2023_09_15T09_53_02.418861
path:
- '**/details_harness|arc:challenge|25_2023-09-15T09-53-02.418861.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-15T09-53-02.418861.parquet'
- config_name: harness_drop_0
data_files:
- split: 2023_09_14T20_50_38.766533
path:
- '**/details_harness|drop|0_2023-09-14T20-50-38.766533.parquet'
- split: 2023_09_15T08_35_01.075146
path:
- '**/details_harness|drop|0_2023-09-15T08-35-01.075146.parquet'
- split: latest
path:
- '**/details_harness|drop|0_2023-09-15T08-35-01.075146.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T17_00_44.389859
path:
- '**/details_harness|drop|3_2023-09-08T17-00-44.389859.parquet'
- split: 2023_09_09T12_32_30.613622
path:
- '**/details_harness|drop|3_2023-09-09T12-32-30.613622.parquet'
- split: 2023_09_20T14_39_46.791628
path:
- '**/details_harness|drop|3_2023-09-20T14-39-46.791628.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-20T14-39-46.791628.parquet'
- config_name: harness_gsm8k_0
data_files:
- split: 2023_09_15T08_35_01.075146
path:
- '**/details_harness|gsm8k|0_2023-09-15T08-35-01.075146.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|0_2023-09-15T08-35-01.075146.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_08T17_00_44.389859
path:
- '**/details_harness|gsm8k|5_2023-09-08T17-00-44.389859.parquet'
- split: 2023_09_09T12_32_30.613622
path:
- '**/details_harness|gsm8k|5_2023-09-09T12-32-30.613622.parquet'
- split: 2023_09_20T14_39_46.791628
path:
- '**/details_harness|gsm8k|5_2023-09-20T14-39-46.791628.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-20T14-39-46.791628.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_19T16_35_46.942696
path:
- '**/details_harness|hellaswag|10_2023-08-19T16:35:46.942696.parquet'
- split: 2023_08_24T09_19_51.585793
path:
- '**/details_harness|hellaswag|10_2023-08-24T09:19:51.585793.parquet'
- split: 2023_08_29T17_54_59.197645
path:
- '**/details_harness|hellaswag|10_2023-08-29T17:54:59.197645.parquet'
- split: 2023_09_15T09_53_02.418861
path:
- '**/details_harness|hellaswag|10_2023-09-15T09-53-02.418861.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-15T09-53-02.418861.parquet'
- config_name: harness_hendrycksTest_0
data_files:
- split: 2023_08_21T20_09_03.352670
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:09:03.352670.parquet'
- split: 2023_08_21T20_15_29.093529
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:15:29.093529.parquet'
- split: 2023_08_21T20_20_08.261679
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:20:08.261679.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T20:20:08.261679.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_19T16_35_46.942696
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-19T16:35:46.942696.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T16:35:46.942696.parquet'
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- split: 2023_08_21T17_55_50.567332
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path:
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-15T09-53-02.418861.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-15T09-53-02.418861.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-15T09-53-02.418861.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-15T09-53-02.418861.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-15T09-53-02.418861.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-09-15T09-53-02.418861.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_0
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T16:35:46.942696.parquet'
- split: 2023_08_24T09_19_51.585793
path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-15T09-53-02.418861.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
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- split: 2023_08_24T09_19_51.585793
path:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-15T09-53-02.418861.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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data_files:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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- split: latest
path:
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data_files:
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path:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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- '**/details_original|mmlu:jurisprudence|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_management_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:management|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_marketing_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
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- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_sociology_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:virology|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T19:52:01.926454.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T19_52_01.926454
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T19:52:01.926454.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T19:52:01.926454.parquet'
- config_name: results
data_files:
- split: 2023_08_19T16_35_46.942696
path:
- results_2023-08-19T16:35:46.942696.parquet
- split: 2023_08_21T17_55_50.567332
path:
- results_2023-08-21T17:55:50.567332.parquet
- split: 2023_08_21T20_09_03.352670
path:
- results_2023-08-21T20:09:03.352670.parquet
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- results_2023-08-21T20:15:29.093529.parquet
- split: 2023_08_21T20_20_08.261679
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- results_2023-08-21T20:20:08.261679.parquet
- split: 2023_08_24T09_19_51.585793
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- results_2023-08-24T09:19:51.585793.parquet
- split: 2023_08_28T19_52_01.926454
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- results_2023-08-28T19:52:01.926454.parquet
- split: 2023_08_29T17_54_59.197645
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- results_2023-08-29T17:54:59.197645.parquet
- split: 2023_09_08T17_00_44.389859
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- results_2023-09-08T17-00-44.389859.parquet
- split: 2023_09_09T12_32_30.613622
path:
- results_2023-09-09T12-32-30.613622.parquet
- split: 2023_09_14T20_50_38.766533
path:
- results_2023-09-14T20-50-38.766533.parquet
- split: 2023_09_15T08_35_01.075146
path:
- results_2023-09-15T08-35-01.075146.parquet
- split: 2023_09_15T09_53_02.418861
path:
- results_2023-09-15T09-53-02.418861.parquet
- split: 2023_09_20T14_39_46.791628
path:
- results_2023-09-20T14-39-46.791628.parquet
- split: latest
path:
- results_2023-09-20T14-39-46.791628.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-7b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-7b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 127 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 14 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-20T14:39:46.791628](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-7b-hf/blob/main/results_2023-09-20T14-39-46.791628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.055925964765100665,
"f1_stderr": 0.0013181664771628632,
"acc": 0.4057988012013119,
"acc_stderr": 0.00970458141675358
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.055925964765100665,
"f1_stderr": 0.0013181664771628632
},
"harness|gsm8k|5": {
"acc": 0.0712661106899166,
"acc_stderr": 0.007086462127954491
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
}
}
```
### 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] |
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