id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
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genesisqu/fake-real-news | genesisqu | 2022-10-17T18:06:58Z | 12 | 0 | null | [
"license:bsd",
"region:us"
] | 2022-10-17T18:06:58Z | 2022-10-17T18:06:19.000Z | 2022-10-17T18:06:19 | ---
license: bsd
---
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awacke1/MedNorm2SnomedCT2UMLS | awacke1 | 2023-01-05T14:05:26Z | 12 | 2 | null | [
"license:mit",
"region:us"
] | 2023-01-05T14:05:26Z | 2022-10-17T18:17:16.000Z | 2022-10-17T18:17:16 | ---
license: mit
---
MedNorm2SnomedCT2UMLS
Paper on Mednorm and harmonisation: https://aclanthology.org/W19-3204.pdf
The medical concept normalisation task aims to map textual descriptions to standard terminologies such as SNOMED-CT or MedDRA.
Existing publicly available datasets annotated using different terminologies cannot be simply merged and utilised, and therefore become less
valuable when developing machine learningbased concept normalisation systems.
To address that, we designed a data harmonisation pipeline and engineered a corpus of 27,979 textual descriptions simultaneously mapped to both MedDRA and SNOMED-CT,
sourced from five publicly available datasets across biomedical and social media domains.
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BioBlast3r/Train-01-Maxx | BioBlast3r | 2022-10-18T05:13:35Z | 12 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-18T05:13:35Z | 2022-10-18T05:12:58.000Z | 2022-10-18T05:12:58 | ---
license: unknown
---
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dargod/damian | dargod | 2022-10-18T17:34:01Z | 12 | 0 | null | [
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awacke1/SNOMED-CT-Code-Value-Semantic-Set.csv | awacke1 | 2022-10-29T12:42:02Z | 12 | 3 | null | [
"license:mit",
"region:us"
] | 2022-10-29T12:42:02Z | 2022-10-18T18:19:41.000Z | 2022-10-18T18:19:41 | ---
license: mit
---
SNOMED-CT-Code-Value-Semantic-Set.csv | [
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awacke1/eCQM-Code-Value-Semantic-Set.csv | awacke1 | 2022-10-29T12:40:54Z | 12 | 1 | null | [
"license:mit",
"region:us"
] | 2022-10-29T12:40:54Z | 2022-10-18T18:48:30.000Z | 2022-10-18T18:48:30 | ---
license: mit
---
eCQM-Code-Value-Semantic-Set.csv | [
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liquidinstinct/autotrain-data-video-2-fly | liquidinstinct | 2023-09-19T21:04:27Z | 12 | 0 | null | [
"region:us"
] | 2023-09-19T21:04:27Z | 2022-10-19T03:38:41.000Z | 2022-10-19T03:38:41 | Entry not found | [
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Adapting/abstract-keyphrases | Adapting | 2022-11-20T14:20:50Z | 12 | 0 | null | [
"license:mit",
"region:us"
] | 2022-11-20T14:20:50Z | 2022-10-19T06:54:41.000Z | 2022-10-19T06:54:41 | ---
license: mit
dataset_info:
features:
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dtype: string
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dtype: string
splits:
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num_examples: 20
download_size: 93062
dataset_size: 118255.0
---
preprocessing: https://colab.research.google.com/drive/1dbiApU33FBwAfxwlGBK00qAkbUsS9iae?usp=sharing | [
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kunwarsaaim/AntiBiasDataset | kunwarsaaim | 2022-10-19T07:23:04Z | 12 | 0 | null | [
"license:mit",
"region:us"
] | 2022-10-19T07:23:04Z | 2022-10-19T07:15:09.000Z | 2022-10-19T07:15:09 | ---
license: mit
---
# Dataset from the paper [Debiasing Pre-Trained Language Models via Efficient Fine-Tuning](https://aclanthology.org/2022.ltedi-1.8/)
------------------------
The dataset is formed by combining two different datasets: [WinoBias](https://github.com/uclanlp/corefBias) and [CrowS-Pairs](https://github.com/nyu-mll/crows-pairs) | [
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truongpdd/viwiki-dummy | truongpdd | 2022-10-19T07:29:55Z | 12 | 0 | null | [
"region:us"
] | 2022-10-19T07:29:55Z | 2022-10-19T07:29:10.000Z | 2022-10-19T07:29:10 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 507670455
num_examples: 491
download_size: 246069772
dataset_size: 507670455
---
# Dataset Card for "viwiki-dummy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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israel/AOHWR | israel | 2022-10-21T15:47:54Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T15:47:54Z | 2022-10-19T12:54:42.000Z | 2022-10-19T12:54:42 | # Test | [
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mariosasko/cities_test | mariosasko | 2023-05-04T18:22:14Z | 12 | 0 | null | [
"region:us"
] | 2023-05-04T18:22:14Z | 2022-10-19T13:25:37.000Z | 2022-10-19T13:25:37 | Entry not found | [
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mbazaNLP/Kinyarwanda_English_parallel_dataset | mbazaNLP | 2023-04-08T15:01:28Z | 12 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-04-08T15:01:28Z | 2022-10-19T15:40:28.000Z | 2022-10-19T15:40:28 | ---
license: cc-by-4.0
extra_gated_prompt: "You agree to not attempt to determine the identity of individuals in this dataset"
extra_gated_fields:
Company: text
Country: text
Email: text
I agree to use this model for non-commercial use ONLY: checkbox
---
## Kinyarwanda-English parallel text
This dataset contains 55,000 Kinyarwanda-English sentence pairs, obtained by scraping web data from religious sources such as:
[Bible](https://servervideos.hopto.org/XMLBible/EnglishKJBible.xml)
[Quran](https://quranenc.com/en/home/download/csv/kinyarwanda_assoc)
This dataset has not been curated only cleaned.
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pierro/sung | pierro | 2022-10-20T04:15:32Z | 12 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | 2022-10-20T04:15:32Z | 2022-10-20T04:12:36.000Z | 2022-10-20T04:12:36 | ---
license: creativeml-openrail-m
---
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cjvt/slo_thesaurus | cjvt | 2022-10-20T12:23:03Z | 12 | 0 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
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"language:sl",
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"sopomenke",
"synonyms",
"region:us"
] | 2022-10-20T12:23:03Z | 2022-10-20T05:56:11.000Z | 2022-10-20T05:56:11 | ---
annotations_creators:
- machine-generated
language:
- sl
language_creators:
- machine-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Thesaurus of Modern Slovene 1.0
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- sopomenke
- synonyms
task_categories:
- other
task_ids: []
---
# Dataset Card for Thesaurus of Modern Slovene 1.0
Also known as "Sopomenke 1.0". Available in application form online: https://viri.cjvt.si/sopomenke/slv/.
### Dataset Summary
This is an automatically created Slovene thesaurus from Slovene data available in a comprehensive English–Slovenian dictionary, a monolingual dictionary, and a corpus. A network analysis on the bilingual dictionary word co-occurrence graph was used, together with additional information from the distributional thesaurus data available as part of the Sketch Engine tool and extracted from the 1.2 billion word Gigafida corpus and the monolingual dictionary.
For a detailed description of the data, please see the paper Krek et al. (2017).
### Supported Tasks and Leaderboards
Other (the data is a knowledge base).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
Each entry is stored in its own instance. The following instance contains the metadata for the `headword` "abeceda" (EN: "alphabet").
```
{
'id_headword': 'th.12',
'headword': 'abeceda',
'groups_core': [],
'groups_near': [
{
'id_words': ['th.12.1', 'th.12.2'],
'words': ['pisava', 'črkopis'],
'scores': [0.3311710059642792, 0.3311710059642792],
'domains': [['jezikoslovje'], ['jezikoslovje']]
}
]
}
```
### Data Fields
- `id_headword`: a string ID of the word;
- `headword`: the word whose synonyms are grouped in the instance;
- `groups_core`: groups of likely synonyms - each group contains the IDs of the words (`id_words`), the synonyms (`words`), and how strong the synonym relation (`scores`) is. Some groups also have domains annotated (`domains`, >= 1 per word, i.e. `domains` is a list of lists);
- `groups_near`: same as `groups_near`, but the synonyms here are typically less likely to be exact synonyms and more likely to be otherwise similar.
## Additional Information
### Dataset Curators
Simon Krek; et al. (please see http://hdl.handle.net/11356/1166 for the full list).
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@article{krek2017translation,
title={From translation equivalents to synonyms: creation of a Slovene thesaurus using word co-occurrence network analysis},
author={Krek, Simon and Laskowski, Cyprian and Robnik-{\v{S}}ikonja, Marko},
journal={Proceedings of eLex},
pages={93--109},
year={2017}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
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amanneo/enron-mail-corpus-mini | amanneo | 2022-10-20T13:08:21Z | 12 | 0 | null | [
"region:us"
] | 2022-10-20T13:08:21Z | 2022-10-20T06:50:10.000Z | 2022-10-20T06:50:10 | ---
dataset_info:
features:
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num_examples: 4000
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num_examples: 36000
download_size: 2332694
dataset_size: 2058375.2311697626
---
# Dataset Card for "enron-mail-corpus-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Andres12an/AT | Andres12an | 2022-10-20T09:48:34Z | 12 | 0 | null | [
"license:c-uda",
"region:us"
] | 2022-10-20T09:48:34Z | 2022-10-20T09:31:46.000Z | 2022-10-20T09:31:46 | ---
license: c-uda
---
| [
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cjvt/slownet | cjvt | 2022-10-21T12:44:13Z | 12 | 0 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:sl",
"license:cc-by-sa-4.0",
"slownet",
"wordnet",
... | 2022-10-21T12:44:13Z | 2022-10-20T12:26:34.000Z | 2022-10-20T12:26:34 | ---
annotations_creators:
- machine-generated
- expert-generated
language:
- sl
language_creators:
- machine-generated
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Semantic lexicon of Slovene sloWNet
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- slownet
- wordnet
- pwn
task_categories:
- other
task_ids: []
---
# Dataset Card for SloWNet
### Dataset Summary
sloWNet is the Slovene WordNet developed in the expand approach: it contains the complete Princeton WordNet 3.0 and over 70 000 Slovene literals. These literals have been added automatically using different types of existing resources, such as bilingual dictionaries, parallel corpora and Wikipedia. 33 000 literals have been subsequently hand-validated.
For a detailed description of the data, please see the paper Fišer et al. (2012).
### Supported Tasks and Leaderboards
Other (the data is a knowledge base).
### Languages
Slovenian.
## Dataset Structure
### Data Instances
Each synset is stored in its own instance. The following instance represents a synset containing the English synonyms `{'able'}` and Slovene synonyms `{'sposoben', 'zmožen'}`:
```
{
'id': 'eng-30-00001740-a',
'pos': 'a',
'bcs': 3,
'en_synonyms': {
'words': ['able'],
'senses': [1],
'pwnids': ['able%3:00:00::']
},
'sl_synonyms': {
'words': ['sposoben', 'zmožen'],
'is_validated': [False, False]
},
'en_def': "(usually followed by `to') having the necessary means or skill or know-how or authority to do something",
'sl_def': 'N/A',
'en_usages': [
'able to swim',
'she was able to program her computer',
'we were at last able to buy a car',
'able to get a grant for the project'
],
'sl_usages': [],
'ilrs': {
'types': ['near_antonym', 'be_in_state', 'be_in_state', 'eng_derivative', 'eng_derivative'],
'id_synsets': ['eng-30-00002098-a', 'eng-30-05200169-n', 'eng-30-05616246-n', 'eng-30-05200169-n', 'eng-30-05616246-n']
},
'semeval07_cluster': 'able',
'domains': ['quality']
}
```
### Data Fields
- `id`: a string ID of the synset;
- `pos`: part of speech tag of the synset;
- `bcs`: Base Concept Set index (`-1` if not present);
- `en_synonyms`: the English synonyms in the synset - synonym `i` is described with its form (`words[i]`), sense (`senses[i]`), and Princeton WordNet ID (`pwnids[i]`);
- `sl_synonyms`: the Slovene synonyms in the synset - synonym `i` is described with its form (`words[i]`) and a flag marking if its correctness has been manually validated (`is_validated[i]`);
- `en_def`: the English definition (`"N/A"` if not present);
- `sl_def`: the Slovene definition (`"N/A"` if not present);
- `en_usages`: the English examples of usage;
- `sl_usages`: the Slovene examples of usage;
- `ilrs`: internal language relations - relation `i` is described by its type (`types[i]`) and the target synset (`id_synsets[i]`);
- `semeval07_cluster`: string cluster (`"N/A"` if not present);
- `domains`: domains of the synset.
## Additional Information
### Dataset Curators
Darja Fišer.
### Licensing Information
CC BY-SA 4.0
### Citation Information
```
@inproceedings{fiser2012slownet,
title={sloWNet 3.0: development, extension and cleaning},
author={Fi{\v{s}}er, Darja and Novak, Jernej and Erjavec, Toma{\v{z}}},
booktitle={Proceedings of 6th International Global Wordnet Conference (GWC 2012)},
pages={113--117},
year={2012}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
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amanneo/collected-mail-corpus-mini | amanneo | 2022-10-20T13:08:59Z | 12 | 0 | null | [
"region:us"
] | 2022-10-20T13:08:59Z | 2022-10-20T13:08:38.000Z | 2022-10-20T13:08:38 | ---
dataset_info:
features:
- name: id
dtype: float64
- name: email_type
dtype: string
- name: text
dtype: string
- name: mail_length
dtype: int64
splits:
- name: test
num_bytes: 4260.131707317073
num_examples: 21
- name: train
num_bytes: 37326.86829268293
num_examples: 184
download_size: 26719
dataset_size: 41587.0
---
# Dataset Card for "collected-mail-corpus-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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research-backup/semeval2012_relational_similarity_v4 | research-backup | 2022-10-21T10:13:46Z | 12 | 0 | null | [
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | 2022-10-21T10:13:46Z | 2022-10-20T15:21:19.000Z | 2022-10-20T15:21:19 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
pretty_name: SemEval2012 task 2 Relational Similarity
---
# Dataset Card for "relbert/semeval2012_relational_similarity_v4"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/)
- **Dataset:** SemEval2012: Relational Similarity
### Dataset Summary
***IMPORTANT***: This is the same dataset as [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity),
but with a different dataset construction.
Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
The relation types are constructed on top of following 10 parent relation types.
```shell
{
1: "Class Inclusion", # Hypernym
2: "Part-Whole", # Meronym, Substance Meronym
3: "Similar", # Synonym, Co-hypornym
4: "Contrast", # Antonym
5: "Attribute", # Attribute, Event
6: "Non Attribute",
7: "Case Relation",
8: "Cause-Purpose",
9: "Space-Time",
10: "Representation"
}
```
Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'relation_type': '8d',
'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ]
}
```
### Data Splits
| name |train|validation|
|---------|----:|---------:|
|semeval2012_relational_similarity| 89 | 89|
### Number of Positive/Negative Word-pairs in each Split
| | positives | negatives |
|:--------------------------------------------|------------:|------------:|
| ('1', 'parent', 'train') | 88 | 544 |
| ('1', 'parent', 'validation') | 22 | 136 |
| ('10', 'parent', 'train') | 48 | 584 |
| ('10', 'parent', 'validation') | 12 | 146 |
| ('10a', 'child', 'train') | 8 | 1324 |
| ('10a', 'child', 'validation') | 2 | 331 |
| ('10a', 'child_prototypical', 'train') | 97 | 1917 |
| ('10a', 'child_prototypical', 'validation') | 26 | 521 |
| ('10b', 'child', 'train') | 8 | 1325 |
| ('10b', 'child', 'validation') | 2 | 331 |
| ('10b', 'child_prototypical', 'train') | 90 | 1558 |
| ('10b', 'child_prototypical', 'validation') | 27 | 469 |
| ('10c', 'child', 'train') | 8 | 1327 |
| ('10c', 'child', 'validation') | 2 | 331 |
| ('10c', 'child_prototypical', 'train') | 85 | 1640 |
| ('10c', 'child_prototypical', 'validation') | 20 | 390 |
| ('10d', 'child', 'train') | 8 | 1328 |
| ('10d', 'child', 'validation') | 2 | 331 |
| ('10d', 'child_prototypical', 'train') | 77 | 1390 |
| ('10d', 'child_prototypical', 'validation') | 22 | 376 |
| ('10e', 'child', 'train') | 8 | 1329 |
| ('10e', 'child', 'validation') | 2 | 332 |
| ('10e', 'child_prototypical', 'train') | 67 | 884 |
| ('10e', 'child_prototypical', 'validation') | 20 | 234 |
| ('10f', 'child', 'train') | 8 | 1328 |
| ('10f', 'child', 'validation') | 2 | 331 |
| ('10f', 'child_prototypical', 'train') | 80 | 1460 |
| ('10f', 'child_prototypical', 'validation') | 19 | 306 |
| ('1a', 'child', 'train') | 8 | 1324 |
| ('1a', 'child', 'validation') | 2 | 331 |
| ('1a', 'child_prototypical', 'train') | 106 | 1854 |
| ('1a', 'child_prototypical', 'validation') | 17 | 338 |
| ('1b', 'child', 'train') | 8 | 1324 |
| ('1b', 'child', 'validation') | 2 | 331 |
| ('1b', 'child_prototypical', 'train') | 95 | 1712 |
| ('1b', 'child_prototypical', 'validation') | 28 | 480 |
| ('1c', 'child', 'train') | 8 | 1327 |
| ('1c', 'child', 'validation') | 2 | 331 |
| ('1c', 'child_prototypical', 'train') | 80 | 1528 |
| ('1c', 'child_prototypical', 'validation') | 25 | 502 |
| ('1d', 'child', 'train') | 8 | 1323 |
| ('1d', 'child', 'validation') | 2 | 330 |
| ('1d', 'child_prototypical', 'train') | 112 | 2082 |
| ('1d', 'child_prototypical', 'validation') | 23 | 458 |
| ('1e', 'child', 'train') | 8 | 1329 |
| ('1e', 'child', 'validation') | 2 | 332 |
| ('1e', 'child_prototypical', 'train') | 63 | 775 |
| ('1e', 'child_prototypical', 'validation') | 24 | 256 |
| ('2', 'parent', 'train') | 80 | 552 |
| ('2', 'parent', 'validation') | 20 | 138 |
| ('2a', 'child', 'train') | 8 | 1324 |
| ('2a', 'child', 'validation') | 2 | 330 |
| ('2a', 'child_prototypical', 'train') | 93 | 1885 |
| ('2a', 'child_prototypical', 'validation') | 36 | 736 |
| ('2b', 'child', 'train') | 8 | 1327 |
| ('2b', 'child', 'validation') | 2 | 331 |
| ('2b', 'child_prototypical', 'train') | 86 | 1326 |
| ('2b', 'child_prototypical', 'validation') | 19 | 284 |
| ('2c', 'child', 'train') | 8 | 1325 |
| ('2c', 'child', 'validation') | 2 | 331 |
| ('2c', 'child_prototypical', 'train') | 96 | 1773 |
| ('2c', 'child_prototypical', 'validation') | 21 | 371 |
| ('2d', 'child', 'train') | 8 | 1328 |
| ('2d', 'child', 'validation') | 2 | 331 |
| ('2d', 'child_prototypical', 'train') | 79 | 1329 |
| ('2d', 'child_prototypical', 'validation') | 20 | 338 |
| ('2e', 'child', 'train') | 8 | 1327 |
| ('2e', 'child', 'validation') | 2 | 331 |
| ('2e', 'child_prototypical', 'train') | 82 | 1462 |
| ('2e', 'child_prototypical', 'validation') | 23 | 463 |
| ('2f', 'child', 'train') | 8 | 1327 |
| ('2f', 'child', 'validation') | 2 | 331 |
| ('2f', 'child_prototypical', 'train') | 88 | 1869 |
| ('2f', 'child_prototypical', 'validation') | 17 | 371 |
| ('2g', 'child', 'train') | 8 | 1323 |
| ('2g', 'child', 'validation') | 2 | 330 |
| ('2g', 'child_prototypical', 'train') | 108 | 1925 |
| ('2g', 'child_prototypical', 'validation') | 27 | 480 |
| ('2h', 'child', 'train') | 8 | 1327 |
| ('2h', 'child', 'validation') | 2 | 331 |
| ('2h', 'child_prototypical', 'train') | 84 | 1540 |
| ('2h', 'child_prototypical', 'validation') | 21 | 385 |
| ('2i', 'child', 'train') | 8 | 1328 |
| ('2i', 'child', 'validation') | 2 | 332 |
| ('2i', 'child_prototypical', 'train') | 72 | 1335 |
| ('2i', 'child_prototypical', 'validation') | 21 | 371 |
| ('2j', 'child', 'train') | 8 | 1328 |
| ('2j', 'child', 'validation') | 2 | 331 |
| ('2j', 'child_prototypical', 'train') | 80 | 1595 |
| ('2j', 'child_prototypical', 'validation') | 19 | 369 |
| ('3', 'parent', 'train') | 64 | 568 |
| ('3', 'parent', 'validation') | 16 | 142 |
| ('3a', 'child', 'train') | 8 | 1327 |
| ('3a', 'child', 'validation') | 2 | 331 |
| ('3a', 'child_prototypical', 'train') | 87 | 1597 |
| ('3a', 'child_prototypical', 'validation') | 18 | 328 |
| ('3b', 'child', 'train') | 8 | 1327 |
| ('3b', 'child', 'validation') | 2 | 331 |
| ('3b', 'child_prototypical', 'train') | 87 | 1833 |
| ('3b', 'child_prototypical', 'validation') | 18 | 407 |
| ('3c', 'child', 'train') | 8 | 1326 |
| ('3c', 'child', 'validation') | 2 | 331 |
| ('3c', 'child_prototypical', 'train') | 93 | 1664 |
| ('3c', 'child_prototypical', 'validation') | 18 | 315 |
| ('3d', 'child', 'train') | 8 | 1324 |
| ('3d', 'child', 'validation') | 2 | 331 |
| ('3d', 'child_prototypical', 'train') | 101 | 1943 |
| ('3d', 'child_prototypical', 'validation') | 22 | 372 |
| ('3e', 'child', 'train') | 8 | 1332 |
| ('3e', 'child', 'validation') | 2 | 332 |
| ('3e', 'child_prototypical', 'train') | 49 | 900 |
| ('3e', 'child_prototypical', 'validation') | 20 | 368 |
| ('3f', 'child', 'train') | 8 | 1327 |
| ('3f', 'child', 'validation') | 2 | 331 |
| ('3f', 'child_prototypical', 'train') | 90 | 1983 |
| ('3f', 'child_prototypical', 'validation') | 15 | 362 |
| ('3g', 'child', 'train') | 8 | 1331 |
| ('3g', 'child', 'validation') | 2 | 332 |
| ('3g', 'child_prototypical', 'train') | 61 | 1089 |
| ('3g', 'child_prototypical', 'validation') | 14 | 251 |
| ('3h', 'child', 'train') | 8 | 1328 |
| ('3h', 'child', 'validation') | 2 | 331 |
| ('3h', 'child_prototypical', 'train') | 71 | 1399 |
| ('3h', 'child_prototypical', 'validation') | 28 | 565 |
| ('4', 'parent', 'train') | 64 | 568 |
| ('4', 'parent', 'validation') | 16 | 142 |
| ('4a', 'child', 'train') | 8 | 1327 |
| ('4a', 'child', 'validation') | 2 | 331 |
| ('4a', 'child_prototypical', 'train') | 85 | 1766 |
| ('4a', 'child_prototypical', 'validation') | 20 | 474 |
| ('4b', 'child', 'train') | 8 | 1330 |
| ('4b', 'child', 'validation') | 2 | 332 |
| ('4b', 'child_prototypical', 'train') | 66 | 949 |
| ('4b', 'child_prototypical', 'validation') | 15 | 214 |
| ('4c', 'child', 'train') | 8 | 1326 |
| ('4c', 'child', 'validation') | 2 | 331 |
| ('4c', 'child_prototypical', 'train') | 86 | 1755 |
| ('4c', 'child_prototypical', 'validation') | 25 | 446 |
| ('4d', 'child', 'train') | 8 | 1332 |
| ('4d', 'child', 'validation') | 2 | 333 |
| ('4d', 'child_prototypical', 'train') | 46 | 531 |
| ('4d', 'child_prototypical', 'validation') | 17 | 218 |
| ('4e', 'child', 'train') | 8 | 1326 |
| ('4e', 'child', 'validation') | 2 | 331 |
| ('4e', 'child_prototypical', 'train') | 92 | 2021 |
| ('4e', 'child_prototypical', 'validation') | 19 | 402 |
| ('4f', 'child', 'train') | 8 | 1328 |
| ('4f', 'child', 'validation') | 2 | 332 |
| ('4f', 'child_prototypical', 'train') | 72 | 1464 |
| ('4f', 'child_prototypical', 'validation') | 21 | 428 |
| ('4g', 'child', 'train') | 8 | 1324 |
| ('4g', 'child', 'validation') | 2 | 330 |
| ('4g', 'child_prototypical', 'train') | 106 | 2057 |
| ('4g', 'child_prototypical', 'validation') | 23 | 435 |
| ('4h', 'child', 'train') | 8 | 1326 |
| ('4h', 'child', 'validation') | 2 | 331 |
| ('4h', 'child_prototypical', 'train') | 85 | 1787 |
| ('4h', 'child_prototypical', 'validation') | 26 | 525 |
| ('5', 'parent', 'train') | 72 | 560 |
| ('5', 'parent', 'validation') | 18 | 140 |
| ('5a', 'child', 'train') | 8 | 1324 |
| ('5a', 'child', 'validation') | 2 | 331 |
| ('5a', 'child_prototypical', 'train') | 101 | 1876 |
| ('5a', 'child_prototypical', 'validation') | 22 | 439 |
| ('5b', 'child', 'train') | 8 | 1329 |
| ('5b', 'child', 'validation') | 2 | 332 |
| ('5b', 'child_prototypical', 'train') | 70 | 1310 |
| ('5b', 'child_prototypical', 'validation') | 17 | 330 |
| ('5c', 'child', 'train') | 8 | 1327 |
| ('5c', 'child', 'validation') | 2 | 331 |
| ('5c', 'child_prototypical', 'train') | 85 | 1552 |
| ('5c', 'child_prototypical', 'validation') | 20 | 373 |
| ('5d', 'child', 'train') | 8 | 1324 |
| ('5d', 'child', 'validation') | 2 | 330 |
| ('5d', 'child_prototypical', 'train') | 102 | 1783 |
| ('5d', 'child_prototypical', 'validation') | 27 | 580 |
| ('5e', 'child', 'train') | 8 | 1329 |
| ('5e', 'child', 'validation') | 2 | 332 |
| ('5e', 'child_prototypical', 'train') | 68 | 1283 |
| ('5e', 'child_prototypical', 'validation') | 19 | 357 |
| ('5f', 'child', 'train') | 8 | 1327 |
| ('5f', 'child', 'validation') | 2 | 331 |
| ('5f', 'child_prototypical', 'train') | 77 | 1568 |
| ('5f', 'child_prototypical', 'validation') | 28 | 567 |
| ('5g', 'child', 'train') | 8 | 1328 |
| ('5g', 'child', 'validation') | 2 | 332 |
| ('5g', 'child_prototypical', 'train') | 79 | 1626 |
| ('5g', 'child_prototypical', 'validation') | 14 | 266 |
| ('5h', 'child', 'train') | 8 | 1324 |
| ('5h', 'child', 'validation') | 2 | 330 |
| ('5h', 'child_prototypical', 'train') | 109 | 2348 |
| ('5h', 'child_prototypical', 'validation') | 20 | 402 |
| ('5i', 'child', 'train') | 8 | 1324 |
| ('5i', 'child', 'validation') | 2 | 331 |
| ('5i', 'child_prototypical', 'train') | 96 | 2010 |
| ('5i', 'child_prototypical', 'validation') | 27 | 551 |
| ('6', 'parent', 'train') | 64 | 568 |
| ('6', 'parent', 'validation') | 16 | 142 |
| ('6a', 'child', 'train') | 8 | 1324 |
| ('6a', 'child', 'validation') | 2 | 330 |
| ('6a', 'child_prototypical', 'train') | 102 | 1962 |
| ('6a', 'child_prototypical', 'validation') | 27 | 530 |
| ('6b', 'child', 'train') | 8 | 1327 |
| ('6b', 'child', 'validation') | 2 | 331 |
| ('6b', 'child_prototypical', 'train') | 90 | 1840 |
| ('6b', 'child_prototypical', 'validation') | 15 | 295 |
| ('6c', 'child', 'train') | 8 | 1325 |
| ('6c', 'child', 'validation') | 2 | 331 |
| ('6c', 'child_prototypical', 'train') | 90 | 1968 |
| ('6c', 'child_prototypical', 'validation') | 27 | 527 |
| ('6d', 'child', 'train') | 8 | 1328 |
| ('6d', 'child', 'validation') | 2 | 331 |
| ('6d', 'child_prototypical', 'train') | 82 | 1903 |
| ('6d', 'child_prototypical', 'validation') | 17 | 358 |
| ('6e', 'child', 'train') | 8 | 1327 |
| ('6e', 'child', 'validation') | 2 | 331 |
| ('6e', 'child_prototypical', 'train') | 85 | 1737 |
| ('6e', 'child_prototypical', 'validation') | 20 | 398 |
| ('6f', 'child', 'train') | 8 | 1326 |
| ('6f', 'child', 'validation') | 2 | 331 |
| ('6f', 'child_prototypical', 'train') | 87 | 1652 |
| ('6f', 'child_prototypical', 'validation') | 24 | 438 |
| ('6g', 'child', 'train') | 8 | 1326 |
| ('6g', 'child', 'validation') | 2 | 331 |
| ('6g', 'child_prototypical', 'train') | 94 | 1740 |
| ('6g', 'child_prototypical', 'validation') | 17 | 239 |
| ('6h', 'child', 'train') | 8 | 1324 |
| ('6h', 'child', 'validation') | 2 | 330 |
| ('6h', 'child_prototypical', 'train') | 115 | 2337 |
| ('6h', 'child_prototypical', 'validation') | 14 | 284 |
| ('7', 'parent', 'train') | 64 | 568 |
| ('7', 'parent', 'validation') | 16 | 142 |
| ('7a', 'child', 'train') | 8 | 1324 |
| ('7a', 'child', 'validation') | 2 | 331 |
| ('7a', 'child_prototypical', 'train') | 99 | 2045 |
| ('7a', 'child_prototypical', 'validation') | 24 | 516 |
| ('7b', 'child', 'train') | 8 | 1330 |
| ('7b', 'child', 'validation') | 2 | 332 |
| ('7b', 'child_prototypical', 'train') | 69 | 905 |
| ('7b', 'child_prototypical', 'validation') | 12 | 177 |
| ('7c', 'child', 'train') | 8 | 1327 |
| ('7c', 'child', 'validation') | 2 | 331 |
| ('7c', 'child_prototypical', 'train') | 85 | 1402 |
| ('7c', 'child_prototypical', 'validation') | 20 | 313 |
| ('7d', 'child', 'train') | 8 | 1324 |
| ('7d', 'child', 'validation') | 2 | 331 |
| ('7d', 'child_prototypical', 'train') | 98 | 2064 |
| ('7d', 'child_prototypical', 'validation') | 25 | 497 |
| ('7e', 'child', 'train') | 8 | 1328 |
| ('7e', 'child', 'validation') | 2 | 331 |
| ('7e', 'child_prototypical', 'train') | 78 | 1270 |
| ('7e', 'child_prototypical', 'validation') | 21 | 298 |
| ('7f', 'child', 'train') | 8 | 1326 |
| ('7f', 'child', 'validation') | 2 | 331 |
| ('7f', 'child_prototypical', 'train') | 89 | 1377 |
| ('7f', 'child_prototypical', 'validation') | 22 | 380 |
| ('7g', 'child', 'train') | 8 | 1328 |
| ('7g', 'child', 'validation') | 2 | 332 |
| ('7g', 'child_prototypical', 'train') | 72 | 885 |
| ('7g', 'child_prototypical', 'validation') | 21 | 263 |
| ('7h', 'child', 'train') | 8 | 1324 |
| ('7h', 'child', 'validation') | 2 | 331 |
| ('7h', 'child_prototypical', 'train') | 94 | 1479 |
| ('7h', 'child_prototypical', 'validation') | 29 | 467 |
| ('8', 'parent', 'train') | 64 | 568 |
| ('8', 'parent', 'validation') | 16 | 142 |
| ('8a', 'child', 'train') | 8 | 1324 |
| ('8a', 'child', 'validation') | 2 | 331 |
| ('8a', 'child_prototypical', 'train') | 93 | 1640 |
| ('8a', 'child_prototypical', 'validation') | 30 | 552 |
| ('8b', 'child', 'train') | 8 | 1330 |
| ('8b', 'child', 'validation') | 2 | 332 |
| ('8b', 'child_prototypical', 'train') | 61 | 1126 |
| ('8b', 'child_prototypical', 'validation') | 20 | 361 |
| ('8c', 'child', 'train') | 8 | 1326 |
| ('8c', 'child', 'validation') | 2 | 331 |
| ('8c', 'child_prototypical', 'train') | 96 | 1547 |
| ('8c', 'child_prototypical', 'validation') | 15 | 210 |
| ('8d', 'child', 'train') | 8 | 1325 |
| ('8d', 'child', 'validation') | 2 | 331 |
| ('8d', 'child_prototypical', 'train') | 92 | 1472 |
| ('8d', 'child_prototypical', 'validation') | 25 | 438 |
| ('8e', 'child', 'train') | 8 | 1327 |
| ('8e', 'child', 'validation') | 2 | 331 |
| ('8e', 'child_prototypical', 'train') | 87 | 1340 |
| ('8e', 'child_prototypical', 'validation') | 18 | 270 |
| ('8f', 'child', 'train') | 8 | 1326 |
| ('8f', 'child', 'validation') | 2 | 331 |
| ('8f', 'child_prototypical', 'train') | 83 | 1416 |
| ('8f', 'child_prototypical', 'validation') | 28 | 452 |
| ('8g', 'child', 'train') | 8 | 1330 |
| ('8g', 'child', 'validation') | 2 | 332 |
| ('8g', 'child_prototypical', 'train') | 62 | 640 |
| ('8g', 'child_prototypical', 'validation') | 19 | 199 |
| ('8h', 'child', 'train') | 8 | 1324 |
| ('8h', 'child', 'validation') | 2 | 331 |
| ('8h', 'child_prototypical', 'train') | 100 | 1816 |
| ('8h', 'child_prototypical', 'validation') | 23 | 499 |
| ('9', 'parent', 'train') | 72 | 560 |
| ('9', 'parent', 'validation') | 18 | 140 |
| ('9a', 'child', 'train') | 8 | 1324 |
| ('9a', 'child', 'validation') | 2 | 331 |
| ('9a', 'child_prototypical', 'train') | 96 | 1520 |
| ('9a', 'child_prototypical', 'validation') | 27 | 426 |
| ('9b', 'child', 'train') | 8 | 1326 |
| ('9b', 'child', 'validation') | 2 | 331 |
| ('9b', 'child_prototypical', 'train') | 93 | 1783 |
| ('9b', 'child_prototypical', 'validation') | 18 | 307 |
| ('9c', 'child', 'train') | 8 | 1330 |
| ('9c', 'child', 'validation') | 2 | 332 |
| ('9c', 'child_prototypical', 'train') | 59 | 433 |
| ('9c', 'child_prototypical', 'validation') | 22 | 163 |
| ('9d', 'child', 'train') | 8 | 1328 |
| ('9d', 'child', 'validation') | 2 | 332 |
| ('9d', 'child_prototypical', 'train') | 78 | 1683 |
| ('9d', 'child_prototypical', 'validation') | 15 | 302 |
| ('9e', 'child', 'train') | 8 | 1329 |
| ('9e', 'child', 'validation') | 2 | 332 |
| ('9e', 'child_prototypical', 'train') | 66 | 1426 |
| ('9e', 'child_prototypical', 'validation') | 21 | 475 |
| ('9f', 'child', 'train') | 8 | 1328 |
| ('9f', 'child', 'validation') | 2 | 331 |
| ('9f', 'child_prototypical', 'train') | 79 | 1436 |
| ('9f', 'child_prototypical', 'validation') | 20 | 330 |
| ('9g', 'child', 'train') | 8 | 1324 |
| ('9g', 'child', 'validation') | 2 | 331 |
| ('9g', 'child_prototypical', 'train') | 100 | 1685 |
| ('9g', 'child_prototypical', 'validation') | 23 | 384 |
| ('9h', 'child', 'train') | 8 | 1325 |
| ('9h', 'child', 'validation') | 2 | 331 |
| ('9h', 'child_prototypical', 'train') | 95 | 1799 |
| ('9h', 'child_prototypical', 'validation') | 22 | 462 |
| ('9i', 'child', 'train') | 8 | 1328 |
| ('9i', 'child', 'validation') | 2 | 332 |
| ('9i', 'child_prototypical', 'train') | 79 | 1361 |
| ('9i', 'child_prototypical', 'validation') | 14 | 252 |
### Citation Information
```
@inproceedings{jurgens-etal-2012-semeval,
title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
author = "Jurgens, David and
Mohammad, Saif and
Turney, Peter and
Holyoak, Keith",
booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
month = "7-8 " # jun,
year = "2012",
address = "Montr{\'e}al, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S12-1047",
pages = "356--364",
}
``` | [
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devozs/israeli_soccer_news | devozs | 2022-10-22T06:20:33Z | 12 | 0 | null | [
"region:us"
] | 2022-10-22T06:20:33Z | 2022-10-20T16:26:57.000Z | 2022-10-20T16:26:57 | ---
dataset_info:
features:
- name: article_title
dtype: string
- name: article_body
dtype: string
- name: article_body_length
dtype: int64
splits:
- name: train
num_bytes: 8956722.687408645
num_examples: 4310
- name: validation
num_bytes: 995422.3125913552
num_examples: 479
download_size: 4052466
dataset_size: 9952145.0
---
# Dataset Card for "israeli_soccer_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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anhdungitvn/sccr | anhdungitvn | 2022-10-21T03:39:41Z | 12 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-10-21T03:39:41Z | 2022-10-21T03:27:59.000Z | 2022-10-21T03:27:59 | ---
license: apache-2.0
---
```python
from datasets import load_dataset
data_name = "anhdungitvn/sccr"
data_files = {"train": "train.tsv", "eval": "eval.tsv"}
sccr = load_dataset(data_name, data_files=data_files)
sccr
```
```python
DatasetDict({
train: Dataset({
features: ['text', 'labels'],
num_rows: 14478
})
eval: Dataset({
features: ['text', 'labels'],
num_rows: 1609
})
})
```
### References
- <a href="https://www.aivivn.com/contests/6">SC: Sentiment Classification (Phân loại sắc thái bình luận)</a>
| [
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autoevaluate/autoeval-eval-SpaceDoge__dataset_test_1-SpaceDoge__dataset_test_1-a8c4b7-1826662823 | autoevaluate | 2022-10-21T03:39:36Z | 12 | 0 | null | [
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"evaluation",
"region:us"
] | 2022-10-21T03:39:36Z | 2022-10-21T03:36:22.000Z | 2022-10-21T03:36:22 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- SpaceDoge/dataset_test_1
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: SpaceDoge/dataset_test_1
dataset_config: SpaceDoge--dataset_test_1
dataset_split: test
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-1.3b_eval
* Dataset: SpaceDoge/dataset_test_1
* Config: SpaceDoge--dataset_test_1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SpaceDoge](https://huggingface.co/SpaceDoge) for evaluating this model. | [
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"autotrain",
"evaluation",
"region:us"
] | 2022-10-21T03:37:58Z | 2022-10-21T03:36:24.000Z | 2022-10-21T03:36:24 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- SpaceDoge/dataset_test_1
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-350m_eval
metrics: []
dataset_name: SpaceDoge/dataset_test_1
dataset_config: SpaceDoge--dataset_test_1
dataset_split: test
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-350m_eval
* Dataset: SpaceDoge/dataset_test_1
* Config: SpaceDoge--dataset_test_1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SpaceDoge](https://huggingface.co/SpaceDoge) for evaluating this model. | [
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"autotrain",
"evaluation",
"region:us"
] | 2022-10-21T03:41:41Z | 2022-10-21T03:36:27.000Z | 2022-10-21T03:36:27 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- SpaceDoge/dataset_test_1
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: SpaceDoge/dataset_test_1
dataset_config: SpaceDoge--dataset_test_1
dataset_split: test
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: SpaceDoge/dataset_test_1
* Config: SpaceDoge--dataset_test_1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@SpaceDoge](https://huggingface.co/SpaceDoge) for evaluating this model. | [
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no3/azura-vibrant-venture | no3 | 2022-10-21T05:11:10Z | 12 | 0 | null | [
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bizjay/DataTest | bizjay | 2022-10-28T10:43:44Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T10:43:44Z | 2022-10-21T04:21:17.000Z | 2022-10-21T04:21:17 | This is dummy data
license: unknown
---
multilinguality:
- monolingual | [
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salascorp/testTrack | salascorp | 2022-10-21T05:26:44Z | 12 | 0 | null | [
"region:us"
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lcw99/oscar-ko-only | lcw99 | 2022-10-21T05:52:05Z | 12 | 2 | null | [
"language:ko",
"region:us"
] | 2022-10-21T05:52:05Z | 2022-10-21T05:38:36.000Z | 2022-10-21T05:38:36 | ---
language:
- ko
---
# oscar dataset only korean | [
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xuqi/cctvcangbao | xuqi | 2022-10-21T06:03:41Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T06:03:41Z | 2022-10-21T06:03:14.000Z | 2022-10-21T06:03:14 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lcw99/cc100-ko-only | lcw99 | 2022-10-21T07:23:11Z | 12 | 1 | null | [
"language:ko",
"region:us"
] | 2022-10-21T07:23:11Z | 2022-10-21T06:05:16.000Z | 2022-10-21T06:05:16 | ---
language:
- ko
---
# cc100 dataset Korean only | [
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-0.16257910430431366... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mareloraby/uk_acc_1985 | mareloraby | 2022-10-23T22:00:08Z | 12 | 0 | null | [
"region:us"
] | 2022-10-23T22:00:08Z | 2022-10-21T09:22:42.000Z | 2022-10-21T09:22:42 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
polinaeterna/smol | polinaeterna | 2022-10-21T09:27:16Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T09:27:16Z | 2022-10-21T09:27:05.000Z | 2022-10-21T09:27:05 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: test
num_bytes: 28
num_examples: 2
- name: train
num_bytes: 44
num_examples: 2
download_size: 1776
dataset_size: 72
---
# Dataset Card for "smol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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zhxdasini/Jackie | zhxdasini | 2022-10-21T11:41:58Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T11:41:58Z | 2022-10-21T11:41:21.000Z | 2022-10-21T11:41:21 | Entry not found | [
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Gemu/Test-Diffusion | Gemu | 2022-10-21T12:34:07Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T12:34:07Z | 2022-10-21T12:32:42.000Z | 2022-10-21T12:32:42 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Gemu/Test2 | Gemu | 2022-10-21T12:42:17Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T12:42:17Z | 2022-10-21T12:41:24.000Z | 2022-10-21T12:41:24 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Rosenberg/CMeEE | Rosenberg | 2022-10-25T12:30:05Z | 12 | 0 | null | [
"license:mit",
"region:us"
] | 2022-10-25T12:30:05Z | 2022-10-21T13:48:53.000Z | 2022-10-21T13:48:53 | ---
license: mit
---
# Mainfest
- CMeEE_train.json: 训练集
- CMeEE_dev.json: 验证集
- CMeEE_test.json: 测试集
- 提交的时候需要为每条记录填充"entities"字段，类型为列表。每个识别出来的实体必须包含"start_idx", "end_idx", "type"3个字段。
- 提交的文件名为：CMeEE_test.json
- example_gold.json: 标准答案示例
- example_pred.json: 提交结果示例
评估指标以严格Micro-F1值为准 | [
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0.245261967182... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
xuqi/cctvtoy | xuqi | 2022-10-21T13:50:49Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T13:50:49Z | 2022-10-21T13:50:19.000Z | 2022-10-21T13:50:19 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BoXiaohe/bio_name_def | BoXiaohe | 2022-10-21T14:23:35Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T14:23:35Z | 2022-10-21T14:22:04.000Z | 2022-10-21T14:22:04 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/DAWQAS | arbml | 2022-10-21T20:29:07Z | 12 | 0 | null | [
"region:us"
] | 2022-10-21T20:29:07Z | 2022-10-21T20:29:03.000Z | 2022-10-21T20:29:03 | ---
dataset_info:
features:
- name: QID
dtype: string
- name: Site_id
dtype: string
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Answer1
dtype: string
- name: Answer2
dtype: string
- name: Answer3
dtype: string
- name: Answer4
dtype: string
- name: Answer5
dtype: string
- name: Answer6
dtype: string
- name: Answer7
dtype: string
- name: Answer8
dtype: string
- name: Answer9
dtype: string
- name: Answer10
dtype: string
- name: Answer11
dtype: string
- name: Original_Category
dtype: string
- name: Author
dtype: string
- name: Date
dtype: string
- name: Site
dtype: string
- name: Year
dtype: string
splits:
- name: train
num_bytes: 22437661
num_examples: 3209
download_size: 10844359
dataset_size: 22437661
---
# Dataset Card for "DAWQAS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.2635767161846161... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
laion/laion2b-en-vit-h-14-embeddings | laion | 2022-10-25T04:19:39Z | 12 | 3 | null | [
"region:us"
] | 2022-10-25T04:19:39Z | 2022-10-21T23:33:29.000Z | 2022-10-21T23:33:29 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jeffdshen/neqa2_8shot | jeffdshen | 2022-10-23T20:19:39Z | 12 | 0 | null | [
"license:cc-by-2.0",
"region:us"
] | 2022-10-23T20:19:39Z | 2022-10-23T20:19:15.000Z | 2022-10-23T20:19:15 | ---
license: cc-by-2.0
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-jeffdshen__neqa0_8shot-jeffdshen__neqa0_8shot-5a61bc-1852963395 | autoevaluate | 2022-10-23T22:16:35Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-23T22:16:35Z | 2022-10-23T20:59:43.000Z | 2022-10-23T20:59:43 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/neqa0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-6.7b_eval
metrics: []
dataset_name: jeffdshen/neqa0_8shot
dataset_config: jeffdshen--neqa0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-6.7b_eval
* Dataset: jeffdshen/neqa0_8shot
* Config: jeffdshen--neqa0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. | [
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0.0166281610727... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-jeffdshen__neqa0_8shot-jeffdshen__neqa0_8shot-5a61bc-1852963396 | autoevaluate | 2022-10-23T22:56:59Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-23T22:56:59Z | 2022-10-23T20:59:45.000Z | 2022-10-23T20:59:45 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/neqa0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-13b_eval
metrics: []
dataset_name: jeffdshen/neqa0_8shot
dataset_config: jeffdshen--neqa0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-13b_eval
* Dataset: jeffdshen/neqa0_8shot
* Config: jeffdshen--neqa0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. | [
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0.043404... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-jeffdshen__neqa2_8shot-jeffdshen__neqa2_8shot-959823-1853063404 | autoevaluate | 2022-10-23T22:21:29Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-23T22:21:29Z | 2022-10-23T21:00:02.000Z | 2022-10-23T21:00:02 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/neqa2_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-13b_eval
metrics: []
dataset_name: jeffdshen/neqa2_8shot
dataset_config: jeffdshen--neqa2_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-13b_eval
* Dataset: jeffdshen/neqa2_8shot
* Config: jeffdshen--neqa2_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. | [
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0.0267518348991... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263421 | autoevaluate | 2022-10-24T02:54:44Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-24T02:54:44Z | 2022-10-23T22:00:50.000Z | 2022-10-23T22:00:50 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/redefine_math0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: jeffdshen/redefine_math0_8shot
dataset_config: jeffdshen--redefine_math0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-30b_eval
* Dataset: jeffdshen/redefine_math0_8shot
* Config: jeffdshen--redefine_math0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. | [
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0.0103367... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LordDrack1/Kaypea | LordDrack1 | 2022-10-24T23:16:01Z | 12 | 0 | null | [
"region:us"
] | 2022-10-24T23:16:01Z | 2022-10-24T18:54:56.000Z | 2022-10-24T18:54:56 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mathemakitten/winobias_antistereotype_test_cot | mathemakitten | 2022-10-25T16:52:48Z | 12 | 0 | null | [
"region:us"
] | 2022-10-25T16:52:48Z | 2022-10-25T16:52:27.000Z | 2022-10-25T16:52:27 | Entry not found | [
-0.3227649927139282,
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arbml/iSarcasmEval_task_A | arbml | 2022-10-25T16:52:57Z | 12 | 0 | null | [
"region:us"
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arbml/iSarcasmEval_task_C | arbml | 2022-10-25T16:54:31Z | 12 | 0 | null | [
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nishimaki/taiyo | nishimaki | 2022-10-26T02:37:00Z | 12 | 0 | null | [
"license:openrail",
"region:us"
] | 2022-10-26T02:37:00Z | 2022-10-26T02:36:13.000Z | 2022-10-26T02:36:13 | ---
license: openrail
---
| [
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MarkGG/Romance-cleaned-1 | MarkGG | 2022-10-26T03:33:28Z | 12 | 0 | null | [
"region:us"
] | 2022-10-26T03:33:28Z | 2022-10-26T03:33:21.000Z | 2022-10-26T03:33:21 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5388007.848468044
num_examples: 6491
- name: validation
num_bytes: 599313.1515319562
num_examples: 722
download_size: 3844960
dataset_size: 5987321.0
---
# Dataset Card for "Romance-cleaned-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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autoevaluate/autoeval-eval-lener_br-lener_br-d57983-1886264289 | autoevaluate | 2022-10-26T04:40:07Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-26T04:40:07Z | 2022-10-26T04:39:11.000Z | 2022-10-26T04:39:11 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- lener_br
eval_info:
task: entity_extraction
model: Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
metrics: []
dataset_name: lener_br
dataset_config: lener_br
dataset_split: validation
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: Luciano/xlm-roberta-base-finetuned-lener_br-finetuned-lener-br
* Dataset: lener_br
* Config: lener_br
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model. | [
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arbml/CheckThat_AR_Task1 | arbml | 2022-10-26T15:13:07Z | 12 | 0 | null | [
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pmfsl/course-feedback-pt_br | pmfsl | 2022-10-26T21:23:04Z | 12 | 0 | null | [
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jhworth8/baileycardosi | jhworth8 | 2022-10-26T16:01:24Z | 12 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-10-26T16:01:24Z | 2022-10-26T15:56:43.000Z | 2022-10-26T15:56:43 | ---
license: apache-2.0
---
| [
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arbml/buisness_corpora | arbml | 2022-10-26T16:03:49Z | 12 | 0 | null | [
"region:us"
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Twitter/TwitterFaveGraph | Twitter | 2022-10-31T23:58:49Z | 12 | 11 | null | [
"license:cc-by-4.0",
"arxiv:2210.16271",
"region:us"
] | 2022-10-31T23:58:49Z | 2022-10-27T00:44:43.000Z | 2022-10-27T00:44:43 | ---
license: cc-by-4.0
---
# MiCRO: Multi-interest Candidate Retrieval Online
[](http://makeapullrequest.com)
[](https://arxiv.org/abs/2210.16271)
This repo contains the TwitterFaveGraph dataset from our paper [MiCRO: Multi-interest Candidate Retrieval Online](). <br />
[[PDF]](https://arxiv.org/pdf/2210.16271.pdf)
[[HuggingFace Datasets]](https://huggingface.co/Twitter)
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
## TwitterFaveGraph
TwitterFaveGraph is a bipartite directed graph of user nodes to Tweet nodes where an edge represents a "fave" engagement. Each edge is binned into predetermined time chunks which are assigned as ordinals. These ordinals are contiguous and respect time ordering. In total TwitterFaveGraph has 6.7M user nodes, 13M Tweet nodes, and 283M edges. The maximum degree for users is 100 and the minimum degree for users is 1. The maximum
degree for Tweets is 280k and the minimum degree for Tweets is 5.
The data format is displayed below.
| user_index | tweet_index | time_chunk |
| ------------- | ------------- | ---- |
| 1 | 2 | 1 |
| 2 | 1 | 1 |
| 3 | 3 | 2 |
## Citation
If you use TwitterFaveGraph in your work, please cite the following:
```bib
@article{portman2022micro,
title={MiCRO: Multi-interest Candidate Retrieval Online},
author={Portman, Frank and Ragain, Stephen and El-Kishky, Ahmed},
journal={arXiv preprint arXiv:2210.16271},
year={2022}
}
``` | [
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roa7n/G_quad_DNA_tokenized_K6 | roa7n | 2022-10-27T09:36:43Z | 12 | 0 | null | [
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biglam/v4design_europeana_style_dataset | biglam | 2022-10-27T11:14:30Z | 12 | 1 | null | [
"task_categories:image-classification",
"annotations_creators:expert-generated",
"license:other",
"region:us"
] | 2022-10-27T11:14:30Z | 2022-10-27T10:55:55.000Z | 2022-10-27T10:55:55 | ---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- other
multilinguality: []
pretty_name: V4Design Europeana style dataset
size_categories: []
source_datasets: []
tags: []
task_categories:
- image-classification
task_ids: []
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: uri
dtype: string
- name: style
dtype:
class_label:
names:
0: Rococo
1: Baroque
2: Other
- name: rights
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 536168550.923
num_examples: 1613
download_size: 535393230
dataset_size: 536168550.923
---
# Dataset Card for V4Design Europeana style dataset
## 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
This dataset contains:
> 1614 paintings belonging to the categories Baroque, Rococo, and Other. The images were obtained using the Europeana Search API, selecting open objects from the art thematic collection. 24k images were obtained, from which the current dataset was derived. The labels were added by the V4Design team, using a custom annotation tool. As described in the project documentation, other categories were used besides Baroque and Rococo. But for the sake of training a machine learning model we have retained only the categories with a significant number of annotations [source](https://zenodo.org/record/4896487)
This version of the dataset is generated using the [CSV file](https://zenodo.org/record/4896487) hosted on Zenodo. This CSV file contains the labels with URLs for the relevant images. Some of these URLs no longer resolve to an image. For consitency with the original dataset and if these URLs become valid again, these rows of the data are preserved here. If you want only successfully loaded images in your dataset, you can filter out the missing images as follows.
```python
ds = ds.filter(lambda x: x['image'] is not None)
```
### Supported Tasks and Leaderboards
This dataset is primarily intended for `image-classification`.
### 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
```
@dataset{europeana_2021_4896487,
author = {Europeana and
V4Design},
title = {V4Design/Europeana style dataset},
month = jun,
year = 2021,
publisher = {Zenodo},
doi = {10.5281/zenodo.4896487},
url = {https://doi.org/10.5281/zenodo.4896487}
}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
| [
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chloeliu/reddit_nosleep_posts | chloeliu | 2022-10-27T15:34:53Z | 12 | 0 | null | [
"license:unknown",
"region:us"
] | 2022-10-27T15:34:53Z | 2022-10-27T15:33:38.000Z | 2022-10-27T15:33:38 | ---
license: unknown
---
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autoevaluate/autoeval-eval-ARTeLab__ilpost-ARTeLab__ilpost-d2ea00-1904764775 | autoevaluate | 2022-10-27T15:44:41Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-27T15:44:41Z | 2022-10-27T15:40:50.000Z | 2022-10-27T15:40:50 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ARTeLab/ilpost
eval_info:
task: summarization
model: ARTeLab/it5-summarization-ilpost
metrics: ['bertscore']
dataset_name: ARTeLab/ilpost
dataset_config: ARTeLab--ilpost
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ARTeLab/it5-summarization-ilpost
* Dataset: ARTeLab/ilpost
* Config: ARTeLab--ilpost
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@morenolq](https://huggingface.co/morenolq) for evaluating this model. | [
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autoevaluate/autoeval-eval-ARTeLab__fanpage-ARTeLab__fanpage-6c7fce-1904864776 | autoevaluate | 2022-10-27T15:47:53Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-27T15:47:53Z | 2022-10-27T15:40:56.000Z | 2022-10-27T15:40:56 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ARTeLab/fanpage
eval_info:
task: summarization
model: ARTeLab/it5-summarization-fanpage
metrics: ['bertscore']
dataset_name: ARTeLab/fanpage
dataset_config: ARTeLab--fanpage
dataset_split: test
col_mapping:
text: source
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: ARTeLab/it5-summarization-fanpage
* Dataset: ARTeLab/fanpage
* Config: ARTeLab--fanpage
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@morenolq](https://huggingface.co/morenolq) for evaluating this model. | [
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hasanriaz121/reqs | hasanriaz121 | 2022-10-27T18:06:50Z | 12 | 0 | null | [
"region:us"
] | 2022-10-27T18:06:50Z | 2022-10-27T18:05:57.000Z | 2022-10-27T18:05:57 | ---
dataset_info:
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---
# Dataset Card for "reqs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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---
# Dataset Card for "tydiqa_secondary_task"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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vicm0r/eurosat | vicm0r | 2022-10-28T00:17:56Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T00:17:56Z | 2022-10-28T00:17:50.000Z | 2022-10-28T00:17:50 | ---
dataset_info:
features:
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splits:
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---
# Dataset Card for "eurosat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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carlosejimenez/mrpc_corpus | carlosejimenez | 2022-10-28T00:26:38Z | 12 | 0 | null | [
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carlosejimenez/qnli_corpus | carlosejimenez | 2022-10-28T00:26:49Z | 12 | 0 | null | [
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carlosejimenez/qqp_corpus | carlosejimenez | 2022-11-08T16:01:09Z | 12 | 0 | null | [
"region:us"
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carlosejimenez/stsb_corpus | carlosejimenez | 2022-10-28T00:27:31Z | 12 | 0 | null | [
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carlosejimenez/wnli_corpus | carlosejimenez | 2022-10-28T00:27:37Z | 12 | 0 | null | [
"region:us"
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nixjoe/2d-style-test | nixjoe | 2022-10-28T01:11:33Z | 12 | 0 | null | [
"region:us"
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randomwalksky/shoes20 | randomwalksky | 2022-10-28T01:32:51Z | 12 | 1 | null | [
"license:openrail",
"region:us"
] | 2022-10-28T01:32:51Z | 2022-10-28T01:23:11.000Z | 2022-10-28T01:23:11 | ---
license: openrail
---
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laspace/photosofwade | laspace | 2022-10-28T02:15:22Z | 12 | 0 | null | [
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xixixi/images | xixixi | 2022-10-28T01:41:32Z | 12 | 0 | null | [
"license:other",
"region:us"
] | 2022-10-28T01:41:32Z | 2022-10-28T01:40:28.000Z | 2022-10-28T01:40:28 | ---
license: other
---
| [
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TeddyCat/Human_obj_bg | TeddyCat | 2022-12-18T05:02:54Z | 12 | 1 | null | [
"region:us"
] | 2022-12-18T05:02:54Z | 2022-10-28T03:25:32.000Z | 2022-10-28T03:25:32 | ---
dataset_info:
features:
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dtype: image
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dtype: image
splits:
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num_bytes: 350102.0
num_examples: 20
download_size: 337556
dataset_size: 350102.0
---
# Dataset Card for "Human_obj_bg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-6c03d1-1913164906 | autoevaluate | 2022-10-28T04:21:46Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-28T04:21:46Z | 2022-10-28T04:06:36.000Z | 2022-10-28T04:06:36 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v1
eval_info:
task: text_zero_shot_classification
model: facebook/opt-6.7b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v1
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v1
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-6.7b
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1
* Config: mathemakitten--winobias_antistereotype_test_cot_v1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-6c03d1-1913164909 | autoevaluate | 2022-10-28T06:25:07Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-28T06:25:07Z | 2022-10-28T04:06:37.000Z | 2022-10-28T04:06:37 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v1
eval_info:
task: text_zero_shot_classification
model: facebook/opt-66b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v1
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v1
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-66b
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1
* Config: mathemakitten--winobias_antistereotype_test_cot_v1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-6c03d1-1913164903 | autoevaluate | 2022-10-28T04:08:28Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-28T04:08:28Z | 2022-10-28T04:06:37.000Z | 2022-10-28T04:06:37 | ---
type: predictions
tags:
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datasets:
- mathemakitten/winobias_antistereotype_test_cot_v1
eval_info:
task: text_zero_shot_classification
model: ArthurZ/opt-350m
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v1
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v1
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: ArthurZ/opt-350m
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1
* Config: mathemakitten--winobias_antistereotype_test_cot_v1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-6c03d1-1913164908 | autoevaluate | 2022-10-28T05:06:39Z | 12 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-28T05:06:39Z | 2022-10-28T04:06:46.000Z | 2022-10-28T04:06:46 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v1
eval_info:
task: text_zero_shot_classification
model: facebook/opt-30b
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v1
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v1
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-30b
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1
* Config: mathemakitten--winobias_antistereotype_test_cot_v1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. | [
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0.35079225897789,
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-0.6300886869430542,
-0.13564702868461... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MarkGG/Romance-cleaned-2 | MarkGG | 2022-10-28T07:20:20Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T07:20:20Z | 2022-10-28T07:20:14.000Z | 2022-10-28T07:20:14 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3407789.8839248433
num_examples: 6466
- name: validation
num_bytes: 378936.11607515655
num_examples: 719
download_size: 2403265
dataset_size: 3786726.0
---
# Dataset Card for "Romance-cleaned-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.3300856053829193,
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-0.20199289917945862... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
joannyli/test | joannyli | 2022-10-28T07:47:41Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T07:47:41Z | 2022-10-28T07:45:58.000Z | 2022-10-28T07:45:58 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wesleywt/uniprot_sprot | wesleywt | 2022-10-30T12:44:58Z | 12 | 0 | null | [
"region:us"
] | 2022-10-30T12:44:58Z | 2022-10-28T09:09:42.000Z | 2022-10-28T09:09:42 | ---
dataset_info:
features:
- name: uniprot_id
dtype: string
- name: sequences
dtype: string
splits:
- name: test
num_bytes: 21314102.893347207
num_examples: 56801
- name: train
num_bytes: 191823924.1066528
num_examples: 511201
download_size: 211969427
dataset_size: 213138027.0
---
# Dataset Card for "uniprot_sprot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.2128156274557113... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tglcourse/latent_afhqv2_256px | tglcourse | 2022-10-28T11:51:36Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T11:51:36Z | 2022-10-28T09:19:16.000Z | 2022-10-28T09:19:16 | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
0: cat
1: dog
2: wild
- name: latent
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 267449972
num_examples: 15803
download_size: 260672854
dataset_size: 267449972
---
# Dataset Card for "latent_afhqv2_256px"
Each image is cropped to 256px square and encoded to a 4x32x32 latent representation using the same VAE as that employed by Stable Diffusion
Decoding
```python
from diffusers import AutoencoderKL
from datasets import load_dataset
from PIL import Image
import numpy as np
import torch
# load the dataset
dataset = load_dataset('tglcourse/latent_afhqv2_256px')
# Load the VAE (requires access - see repo model card for info)
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 32, 32)
latent = (1 / 0.18215) * latent # Scale to match SD implementation
with torch.no_grad():
image = vae.decode(latent).sample[0] # Decode
image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1)
image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat
image = (image * 255).round().astype("uint8") # (0, 255) and type uint8
image = Image.fromarray(image) # To PIL
image # The resulting PIL image
``` | [
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-0.2231510132... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
havens2/naacl2022 | havens2 | 2022-10-28T11:37:16Z | 12 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"acl",
"sciBERT",
"sc... | 2022-10-28T11:37:16Z | 2022-10-28T09:38:15.000Z | 2022-10-28T09:38:15 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- crowdsourced
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: sci_NER_naacl
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- acl
- sciBERT
- sci
- acl
- '11711'
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for [naacl2022]
## 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
This is a named entity recognition dataset annotated for the science entity recognition task, a [project](https://github.com/neubig/nlp-from-scratch-assignment-2022) from the CMU 11-711 course.
### Supported Tasks and Leaderboards
NER task.
### Languages
English
## Dataset Structure
### Data Instances
A sample of the dataset
{'id': '0',
'tokens': ['We', 'sample', '50', 'negative', 'cases', 'from', 'T5LARGE', '+', 'GenMC', 'for', 'each', 'dataset'],
'ner_tags':['O', 'O', 'O', 'O', 'O', 'O', 'B-MethodName', 'O', 'B-MethodName', 'O', 'O', 'O']}
### Data Fields
id,tokens,ner_tags
- `id`: a `string` feature give the sample index.
- `tokens`: a `list` of `string` features give the sequence.
- `ner_tags`: a `list` of classification labels for each token in the sentence, with possible values including
`O` (0), `B-MethodName` (1), `I-MethodName` (2), `B-HyperparameterName` (3),`I-HyperparameterName` (4),`B-HyperparameterValue` (5),`I-HyperparameterValue` (6),`B-MetricName` (7),`I-MetricName` (8),`B-MetricValue` (9),`I-MetricValue` (10),`B-TaskName` (11),`I-TaskName` (12),`B-DatasetName` (13),`I-DatasetName` (14).
### Data Splits
Data split into
train.txt
dev.txt
test.txt
## 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
The data is annotated by using labelstudio, the papers are collected from TACL and ACL 2022 conferences.
#### Who are the annotators?
Xiaoyue Cui and Haotian Teng annotated the datasets.
### 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 [@xcui297](https://github.com/xcui297); [@haotianteng](https://github.com/haotianteng) for adding this dataset.
| [
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0.3254298269748688... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
puellacurae/x | puellacurae | 2022-10-28T09:44:17Z | 12 | 0 | null | [
"license:openrail",
"doi:10.57967/hf/0067",
"region:us"
] | 2022-10-28T09:44:17Z | 2022-10-28T09:43:22.000Z | 2022-10-28T09:43:22 | ---
license: openrail
---
| [
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tglcourse/latent_afhqv2_512px | tglcourse | 2022-10-28T11:52:19Z | 12 | 0 | null | [
"region:us"
] | 2022-10-28T11:52:19Z | 2022-10-28T10:21:26.000Z | 2022-10-28T10:21:26 | ---
dataset_info:
features:
- name: label
dtype:
class_label:
names:
0: cat
1: dog
2: wild
- name: latent
sequence:
sequence:
sequence: float32
splits:
- name: train
num_bytes: 1052290164
num_examples: 15803
download_size: 1038619876
dataset_size: 1052290164
---
# Dataset Card for "latent_afhqv2_512px"
Each image is cropped to 512px square and encoded to a 4x64x64 latent representation using the same VAE as that employed by Stable Diffusion
Decoding
```python
from diffusers import AutoencoderKL
from datasets import load_dataset
from PIL import Image
import numpy as np
import torch
# load the dataset
dataset = load_dataset('tglcourse/latent_lsun_church_256px')
# Load the VAE (requires access - see repo model card for info)
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
latent = torch.tensor([dataset['train'][0]['latent']]) # To tensor (bs, 4, 64, 3264
latent = (1 / 0.18215) * latent # Scale to match SD implementation
with torch.no_grad():
image = vae.decode(latent).sample[0] # Decode
image = (image / 2 + 0.5).clamp(0, 1) # To (0, 1)
image = image.detach().cpu().permute(1, 2, 0).numpy() # To numpy, channels lsat
image = (image * 255).round().astype("uint8") # (0, 255) and type uint8
image = Image.fromarray(image) # To PIL
image # The resulting PIL image
``` | [
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-0.187135457992... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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