sha
stringlengths 40
40
| text
stringlengths 0
13.4M
| id
stringlengths 2
117
| tags
sequence | created_at
stringlengths 25
25
| metadata
stringlengths 2
31.7M
| last_modified
stringlengths 25
25
|
|---|---|---|---|---|---|---|
107ceaebf9a34cbc98f1f06671f329091ca8935a | Li-Tang/demo | [
"license:apache-2.0",
"region:us"
] | 2022-07-13T07:31:37+00:00 | {"license": "apache-2.0"} | 2022-07-13T07:31:58+00:00 |
|
3260dccba6b51ccb97d5c1f254dca537f878bd71 | thelou1s/AudioSet | [
"license:apache-2.0",
"region:us"
] | 2022-07-13T07:40:42+00:00 | {"license": "apache-2.0"} | 2022-07-13T07:40:42+00:00 |
|
c1fd84ff07d2109ae732763f1a493ec701cdf0fc | dasago78/tweets | [
"license:afl-3.0",
"region:us"
] | 2022-07-13T08:54:21+00:00 | {"license": "afl-3.0"} | 2022-07-13T08:54:21+00:00 |
|
f157539762cc2043179f65803a83edf536505d2e | # Five standard datasets for few-shot classification
- *miniImageNet*. It contains 100 classes with 600 images in each class, which are built upon the ImageNet dataset. The 100 classes are divided into 64, 16, 20 for meta-training, meta-validation and meta-testing, respectively.
- *tieredImageNet*. TieredImageNet is also a subset of ImageNet, which includes 608 classes from 34 super-classes. Compared with miniImageNet, the splits of meta-training(20), meta-validation(6) and meta-testing(8) are set according to the super-classes to enlarge the domain difference between training and testing phase. The dataset also include more images for training and evaluation.
- *CIFAR-FS*. CIFAR-FS is divided from CIFAR-100, which consists of 60,000 images in 100 categories. The CIFAR-FS is divided into 64, 16 and 20 for training, validation, and evaluation, respectively.
- *FC100*. FC100 is also divided from CIFAR-100, which is more difficult because it is more diverse. The FC100 uses a split similar to tieredImageNet, where train, validation, and test splits contain 60, 20, and 20 classes.
- *CUB*. CUB-200-2011 (CUB) is a fine-grained dataset of 200 bird species with total 11,788 images. It is is randomly divided into three disjoint sets of the training set (100 classes), validation set (50 classes), and testing set (50 classes). | pancake/few_shot_datasets | [
"license:mit",
"region:us"
] | 2022-07-13T09:21:56+00:00 | {"license": "mit"} | 2022-07-13T10:08:50+00:00 |
83ec5b2be9b212d6b2449659f1f34bfd88daddb2 | Amro-Kamal/ObjectPose | [
"license:apache-2.0",
"region:us"
] | 2022-07-13T10:48:24+00:00 | {"license": "apache-2.0"} | 2022-07-18T16:36:07+00:00 |
|
1712fe0a5a288d611a7ad0d4a2606bbae6e1d20e | JeunesseAfricaine/my_tweets | [
"license:apache-2.0",
"region:us"
] | 2022-07-13T11:22:11+00:00 | {"license": "apache-2.0"} | 2022-07-13T11:28:15+00:00 |
|
eddd3f1d7abd852e36b6382f7c552cfccd24dacf | pancake/TransVLAD_pretrain_models | [
"license:mit",
"region:us"
] | 2022-07-13T11:24:51+00:00 | {"license": "mit"} | 2022-07-13T13:48:19+00:00 |
|
babeb4f95e4456db3d2bd7fad9817c1e11bd2fe2 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-6e6ed30f-40d7-4939-99af-0ba4041a05ee-6559 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T12:43:34+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "autoevaluate/binary-classification", "metrics": ["matthews_correlation"], "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}} | 2022-07-13T12:44:19+00:00 |
3917c429489260542649a032c487a1625a1fb27f | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: autoevaluate/binary-classification
* Dataset: glue
* Config: sst2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-516fe874-79cb-42fc-b851-f98848ce24df-6660 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T12:50:43+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["glue"], "eval_info": {"task": "binary_classification", "model": "autoevaluate/binary-classification", "metrics": ["matthews_correlation"], "dataset_name": "glue", "dataset_config": "sst2", "dataset_split": "validation", "col_mapping": {"text": "sentence", "target": "label"}}} | 2022-07-13T12:51:24+00:00 |
fb6e978692355615bcc252f1720e442e932d7ecb | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: autoevaluate/multi-class-classification
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-5968bffe-3bbc-4366-a1a8-9d11b19abcf7-6862 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T13:02:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "autoevaluate/multi-class-classification", "metrics": ["matthews_correlation"], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-13T13:03:09+00:00 |
e1515020a6349b9a4f15d6c063dcbfb59ab5b058 | # 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: autoevaluate/entity-extraction
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-9e17c416-43f7-4fe8-b337-f391ae065c4a-6963 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T13:17:47+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["conll2003"], "eval_info": {"task": "entity_extraction", "model": "autoevaluate/entity-extraction", "metrics": [], "dataset_name": "conll2003", "dataset_config": "conll2003", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}} | 2022-07-13T13:19:40+00:00 |
a2718d91d23b04a40cf9da5e19e37ba7a40af32d | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: autoevaluate/translation
* Dataset: wmt16
* Config: ro-en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-5cf6dc10-95bf-44e5-9ff2-42dca08d711a-7064 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T13:22:03+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["wmt16"], "eval_info": {"task": "translation", "model": "autoevaluate/translation", "metrics": [], "dataset_name": "wmt16", "dataset_config": "ro-en", "dataset_split": "test", "col_mapping": {"source": "translation.ro", "target": "translation.en"}}} | 2022-07-13T13:26:06+00:00 |
bb88e1af8514f9d01d0134aa319dc77d5ac61699 |
This is a parsed version of [github-jupyter-parsed](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed), with markdown and code pairs. We provide the preprocessing script in [preprocessing.py](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed-v2/blob/main/preprocessing.py). The data is deduplicated and consists of 451662 examples.
For similar datasets with text and Python code, there is [CoNaLa](https://huggingface.co/datasets/neulab/conala) benchmark from StackOverflow, with some samples curated by annotators. | codeparrot/github-jupyter-text-code-pairs | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
] | 2022-07-13T13:34:33+00:00 | {"annotations_creators": [], "language": ["code"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "github-jupyter-text-code-pairs"} | 2022-10-25T08:30:34+00:00 |
e989f41f7b4bd9fcc4dee49de89c0e40846e2874 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: aatmasidha/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@aatmasidha](https://huggingface.co/aatmasidha) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-41e4622b-10765447 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T14:02:15+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "aatmasidha/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-13T14:02:51+00:00 |
305e4097db04afdaac3bb9e4b8d50d9982e879bc | TheoMrc/Zebrafish-AChE-Orientation-Classification | [
"region:us"
] | 2022-07-13T14:50:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "left-top", "1": "right-top", "2": "left-bottom", "3": "right-bottom"}}}}], "splits": [{"name": "train", "num_bytes": 286091806.87116563, "num_examples": 272}], "download_size": 267568992, "dataset_size": 286091806.87116563}} | 2024-02-13T11:20:03+00:00 |
|
ad54a715f87110485a83cbcbf6a4a3d2cb14327f | # 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: sarahmiller137/distilbert-base-uncased-ft-conll2003
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sarahmiller137](https://huggingface.co/sarahmiller137) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-conll2003-70dc316d-10775449 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T15:01:12+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["conll2003"], "eval_info": {"task": "entity_extraction", "model": "sarahmiller137/distilbert-base-uncased-ft-conll2003", "metrics": [], "dataset_name": "conll2003", "dataset_config": "conll2003", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}} | 2022-07-13T15:02:16+00:00 |
c4821b678115e52620027e77f76919953581236c | Question & question body together with the best answers to that question from Reddit.
The score for the question / answer is the upvote count (i.e. positive-negative upvotes).
Only questions / answers that have these properties were extracted:
min_score = 3
min_title_len = 20
min_body_len = 100 | nreimers/reddit_question_best_answers | [
"region:us"
] | 2022-07-13T15:14:37+00:00 | {} | 2022-07-13T16:25:49+00:00 |
73dc78712bdc87098038515d9fb03bbf97b9e6fb |
# KENNSLURÓMUR - ICELANDIC LECTURES
### [Icelandic]
Kennslurómur - Íslenskir fyrirlestrar er safn af hljóðskrám og samsvarandi texta úr kennslufyrirlestrum sem teknir voru upp í áföngum í Háskólanum í Reykjavík og Háskóla Íslands. Þetta safn má nota við þjálfun talgreina.
Fyrirlesararnir gáfu upptökurnar sínar sem síðan voru talgreindar með talgreini, næst var frálagið lesið og leiðrétt af hópi sumarnema og að lokum var allur texti yfirfarinn af prófarkalesara.
Í þessu safni eru 51 klukkustund af hljóðskrám sem dreifast á 171 fyrirlestur frá 11 fyrirlesurum.
### [English]
Kennslurómur - Icelandic Lectures is a collection of audio recordings and their corresponding segmented transcripts from class lectures recorded at Reykjavik University and the University of Iceland. This material was compiled for the training of speech recognition models.
The lectures were donated by each lecturer, then transcribed with an Icelandic speech recognizer, then manually corrected by human transcribers and finally verified by a proofreader.
This release contains 51 hours divided between 171 lectures from 11 lecturers.
## LECTURE TOPICS
The topic of the lextures cover a diverse range of university level subjects.
```
Linguistics 15 lectures 1 speaker 7,12 hours
Computer science 33 lectures 3 speakers 15,3 hours
Labour market economics 13 lectures 1 speaker 1,91 hours
Engineering 64 lectures 3 speakers 11,3 hours
Legal studies 25 lectures 2 speakers 7,52 hours
Business intelligence 1 lecture 1 speaker 19,2 minutes
Psychology 10 lectures 1 speaker 3,03 hours
Sports science 10 lectures 1 speaker 4,79 hours
```
## STRUCTURE
SPEAKERS.tsv - Lists the speakers (lecturers) and their IDs.
LECTURES.tsv - Lists all lectures. See header for the format.
DOCS/
transcription_guidelines_is.txt - Transcription guidelines in Icelandic.
LICENSE.txt - Description of the license.
prerp_for_training.py - An example data preparation script for KALDI.
<SPK-ID>/ - A directory per speaker.
<LECTURE-ID>.wav - Audio recording of the entire lecture.
<LECTURE-ID>.txt - Transcript of the entire lecture in 1 to
40 second segments. Tab separated list with the
fields: segment ID, start time in milliseconds,
end time in milliseconds and utterance text.
## Alignment and segmentation
The segments are mostly split on sentence boundaries. Each segment ranges from a few seconds to roughly 40 seconds in duration. The recordings and transcripts were automatically aligned using either [Montreal Forced Aligner](https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner) or the aligner [Gentle](https://github.com/lowerquality/gentle). The alignment quality was tested by training an acoustic model in Kaldi and rejected segments due to alignment issues. Recordings with an abnormally high number of faulty segments were manually aligned. This means that there are likely still some imperfectly aligned segments, but due to resource constraints, they were not manually checked and verified.
## Training, development and testing sets
Every segment has been marked as either train, dev or eval. This can be seen in the \<SPK-ID\>/\<LECTURE-ID\>.txt files. There are a few speakers in this dataset creating training sets without overlap of speakers is not possible without holding out a large portion of the data. Therefore, it was decided to randomly assign each speaker's segments proportionally 80/10/10 (train, dev, eval) based on the duration of each segment.
## FORMAT
Sampling rate 16000 Hz
Audio format 16 bit PCM RIFF WAVE
Language Icelandic
Type of speech Single speaker spontaneous and scripted speech with minimal
backspeech.
Media type Recorded university lectures, a mixture of prerecorded
classes and in-class recordings.
## SPECIAL ANNOTATIONS
Three types of special annotations are found the transcripts:
[UNK] Unintelligible, spoken background noise
[HIK: <stubs>] Hesitation, where <stubs> can be a comma separated list
of false start (often partial) words.
[<IPA sym>] Standalone IPA phones are transcribed in brackets which
only appear in "Icelandic linguistics" lectures.
E.g. "Þannig fáum við eins og raddað b, [p] [p] [p]
„bera bera“.".
## LICENSE
The audio recordings (.wav files) are attributed to the corresponding lecturer
in the file `SPEAKERS.tsv`. Everything else is attributed to
[Tiro ehf](https://tiro.is).
Published with a CC BY 4.0 license. You are free to copy and redistribute the
material in any medium or format, remix, transform and build upon the material
for any purpose, even commercially under the following terms: 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.
Link to the license: https://creativecommons.org/licenses/by/4.0/
## ACKNOWLEDGMENTS
This project was funded by the Language Technology Programme for Icelandic
2019-2023. The programme, which is managed and coordinated by Almannarómur, is
funded by the Icelandic Ministry of Education, Science and Culture.
| tiro-is/kennsluromur | [
"region:us"
] | 2022-07-13T15:41:14+00:00 | {} | 2022-08-22T14:27:03+00:00 |
ed6fe0515a01f2663b65e58af0f0117ea29add96 | # 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: issifuamajeed/distilbert-base-uncased-finetuned-ner
* Dataset: conll2003
* Config: conll2003
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@issifuamajeed](https://huggingface.co/issifuamajeed) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-conll2003-6fdc3173-10805452 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T15:43:41+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["conll2003"], "eval_info": {"task": "entity_extraction", "model": "issifuamajeed/distilbert-base-uncased-finetuned-ner", "metrics": [], "dataset_name": "conll2003", "dataset_config": "conll2003", "dataset_split": "test", "col_mapping": {"tokens": "tokens", "tags": "ner_tags"}}} | 2022-07-13T15:44:49+00:00 |
60c5c133f043a5cffe162f9de1c62b9d88f309cf |
# XLCost for text-to-code synthesis
## Dataset Description
This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at snippet level and program level for **7** programming languages: `Python, C, C#, C++, Java, Javascript and PHP`.
## Languages
The dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages.
## Dataset Structure
To load the dataset you need to specify a subset among the **14 exiting instances**: `LANGUAGE-snippet-level/LANGUAGE-program-level` for `LANGUAGE` in `[Python, C, Csharp, C++, Java, Javascript and PHP]`. By default `Python-snippet-level` is loaded.
```python
from datasets import load_dataset
load_dataset("codeparrot/xlcost-text-to-code", "Python-program-level")
DatasetDict({
train: Dataset({
features: ['text', 'code'],
num_rows: 9263
})
test: Dataset({
features: ['text', 'code'],
num_rows: 887
})
validation: Dataset({
features: ['text', 'code'],
num_rows: 472
})
})
```
```python
next(iter(data["train"]))
{'text': 'Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ; Driver code',
'code': 'def maxPresum ( a , b ) : NEW_LINE INDENT X = max ( a [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( a ) ) : NEW_LINE INDENT a [ i ] += a [ i - 1 ] NEW_LINE X = max ( X , a [ i ] ) NEW_LINE DEDENT Y = max ( b [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( b ) ) : NEW_LINE INDENT b [ i ] += b [ i - 1 ] NEW_LINE Y = max ( Y , b [ i ] ) NEW_LINE DEDENT return X + Y NEW_LINE DEDENT A = [ 2 , - 1 , 4 , - 5 ] NEW_LINE B = [ 4 , - 3 , 12 , 4 , - 3 ] NEW_LINE print ( maxPresum ( A , B ) ) NEW_LINE'}
```
Note that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of `\n` and INDENT instead of `\t`, DEDENT to cancel indentation...
## Data Fields
* text: natural language description/comment
* code: code at snippet/program level
## Data Splits
Each subset has three splits: train, test and validation.
## Citation Information
```
@misc{zhu2022xlcost,
title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
url = {https://arxiv.org/abs/2206.08474},
author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
year = {2022},
eprint={2206.08474},
archivePrefix={arXiv}
}
``` | codeparrot/xlcost-text-to-code | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:cc-by-sa-4.0",
"arxiv:2206.08474",
"region:us"
] | 2022-07-13T17:13:17+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "pretty_name": "xlcost-text-to-code"} | 2022-10-25T08:30:47+00:00 |
def6fb768c983ea694dbf3603b05c043eeeb10b4 | # 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: pszemraj/led-base-book-summary
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-d9df6ac3-10825454 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T17:26:11+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-14T02:24:43+00:00 |
46a2c0595dc3673ad5970be668c88155a90b1bd4 |
# Dataset Card for `reviews_with_drift`
## 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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## 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
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. | arize-ai/fashion_mnist_label_drift | [
"task_categories:image-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imdb",
"language:en",
"license:mit",
"region:us"
] | 2022-07-13T19:36:05+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended|imdb"], "task_categories": ["image-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "sentiment-classification-reviews-with-drift"} | 2022-10-25T09:40:04+00:00 |
2db78afdeaccaedc3b33a95442a4e55766887e17 |
# Dataset Card for Flores 200
## Table of Contents
- [Dataset Card for Flores 200](#dataset-card-for-flores-200)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [Flores](https://github.com/facebookresearch/flores)
- **Repository:** [Github](https://github.com/facebookresearch/flores)
### Dataset Summary
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
>The creation of FLORES-200 doubles the existing language coverage of FLORES-101.
Given the nature of the new languages, which have less standardization and require
more specialized professional translations, the verification process became more complex.
This required modifications to the translation workflow. FLORES-200 has several languages
which were not translated from English. Specifically, several languages were translated
from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also
includes two script alternatives for four languages. FLORES-200 consists of translations
from 842 distinct web articles, totaling 3001 sentences. These sentences are divided
into three splits: dev, devtest, and test (hidden). On average, sentences are approximately
21 words long.
**Disclaimer**: *The Flores-200 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this.
### Languages
The dataset contains parallel sentences for 200 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) plus an additional code describing the script (e.g., "eng_Latn", "ukr_Cyrl"). See [the webpage for code descriptions](https://github.com/facebookresearch/flores/blob/main/flores200/README.md).
Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command.
Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-ukr_Cyrl" will provide sentences in the format below).
## Dataset Structure
### Data Instances
A sample from the `dev` split for the Ukrainian language (`ukr_Cyrl` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
'id': 1,
'sentence': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.',
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0
}
```
When using a hyphenated pairing or using the `all` function, data will be presented as follows:
```python
{
'id': 1,
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0,
'sentence_eng_Latn': 'On Monday, scientists from the Stanford University School of Medicine announced the invention of a new diagnostic tool that can sort cells by type: a tiny printable chip that can be manufactured using standard inkjet printers for possibly about one U.S. cent each.',
'sentence_ukr_Cyrl': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.'
}
```
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language (may have _lang for pairings)
- `URL`: The URL for the English article from which the sentence was extracted.
- `domain`: The domain of the sentence.
- `topic`: The topic of the sentence.
- `has_image`: Whether the original article contains an image.
- `has_hyperlink`: Whether the sentence contains a hyperlink.
### Data Splits
| config| `dev`| `devtest`|
|-----------------:|-----:|---------:|
|all configurations| 997| 1012:|
### Dataset Creation
Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation.
## Additional Information
### Dataset Curators
See paper for details.
### Licensing Information
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@article{nllb2022,
author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},
title = {No Language Left Behind: Scaling Human-Centered Machine Translation},
year = {2022}
}
```
Please also cite prior work that this dataset builds on:
```bibtex
@inproceedings{,
title={The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
year={2021}
}
```
```bibtex
@inproceedings{,
title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
author={Guzm\'{a}n, Francisco and Chen, Peng-Jen and Ott, Myle and Pino, Juan and Lample, Guillaume and Koehn, Philipp and Chaudhary, Vishrav and Ranzato, Marc'Aurelio},
journal={arXiv preprint arXiv:1902.01382},
year={2019}
}
``` | facebook/flores | [
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|flores",
"language:ace",
"language:acm",
"language:acq",
"language:aeb",
"language:af",
"language:ajp",
"language:ak",
"language:als",
"language:am",
"language:apc",
"language:ar",
"language:ars",
"language:ary",
"language:arz",
"language:as",
"language:ast",
"language:awa",
"language:ayr",
"language:azb",
"language:azj",
"language:ba",
"language:bm",
"language:ban",
"language:be",
"language:bem",
"language:bn",
"language:bho",
"language:bjn",
"language:bo",
"language:bs",
"language:bug",
"language:bg",
"language:ca",
"language:ceb",
"language:cs",
"language:cjk",
"language:ckb",
"language:crh",
"language:cy",
"language:da",
"language:de",
"language:dik",
"language:dyu",
"language:dz",
"language:el",
"language:en",
"language:eo",
"language:et",
"language:eu",
"language:ee",
"language:fo",
"language:fj",
"language:fi",
"language:fon",
"language:fr",
"language:fur",
"language:fuv",
"language:gaz",
"language:gd",
"language:ga",
"language:gl",
"language:gn",
"language:gu",
"language:ht",
"language:ha",
"language:he",
"language:hi",
"language:hne",
"language:hr",
"language:hu",
"language:hy",
"language:ig",
"language:ilo",
"language:id",
"language:is",
"language:it",
"language:jv",
"language:ja",
"language:kab",
"language:kac",
"language:kam",
"language:kn",
"language:ks",
"language:ka",
"language:kk",
"language:kbp",
"language:kea",
"language:khk",
"language:km",
"language:ki",
"language:rw",
"language:ky",
"language:kmb",
"language:kmr",
"language:knc",
"language:kg",
"language:ko",
"language:lo",
"language:lij",
"language:li",
"language:ln",
"language:lt",
"language:lmo",
"language:ltg",
"language:lb",
"language:lua",
"language:lg",
"language:luo",
"language:lus",
"language:lvs",
"language:mag",
"language:mai",
"language:ml",
"language:mar",
"language:min",
"language:mk",
"language:mt",
"language:mni",
"language:mos",
"language:mi",
"language:my",
"language:nl",
"language:nn",
"language:nb",
"language:npi",
"language:nso",
"language:nus",
"language:ny",
"language:oc",
"language:ory",
"language:pag",
"language:pa",
"language:pap",
"language:pbt",
"language:pes",
"language:plt",
"language:pl",
"language:pt",
"language:prs",
"language:quy",
"language:ro",
"language:rn",
"language:ru",
"language:sg",
"language:sa",
"language:sat",
"language:scn",
"language:shn",
"language:si",
"language:sk",
"language:sl",
"language:sm",
"language:sn",
"language:sd",
"language:so",
"language:st",
"language:es",
"language:sc",
"language:sr",
"language:ss",
"language:su",
"language:sv",
"language:swh",
"language:szl",
"language:ta",
"language:taq",
"language:tt",
"language:te",
"language:tg",
"language:tl",
"language:th",
"language:ti",
"language:tpi",
"language:tn",
"language:ts",
"language:tk",
"language:tum",
"language:tr",
"language:tw",
"language:tzm",
"language:ug",
"language:uk",
"language:umb",
"language:ur",
"language:uzn",
"language:vec",
"language:vi",
"language:war",
"language:wo",
"language:xh",
"language:ydd",
"language:yo",
"language:yue",
"language:zh",
"language:zsm",
"language:zu",
"license:cc-by-sa-4.0",
"conditional-text-generation",
"arxiv:2207.04672",
"region:us"
] | 2022-07-13T20:11:38+00:00 | {"annotations_creators": ["found"], "language_creators": ["expert-generated"], "language": ["ace", "acm", "acq", "aeb", "af", "ajp", "ak", "als", "am", "apc", "ar", "ars", "ary", "arz", "as", "ast", "awa", "ayr", "azb", "azj", "ba", "bm", "ban", "be", "bem", "bn", "bho", "bjn", "bo", "bs", "bug", "bg", "ca", "ceb", "cs", "cjk", "ckb", "crh", "cy", "da", "de", "dik", "dyu", "dz", "el", "en", "eo", "et", "eu", "ee", "fo", "fj", "fi", "fon", "fr", "fur", "fuv", "gaz", "gd", "ga", "gl", "gn", "gu", "ht", "ha", "he", "hi", "hne", "hr", "hu", "hy", "ig", "ilo", "id", "is", "it", "jv", "ja", "kab", "kac", "kam", "kn", "ks", "ka", "kk", "kbp", "kea", "khk", "km", "ki", "rw", "ky", "kmb", "kmr", "knc", "kg", "ko", "lo", "lij", "li", "ln", "lt", "lmo", "ltg", "lb", "lua", "lg", "luo", "lus", "lvs", "mag", "mai", "ml", "mar", "min", "mk", "mt", "mni", "mos", "mi", "my", "nl", "nn", "nb", "npi", "nso", "nus", "ny", "oc", "ory", "pag", "pa", "pap", "pbt", "pes", "plt", "pl", "pt", "prs", "quy", "ro", "rn", "ru", "sg", "sa", "sat", "scn", "shn", "si", "sk", "sl", "sm", "sn", "sd", "so", "st", "es", "sc", "sr", "ss", "su", "sv", "swh", "szl", "ta", "taq", "tt", "te", "tg", "tl", "th", "ti", "tpi", "tn", "ts", "tk", "tum", "tr", "tw", "tzm", "ug", "uk", "umb", "ur", "uzn", "vec", "vi", "war", "wo", "xh", "ydd", "yo", "yue", "zh", "zsm", "zu"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["unknown"], "source_datasets": ["extended|flores"], "task_categories": ["text2text-generation", "translation"], "task_ids": [], "paperswithcode_id": "flores", "pretty_name": "flores200", "language_details": "ace_Arab, ace_Latn, acm_Arab, acq_Arab, aeb_Arab, afr_Latn, ajp_Arab, aka_Latn, amh_Ethi, apc_Arab, arb_Arab, ars_Arab, ary_Arab, arz_Arab, asm_Beng, ast_Latn, awa_Deva, ayr_Latn, azb_Arab, azj_Latn, bak_Cyrl, bam_Latn, ban_Latn,bel_Cyrl, bem_Latn, ben_Beng, bho_Deva, bjn_Arab, bjn_Latn, bod_Tibt, bos_Latn, bug_Latn, bul_Cyrl, cat_Latn, ceb_Latn, ces_Latn, cjk_Latn, ckb_Arab, crh_Latn, cym_Latn, dan_Latn, deu_Latn, dik_Latn, dyu_Latn, dzo_Tibt, ell_Grek, eng_Latn, epo_Latn, est_Latn, eus_Latn, ewe_Latn, fao_Latn, pes_Arab, fij_Latn, fin_Latn, fon_Latn, fra_Latn, fur_Latn, fuv_Latn, gla_Latn, gle_Latn, glg_Latn, grn_Latn, guj_Gujr, hat_Latn, hau_Latn, heb_Hebr, hin_Deva, hne_Deva, hrv_Latn, hun_Latn, hye_Armn, ibo_Latn, ilo_Latn, ind_Latn, isl_Latn, ita_Latn, jav_Latn, jpn_Jpan, kab_Latn, kac_Latn, kam_Latn, kan_Knda, kas_Arab, kas_Deva, kat_Geor, knc_Arab, knc_Latn, kaz_Cyrl, kbp_Latn, kea_Latn, khm_Khmr, kik_Latn, kin_Latn, kir_Cyrl, kmb_Latn, kon_Latn, kor_Hang, kmr_Latn, lao_Laoo, lvs_Latn, lij_Latn, lim_Latn, lin_Latn, lit_Latn, lmo_Latn, ltg_Latn, ltz_Latn, lua_Latn, lug_Latn, luo_Latn, lus_Latn, mag_Deva, mai_Deva, mal_Mlym, mar_Deva, min_Latn, mkd_Cyrl, plt_Latn, mlt_Latn, mni_Beng, khk_Cyrl, mos_Latn, mri_Latn, zsm_Latn, mya_Mymr, nld_Latn, nno_Latn, nob_Latn, npi_Deva, nso_Latn, nus_Latn, nya_Latn, oci_Latn, gaz_Latn, ory_Orya, pag_Latn, pan_Guru, pap_Latn, pol_Latn, por_Latn, prs_Arab, pbt_Arab, quy_Latn, ron_Latn, run_Latn, rus_Cyrl, sag_Latn, san_Deva, sat_Beng, scn_Latn, shn_Mymr, sin_Sinh, slk_Latn, slv_Latn, smo_Latn, sna_Latn, snd_Arab, som_Latn, sot_Latn, spa_Latn, als_Latn, srd_Latn, srp_Cyrl, ssw_Latn, sun_Latn, swe_Latn, swh_Latn, szl_Latn, tam_Taml, tat_Cyrl, tel_Telu, tgk_Cyrl, tgl_Latn, tha_Thai, tir_Ethi, taq_Latn, taq_Tfng, tpi_Latn, tsn_Latn, tso_Latn, tuk_Latn, tum_Latn, tur_Latn, twi_Latn, tzm_Tfng, uig_Arab, ukr_Cyrl, umb_Latn, urd_Arab, uzn_Latn, vec_Latn, vie_Latn, war_Latn, wol_Latn, xho_Latn, ydd_Hebr, yor_Latn, yue_Hant, zho_Hans, zho_Hant, zul_Latn", "tags": ["conditional-text-generation"]} | 2024-01-18T15:05:58+00:00 |
d578cb5b1cfdbfe451e7c31f8e00ad48f54a5185 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: lewiswatson/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewiswatson](https://huggingface.co/lewiswatson) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-700553d6-10835457 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-13T21:39:39+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "lewiswatson/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-13T21:40:06+00:00 |
bb6b2ea9bac5837836d38dc524d0b987d2a1fc0f |
# Namuwiki database dump (2021-03-01)
## Dataset Description
- **Homepage:** [나무위키:데이터베이스 덤프](https://namu.wiki/w/%EB%82%98%EB%AC%B4%EC%9C%84%ED%82%A4:%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B2%A0%EC%9D%B4%EC%8A%A4%20%EB%8D%A4%ED%94%84)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
## Namuwiki
https://namu.wiki/
It is a Korean wiki based on the seed engine, established on April 17, 2015 (KST).
## About dataset
All data from Namuwiki collected on 2021-03-01. I filtered data without text(mostly redirecting documents).
You can download the original data converted to csv in [Kaggle](https://www.kaggle.com/datasets/brainer3220/namu-wiki).
## 2022-03-01 dataset
[heegyu/namuwiki](https://huggingface.co/datasets/heegyu/namuwiki)<br>
[heegyu/namuwiki-extracted](https://huggingface.co/datasets/heegyu/namuwiki-extracted)<br>
[heegyu/namuwiki-sentences](https://huggingface.co/datasets/heegyu/namuwiki-sentences)
### Lisence
[CC BY-NC-SA 2.0 KR](https://creativecommons.org/licenses/by-nc-sa/2.0/kr/)
## Data Structure
### Data Instance
```pycon
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/namuwiki_20210301_filtered")
>>> dataset
DatasetDict({
train: Dataset({
features: ['title', 'text'],
num_rows: 571308
})
})
```
```pycon
>>> dataset["train"].features
{'title': Value(dtype='string', id=None),
'text': Value(dtype='string', id=None)}
```
### Data Size
download: 3.26 GiB<br>
generated: 3.73 GiB<br>
total: 6.99 GiB
### Data Field
- title: `string`
- text: `string`
### Data Splits
| | train |
| ---------- | ------ |
| # of texts | 571308 |
```pycon
>>> dataset["train"][2323]
{'title': '55번 지방도',
'text': '55번 국가지원지방도\n해남 ~ 금산\n시점 전라남도 해남군 북평면 남창교차로\n종점 충청남도 금산군 금산읍 우체국사거리\n총 구간 279.2km\n경유지 전라남도 강진군, 장흥군, 영암군 전라남도 나주시, 화순군 광주광역시 동구, 북구 전라남도 담양군 전라북도 순창군, 정읍시, 완주군 전라북도 임실군, 진안군\n개요\n국가지원지방도 제55호선은 전라남도 해남군에서 출발하여 충청남도 금산군까지 이어지는 대한민국의 국가지원지방도이다.\n전라남도 해남군 북평면 - 전라남도 강진군 도암면 구간은 광주광역시, 전라남도 동부권, 영남 지방에서 완도군 완도읍으로 갈 때 주로 이용된다.] 해남 - 완도구간이 확장되기 전에는 그랬다. 강진군, 장흥군은 예외]\n노선\n전라남도\n해남군\n백도로\n북평면 남창교차로에서 13번 국도, 77번 국도와 만나며 출발한다.\n쇄노재\n북일면 북일초교 앞에서 827번 지방도와 만난다.\n강진군\n백도로\n도암면소재지 사거리에서 819번 지방도와 만난다. 819번 지방도는 망호선착장까지만 길이 있으며, 뱃길을 통해 간접적으로 바다 건너의 819번 지방도와 연결된다.\n석문공원\n도암면 계라교차로에서 18번 국도에 합류한다. 우회전하자. 이후 강진읍까지 18번 국도와 중첩되고 장흥군 장흥읍까지 2번 국도와 중첩된다. 그리고 장흥읍부터 영암군을 거쳐 나주시 세지면까지는 23번 국도와 중첩된다.\n나주시\n동창로\n세지면 세지교차로에서 드디어 23번 국도로부터 분기하면서 820번 지방도와 직결 합류한다. 이 길은 2013년 현재 확장 공사 중이다. 확장공사가 완료되면 동창로가 55번 지방도 노선이 된다.\n세남로\n봉황면 덕림리 삼거리에서 820번 지방도와 분기한다.\n봉황면 철천리 삼거리에서 818번 지방도와 합류한다.\n봉황면 송현리 삼거리에서 818번 지방도와 분기한다.\n송림산제길\n동창로\n여기부터 완공된 왕복 4차로 길이다. 이 길을 만들면서 교통량이 늘어났지만 주변 농민들이 이용하는 농로의 교량을 설치하지 않아 문제가 생기기도 했다. #1 #2\n세남로\n남평읍에서 다시 왕복 2차로로 줄어든다.\n남평읍 남평오거리에서 822번 지방도와 만난다.\n산남로\n남평교를 건너고 남평교사거리에서 우회전\n동촌로\n남평역\n화순군\n동촌로\n화순읍 앵남리 삼거리에서 817번 지방도와 합류한다. 좌회전하자.\n앵남역\n지강로\n화순읍 앵남리 앵남교차로에서 817번 지방도와 분기한다. 앵남교차로부터 나주 남평읍까지 55번 지방도의 확장공사가 진행중이다.\n오성로\n여기부터 화순읍 대리사거리까지 왕복 4차선으로 확장 공사를 진행했고, 2015년 8월 말 화순읍 구간은 왕복 4차선으로 확장되었다.\n화순역\n화순읍에서 광주광역시 동구까지 22번 국도와 중첩되고, 동구부터 전라북도 순창군 쌍치면까지는 29번 국도와 중첩된다.\n전라북도\n순창군\n청정로\n29번 국도를 따라가다가 쌍치면 쌍길매삼거리에서 우회전하여 21번 국도로 들어가자. 쌍치면 쌍치사거리에서 21번 국도와 헤어진다. 직진하자.\n정읍시\n청정로\n산내면 산내사거리에서 715번 지방도와 직결하면서 30번 국도에 합류한다. 좌회전하여 구절재를 넘자.\n산외로\n칠보면 시산교차로에서 49번 지방도와 교차되면 우회전하여 49번 지방도와 합류한다. 이제 오랜 시간 동안 49번 지방도와 합류하게 될 것이다.\n산외면 산외교차로에서 715번 지방도와 교차한다.\n엄재터널\n완주군\n산외로\n구이면 상용교차로에서 27번 국도에 합류한다. 좌회전하자.\n구이로\n구이면 백여교차로에서 27번 국도로부터 분기된다.\n구이면 대덕삼거리에서 714번 지방도와 만난다.\n구이면 염암삼거리에서 우회전\n신덕평로\n고개가 있다. 완주군과 임실군의 경계이다.\n임실군\n신덕평로\n신덕면 외량삼거리, 삼길삼거리에서 749번 지방도와 만난다.\n야트막한 고개가 하나 있다.\n신평면 원천리 원천교차로에서 745번 지방도와 교차한다.\n신평면 관촌역 앞에서 17번 국도와 합류한다. 좌회전하자.\n관진로\n관촌면 병암삼거리에서 17번 국도로부터 분기된다.\n순천완주고속도로와 교차되나 연결되지 않는다.\n진안군\n관진로\n성수면 좌산리에서 721번 지방도와 만난다.\n성수면 좌산리 좌산삼거리에서 721번 지방도와 만난다.\n마령면 강정교차로 부근에서 745번 지방도와 만난다.\n익산포항고속도로와 교차되나 연결되지 않는다.\n진안읍 진안연장농공단지 앞에서 26번 국도에 합류한다. 좌회전하자.\n전진로\n부귀면 부귀교차로에서 드디어 49번 지방도를 떠나보낸다. 그러나 아직 26번 국도와 중첩된다.\n완주군\n동상로\n드디어 55번이라는 노선 번호가 눈에 보이기 시작한다. 완주군 소양면에서 26번 국도와 분기된다. 이제부터 꼬불꼬불한 산길이므로 각오하고 운전하자.\n밤치. 소양면과 동상면의 경계가 되는 고개다.\n동상면 신월삼거리에서 732번 지방도와 만난다. 동상저수지에 빠지지 않도록 주의하자.\n동상주천로\n운장산고개를 올라가야 한다. 완주군과 진안군의 경계다. 고개 정상에 휴게소가 있다.\n진안군\n동상주천로\n주천면 주천삼거리에서 725번 지방도와 만난다.\n충청남도\n금산군\n보석사로\n남이면 흑암삼거리에서 635번 지방도와 만난다. 우회전해야 한다. 네이버 지도에는 좌회전해서 좀더 가면 나오는 길을 55번 지방도라고 써놓았는데, 잘못 나온 거다. 다음 지도에는 올바르게 나와있다.\n십이폭포로\n남이면에서 남일면으로 넘어간다.\n남일면에서 13번 국도와 합류한다. 좌회전하자. 이후 구간은 남이면을 거쳐 금산읍까지 13번 국도와 중첩되면서 55번 지방도 구간은 종료된다.'}
```
| Bingsu/namuwiki_20210301_filtered | [
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-nc-sa-2.0",
"region:us"
] | 2022-07-14T01:18:12+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["ko"], "license": ["cc-by-nc-sa-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["fill-mask", "text-generation"], "task_ids": ["masked-language-modeling", "language-modeling"], "pretty_name": "Namuwiki database dump (2021-03-01)"} | 2022-10-14T06:49:53+00:00 |
c46482041aedc5ee17e5915baef04dbf51ef437b | RedBaron/Naturetreasures | [
"license:artistic-2.0",
"region:us"
] | 2022-07-14T01:52:12+00:00 | {"license": "artistic-2.0"} | 2022-07-14T01:52:12+00:00 |
|
b33f4c49d4d160cecff232b288f3471acd242d62 | prasoonskrishnan/movie_recomendation | [
"license:afl-3.0",
"region:us"
] | 2022-07-14T05:04:28+00:00 | {"license": "afl-3.0"} | 2022-07-14T05:10:57+00:00 |
|
beb202e174b553589cd2e1e25142a2e6fe4bd0a4 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bhadresh-savani/bertweet-base-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bhadresh-savani](https://huggingface.co/bhadresh-savani) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-48491e5e-10845458 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T05:49:43+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "bhadresh-savani/bertweet-base-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-14T05:50:13+00:00 |
fb9fad767d82d8d50df9ca04cebfa24efe072d7a | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bhadresh-savani/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bhadresh-savani](https://huggingface.co/bhadresh-savani) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-872f08fa-10855459 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T05:56:02+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "bhadresh-savani/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-14T05:56:34+00:00 |
c377dbe9f7c7de4e6c26196dbfea36e09e85277a | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: bhadresh-savani/electra-base-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bhadresh-savani](https://huggingface.co/bhadresh-savani) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-c4654930-10865460 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T05:58:39+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "bhadresh-savani/electra-base-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-14T05:59:05+00:00 |
620a4f99bd28587ddc39712c5d7d2684e31dbf9e | # 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: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-xsum-c7d88063-10885461 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T09:15:03+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["xsum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-book-summary", "metrics": [], "dataset_name": "xsum", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "document", "target": "summary"}}} | 2022-07-15T08:10:49+00:00 |
49d2869936bc82b372c79b8779c2646872a6d55d | Zaib/java-vulnerability | [
"license:afl-3.0",
"region:us"
] | 2022-07-14T09:16:40+00:00 | {"license": "afl-3.0"} | 2022-07-14T10:09:57+00:00 |
|
f1e681e92cddae20d01fc498d685f1cf6a052d34 |
# Dataset Card for ASRS Aviation Incident Reports
## 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://huggingface.co/datasets/elihoole/asrs-aviation-reports]
- **Repository:** [ASRS Incident Reports Summarisation code repo](https://github.com/elihoole/asrs-incident-reports)
- **Point of Contact:** [Elijah Hoole](mailto:[email protected])
### Dataset Summary
This dataset collects 47,723 aviation incident reports published in the Aviation Safety Reporting System (ASRS) database maintained by NASA.
### Supported Tasks and Leaderboards
- 'summarization': Dataset can be used to train a model for abstractive and extractive summarization. The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given narrative account of an aviation incident is when compared to the synopsis as written by a NASA expert. Models and scores to follow.
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
### Data Instances
For each instance, there is a string for the narrative account (Report 1_Narrative), a string for the synopsis (Report 1.2_Synopsis), and a string for the document id (acn_num_ACN). Some instances may have two narratives (Report 1_Narrative & Report 2_Narrative) and extended analyses produced by experts (Report 1.1_Callback & Report 2.1_Callback). Other fields contain metadata such as time, location, flight conditions, aircraft model name, etc. associated with the incident. See the [ASRS Incident Reports dataset viewer](https://huggingface.co/datasets/elihoole/asrs-aviation-reports/viewer/elihoole--asrs-aviation-reports/train) to explore more examples.
```
{'acn_num_ACN': '1206196',
'Report 1_Narrative': 'While taxiing company B757 aircraft from gate to Hangar line; we were cleared by Ground Control to proceed via A-T-join runway XX. After receiving subsequent clearance to T1 [then associated taxiways] to the hangar; we caught up to a dark; apparently unpowered company livery RJ (ERJ-145) near the T1 intersection. The RJ was being towed dark with absolutely no external lighting on; a completely dark aircraft. This situation only presented itself as we drew close to the aircraft in tow. The towbarless tractor (supertug) was lit externally; but minimally visible from our vantage point; with a completely dark aircraft between us and the tractor. Once the towing operation completed a turn onto taxiway T; a single green light came in view which is somehow mounted on supertug; presented a similar appearance to a green wing navigation light common on all aircraft. To say this presented a confusing situation is an understatement. [Aircraft] operation in Noncompliance with FARs; Policy and Procedures. This is a situation never before observed in [my] 30 plus years as a taxi mechanic at our location. There are long established standards in place regarding external light usage and requirements; both in gate areas; as well as movement in active controlled taxiways; most with an eye on safety regarding aircraft position (nav lights) and anti-collision lights signaling running engines and/or aircraft movement.',
'Report 1.1_Callback': '',
'Report 2_Narrative': '',
'Report 2.1_Callback': '',
'Report 1.2_Synopsis': 'A Line Aircraft Maintenance Technician (AMT) taxiing a company B757 aircraft reports coming up on a dark; unpowered ERJ-145 aircraft with no external lighting on. Light on the towbarless Supertug tractor only minimally visible; with completely dark aircraft between their B757 and Tow tractor. Technician notes long established standards requiring Anti-Collision and Nav lights not enforced during aircraft tow.'}
```
The average token count for the articles and the highlights are provided below.
| Feature | Number of Instances | Mean Token Count |
| ------------------- | ------------------ | ---------------- |
| Report 1_Narrative | 47,723 | 281 |
| Report 1.1_Callback | 1,435 | 103 |
| Report 2_Narrative | 11,228 | 169 |
| Report 2.1 Callback | 85 | 110 |
| Report 1.2_Synopsis | 47,723 | 27 |
### Data fields
More data explanation.
| elihoole/asrs-aviation-reports | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-07-14T10:06:32+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["other"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["summarization"], "pretty_name": "ASRS Aviation Incident Reports "} | 2022-07-15T07:48:26+00:00 |
b830cf56eb00bc4edd1860dd544a192216eb3587 |
# Dataset Card for Moral Stories
## 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:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Repository:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Paper:** [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.
Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Supported Tasks and Leaderboards
- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A [BART model](https://huggingface.co/facebook/bart-large) can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.
### Languages
*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the [*Ethical Considerations* section of the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Structure
### Data Instances
Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf). Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):
"full" instance:
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",
"norm": "It's kind to wait for people.",
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",
"intention": "Josh needs to get to work.",
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",
"norm": "It's bad to be too loud and wake people up.",
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",
"label": "1"}
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",
"norm": "It's bad to be too loud and wake people up.",
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",
"label": "0"}
"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",
"norm": "It's mean to hit a bear with a guitar.",
"situation": "Joe was camping in Montana with his band when he came across a black bear.",
"intention": "Joe wanted to scare away the black bear.",
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",
"label": "1"}
### Data Fields
- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")
### Data Splits
For classification tasks, we examined three data split strategies:
- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set.
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to [*Section 3* and *Appendix C* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Creation
### Curation Rationale
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Source Data
#### Initial Data Collection and Normalization
Please refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the source language producers?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Annotations
#### Annotation process
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the annotators?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Discussion of Biases
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Other Known Limitations
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@article{Emelin2021MoralSS,
title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
journal={ArXiv},
year={2021},
volume={abs/2012.15738}
} | demelin/moral_stories | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"task_ids:text-scoring",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-07-14T10:19:52+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "text-generation", "text-classification", "commonsense-reasoning", "moral-reasoning", "social-reasoning"], "task_ids": ["multiple-choice-qa", "language-modeling", "text-scoring"], "pretty_name": "Moral Stories"} | 2022-07-17T14:29:10+00:00 |
79a0451ac1f2e0b1512e25f1a56839e4eb941c48 |
# Dataset Card for Wino-X
## 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:** [Wino-X repository](https://github.com/demelin/Wino-X)
- **Repository:** [Wino-X repository](https://github.com/demelin/Wino-X)
- **Paper:** [Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution](https://aclanthology.org/2021.emnlp-main.670/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Wino-X is a parallel dataset of German, French, and Russian Winograd schemas, aligned with their English
counterparts, used to examine whether neural machine translation models can perform coreference resolution that
requires commonsense knowledge, and whether multilingual language models are capable of commonsense reasoning across
multiple languages.
### Supported Tasks and Leaderboards
- translation: The dataset can be used to evaluate translations of ambiguous source sentences, as produced by translation models . A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
- coreference-resolution: The dataset can be used to rank alternative translations of an ambiguous source sentence that differ in the chosen referent of an ambiguous source pronoun. A [pretrained transformer-based NMT model](https://huggingface.co/Helsinki-NLP/opus-mt-en-de) can be used for this purpose.
- commonsense-reasoning: The dataset can also be used evaluate whether pretrained multilingual language models can perform commonsense reasoning in (or across) multiple languages by identifying the correct filler in a cloze completion task. An [XLM-based model](https://huggingface.co/xlm-roberta-base) can be used for this purpose.
### Languages
The dataset (both its MT and LM portions) is available in the following translation pairs: English-German, English-French, English-Russian. All English sentences included in *Wino-X* were extracted from publicly available parallel corpora, as detailed in the accompanying paper, and represent the dataset-specific language varieties. All non-English sentences were obtained through machine translation and may, as such, exhibit features of translationese.
## Dataset Structure
### Data Instances
The following represents a typical *MT-Wino-X* instance (for the English-German translation pair):
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
"translation1": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil sie zu klein war.",
"translation2": "Die Frau suchte nach einer anderen Vase für den Blumenstrauß, weil er zu klein war.",
"answer": 1,
"pronoun1": "sie",
"pronoun2": "er",
"referent1_en": "vase",
"referent2_en": "bouquet",
"true_translation_referent_of_pronoun1_de": "Vase",
"true_translation_referent_of_pronoun2_de": "Blumenstrauß",
"false_translation_referent_of_pronoun1_de": "Vase",
"false_translation_referent_of_pronoun2_de": "Blumenstrauß"}
The following represents a typical *LM-Wino-X* instance (for the English-French translation pair):
{"qID": "3UDTAB6HH8D37OQL3O6F3GXEEOF09Z-1",
"sentence": "The woman looked for a different vase for the bouquet because it was too small.",
"context_en": "The woman looked for a different vase for the bouquet because _ was too small.",
"context_fr": "La femme a cherché un vase différent pour le bouquet car _ était trop petit.",
"option1_en": "the bouquet",
"option2_en": "the vase",
"option1_fr": "le bouquet",
"option2_fr": "le vase",
"answer": 2,
"context_referent_of_option1_fr": "bouquet",
"context_referent_of_option2_fr": "vase"}
### Data Fields
For *MT-Wino-X*:
- "qID": Unique identifier ID for this dataset instance.
- "sentence": English sentence containing the ambiguous pronoun 'it'.
- "translation1": First translation candidate.
- "translation2": Second translation candidate.
- "answer": ID of the correct translation.
- "pronoun1": Translation of the ambiguous source pronoun in translation1.
- "pronoun2": Translation of the ambiguous source pronoun in translation2.
- "referent1_en": English referent of the translation of the ambiguous source pronoun in translation1.
- "referent2_en": English referent of the translation of the ambiguous source pronoun in translation2.
- "true_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the correct translation.
- "true_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the correct translation.
- "false_translation_referent_of_pronoun1_[TGT-LANG]": Target language referent of pronoun1 in the incorrect translation.
- "false_translation_referent_of_pronoun2_[TGT-LANG]": Target language referent of pronoun2 in the incorrect translation.
For *LM-Wino-X*:
- "qID": Unique identifier ID for this dataset instance.
- "sentence": English sentence containing the ambiguous pronoun 'it'.
- "context_en": Same English sentence, where 'it' is replaced by a gap.
- "context_fr": Target language translation of the English sentence, where the translation of 'it' is replaced by a gap.
- "option1_en": First filler option for the English sentence.
- "option2_en": Second filler option for the English sentence.
- "option1_[TGT-LANG]": First filler option for the target language sentence.
- "option2_[TGT-LANG]": Second filler option for the target language sentence.
- "answer": ID of the correct gap filler.
- "context_referent_of_option1_[TGT-LANG]": English translation of option1_[TGT-LANG].
- "context_referent_of_option2_[TGT-LANG]": English translation of option2_[TGT-LANG]
### Data Splits
*Wno-X* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
## Dataset Creation
### Curation Rationale
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Source Data
#### Initial Data Collection and Normalization
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
#### Who are the source language producers?
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Annotations
#### Annotation process
Please refer to [Section 2 in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
#### Who are the annotators?
Annotations were generated automatically and verified by the dataset author / curator for correctness.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Discussion of Biases
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
### Other Known Limitations
Please refer to ['Ethical Considerations' in the dataset paper](https://aclanthology.org/2021.emnlp-main.670.pdf).
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@inproceedings{Emelin2021WinoXMW,
title={Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference Resolution},
author={Denis Emelin and Rico Sennrich},
booktitle={EMNLP},
year={2021}
} | demelin/wino_x | [
"task_categories:translation",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:de",
"language:fr",
"language:ru",
"license:mit",
"region:us"
] | 2022-07-14T10:21:23+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated", "expert-generated"], "language": ["en", "de", "fr", "ru"], "license": ["mit"], "multilinguality": ["multilingual", "translation"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["translation", "coreference resolution", "commonsense reasoning"], "task_ids": ["multiple-choice-qa", "language-modeling"], "pretty_name": "Wino-X"} | 2022-07-15T21:28:18+00:00 |
cbb6e1d3a32411f1b176e4d116f37d414619a703 | This is a handcrafted english to french gender debiasing dataset
Dataset is handcrafted as per the following paper https://aclanthology.org/2020.acl-main.690/ | nickcpk/handcrafted_en_fr_data | [
"region:us"
] | 2022-07-14T10:54:26+00:00 | {} | 2022-07-14T13:42:25+00:00 |
3294fd896c134828fee32e63ca9e99ea7fc8c01d | # 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: pszemraj/led-large-book-summary
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-79c1c0d8-10905463 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T11:47:33+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-large-book-summary", "metrics": ["bleu", "perplexity"], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-14T17:31:17+00:00 |
3bb7788b5d5e27bea1fbbb9fd89bb4119da8f327 | # 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: pszemraj/bigbird-pegasus-large-K-booksum
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-79c1c0d8-10905464 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T11:47:36+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/bigbird-pegasus-large-K-booksum", "metrics": ["bleu", "perplexity"], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-15T07:27:05+00:00 |
64fe41966657fbf60c1443a84143a2081dbd50a6 | naver-clova-ix/cord-v1 | [
"license:cc-by-4.0",
"region:us"
] | 2022-07-14T12:41:50+00:00 | {"license": "cc-by-4.0"} | 2022-07-14T13:08:12+00:00 |
|
fa83741c5357d84b6f14110210b0c3a990d91f6b | Ciara/nlsn | [
"license:afl-3.0",
"region:us"
] | 2022-07-14T13:56:06+00:00 | {"license": "afl-3.0"} | 2022-07-14T13:56:07+00:00 |
|
15ea9719504f010d60ea35f300884023dbea9414 | ```bib
@article{sileo2023wikimedqa,
title={Generating multiple-choice questions for medical question answering with distractors and cue-masking},
author={Sileo, Damien and Uma, Kanimozhi and Moens, Marie-Francine},
journal={arXiv preprint arXiv:2303.07069 },
year={2023}
}
``` | sileod/wikimedqa | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] | 2022-07-14T14:09:22+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification", "multiple-choice"], "tags": ["medical"]} | 2023-05-16T06:47:46+00:00 |
c487794585c57af63b407e88cb4ff68ff49a84e5 | # 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: pszemraj/pegasus-large-summary-explain
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-79c1c0d8-10905465 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-14T17:31:38+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/pegasus-large-summary-explain", "metrics": ["bleu", "perplexity"], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-15T19:08:50+00:00 |
75280a8f3926668982a17d970708c325a412e0b9 |
# Dataset Card for Understanding Fables
## 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:** [Understanding Fables BIG-Bench entry](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/understanding_fables)
- **Repository:** [Understanding Fables BIG-Bench entry](https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/understanding_fables)
- **Paper:** [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Fables are short narratives that aim to communicate a specific lesson or wisdom, referred to as the moral. Morals can be idiomatic or provide a succinct summary of the fable. Importantly, they make explicit the communicative intent of the fable and, thus, are highly relevant to its content. A computational model capable of natural language understanding should, when presented with a fable and a set of potentially relevant morals, rank the moral that accurately captures the message communicated by the fable above the rest. Additionally, fables represent a highly unusual narrative domain, where animals and inanimate objects are anthropomorphized and referred to with gendered pronouns, i.e., a rabbit may be a she, rather than an it. Thus, to understand fables, models must abstract away from patterns commonly encountered in their training data by applying human-like characteristics to non-human actors. Overall, for a computational model to perform well on this task, it must be capable of (1) successfully identifying the core message of a short narrative, (2) identifying a moral that expresses this message among a set of distractor morals, and (3) doing so within a narrative domain that is unlike the majority of pre-training data. Thus, the evaluated large language models would need to demonstrate cross-domain generalization capability in addition to narrative comprehension.
The dataset evaluates models' ability to comprehend written narratives by asking them to select the most appropriate moral for each fable, from a set of five alternatives. In addition to the correct answer, this set contains four distractor morals, which were selected semi-automatically. To identify challenging distractor morals for each fable, sentence similarity was computed between the embeddings of each sentence in the fable and all morals found in the entire dataset, keeping ten morals that were found to be most similar to any of the fable's sentences. From this set, four distractors were selected manually, so that neither of the final distractors represents a valid moral of the fable. By construction, such distractor items are likely to pose a challenge for models that disproportionally rely on shallow heuristics as opposed to more sophisticated language-understanding strategies.
### Supported Tasks and Leaderboards
- multiple-choice: The dataset can be used to evaluate models on their ability to rank a moral that is relevant to a specified fable above distractor morals, e.g. by assigning the correct choice a lower model perplexity. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can also be used to train models to generate appropriate morals conditioned on the fable text. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.
### Languages
The text in the dataset is in contemporary American English.
## Dataset Structure
### Data Instances
A typical data point consists of a single fable, five morals - four distractors and one that correctly captures the lesson imparted by the fable, and an integer label denoting the ID of the correct answer option. An example dataset entry looks as follows:
{"story": "On a warm winter's day, the ants were busy drying corn that they had collected during summer. While they were engaged in their work, a starving grasshopper passed by and begged them for a single grain to stop his hunger. The ants, in turn, asked him why he had not collected food during the summer to prepare for the harsh winter, to which the grasshopper replied that he spent the warm days singing. Mockingly, the ants said to him in unison: "Since you spent your summer singing, then you must dance hungrily to bed in winter." What is the moral of this story?",
"answer0": "Fine clothes may disguise, but silly words will disclose a fool.",
"answer1": "Better starve free than be a fat slave.",
"answer2": "Evil wishes, like chickens, come home to roost.",
"answer3": "Grasp at the shadow and you will lose the substance.",
"answer4": "It is thrifty to prepare today for the wants of tomorrow.",
"label": 4}
### Data Fields
- "story': The fable for which the appropriate moral has to be identified
- "answerN": Moral candidates to be evaluated by the model
- "label": The ID of the moral belonging to the fable
### Data Splits
*Understanding Fables* was designed as an evaluation-only benchmark and therefore is intended to be used for zero-shot testing only. However, users are very welcome to split the data as they wish :) .
## Dataset Creation
### Curation Rationale
To comply with the requirements for inclusion in BIG-bench, each fable was manually paraphrased, to ensure that the task cannot be solved by memorising web data. Following sources were used for fable collection:
- [https://www.aesopfables.com/aesop1.html](https://www.aesopfables.com/aesop1.html)
- [https://www.aesopfables.com/aesop2.html](https://www.aesopfables.com/aesop2.html)
- [https://www.aesopfables.com/aesop3.html](https://www.aesopfables.com/aesop3.html)
- [https://www.aesopfables.com/aesop4.html](https://www.aesopfables.com/aesop4.html)
### Source Data
#### Initial Data Collection and Normalization
Paraphrasing was done by an English speaker with native-like language proficiency and an academic background in literature. The created paraphrases differ from the originals in the identity of their participants (lion was replaced with tiger, wolf with coyote etc.), their sentence and narrative structure, and their register (archaic terms such as thou have been replaced with their modern counterparts). The phrasing of the morals has also been updated in cases where the original language was noticeably archaic (e.g., o'er reach -> overreach), with changes kept to a minimum. The mean string similarity between original fables and their paraphrases is consequently low at 0.26, according to the word-level DamerauLevenshtein distance. At the same time, great care was taken not to alter the content of the fables and preserve the relevance of their respective morals. This is evidenced by the high semantic similarity between the originals and their paraphrases, with a mean of 0.78, computed as the cosine similarity between the embeddings of the originals and their paraphrases, obtained using the Sentence-Transformers library. Moreover, duplicate and near-duplicate fables were removed from the final collection, as were several thematically problematic stories, e.g., ones with sexist undertones. In total, the dataset includes 189 paraphrased, unique fables.
#### Who are the source language producers?
Original authors and transcribers of the fables (unknown), the [dataset author](demelin.github.io).
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
Several thematically problematic fables, e.g., ones with sexist undertones, were removed by the author during the dataset creation process. However, it can be that the author overlooked other problematic or harmful biases present within the dataset.
### Other Known Limitations
The dataset is very limited in size due to the small number of thematically distinct fables available online. Similarly, the focus on English fables alone is a limiting factor to be addressed in future dataset iterations.
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@article{Srivastava2022BeyondTI,
title={Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models},
author={Aarohi Srivastava and Abhinav Rastogi and Abhishek B Rao and Abu Awal Md Shoeb and Abubakar Abid and Adam Fisch and Adam R. Brown and Adam Santoro and Aditya Gupta and Adri{\`a} Garriga-Alonso and Agnieszka Kluska and Aitor Lewkowycz and Akshat Agarwal and Alethea Power and Alex Ray and Alex Warstadt and Alexander W. Kocurek and Ali Safaya and Ali Tazarv and Alice Xiang and Alicia Parrish and Allen Nie and Aman Hussain and Amanda Askell and Amanda Dsouza and Ameet Annasaheb Rahane and Anantharaman S. Iyer and Anders Johan Andreassen and Andrea Santilli and Andreas Stuhlmuller and Andrew M. Dai and Andrew D. La and Andrew Kyle Lampinen and Andy Zou and Angela Jiang and Angelica Chen and Anh Vuong and Animesh Gupta and Anna Gottardi and Antonio Norelli and Anu Venkatesh and Arash Gholamidavoodi and Arfa Tabassum and Arul Menezes and Arun Kirubarajan and Asher Mullokandov and Ashish Sabharwal and Austin Herrick and Avia Efrat and Aykut Erdem and Ayla Karakacs and Bridget R. Roberts and Bao Sheng Loe and Barret Zoph and Bartlomiej Bojanowski and Batuhan Ozyurt and Behnam Hedayatnia and Behnam Neyshabur and Benjamin Inden and Benno Stein and Berk Ekmekci and Bill Yuchen Lin and Blake Stephen Howald and Cameron Diao and Cameron Dour and Catherine Stinson and Cedrick Argueta and C'esar Ferri Ram'irez and Chandan Singh and Charles Rathkopf and Chenlin Meng and Chitta Baral and Chiyu Wu and Chris Callison-Burch and Chris Waites and Christian Voigt and Christopher D. Manning and Christopher Potts and Cindy Tatiana Ramirez and Clara Rivera and Clemencia Siro and Colin Raffel and Courtney Ashcraft and Cristina Garbacea and Damien Sileo and Daniel H Garrette and Dan Hendrycks and Dan Kilman and Dan Roth and Daniel Freeman and Daniel Khashabi and Daniel Levy and Daniel Gonz'alez and Danny Hernandez and Danqi Chen and Daphne Ippolito and Dar Gilboa and David Dohan and D. Drakard and David Jurgens and Debajyoti Datta and Deep Ganguli and Denis Emelin and Denis Kleyko and Deniz Yuret and Derek Chen and Derek Tam and Dieuwke Hupkes and Diganta Misra and Dilyar Buzan and Dimitri Coelho Mollo and Diyi Yang and Dong-Ho Lee and Ekaterina Shutova and Ekin Dogus Cubuk and Elad Segal and Eleanor Hagerman and Elizabeth Barnes and Elizabeth P. Donoway and Ellie Pavlick and Emanuele Rodol{\`a} and Emma FC Lam and Eric Chu and Eric Tang and Erkut Erdem and Ernie Chang and Ethan A. Chi and Ethan Dyer and Ethan Jerzak and Ethan Kim and Eunice Engefu Manyasi and Evgenii Zheltonozhskii and Fan Xia and Fatemeh Siar and Fernando Mart'inez-Plumed and Francesca Happ'e and François Chollet and Frieda Rong and Gaurav Mishra and Genta Indra Winata and Gerard de Melo and Germ{\'a}n Kruszewski and Giambattista Parascandolo and Giorgio Mariani and Gloria Wang and Gonzalo Jaimovitch-L'opez and Gregor Betz and Guy Gur-Ari and Hana Galijasevic and Han Sol Kim and Hannah Rashkin and Hanna Hajishirzi and Harsh Mehta and Hayden Bogar and Henry Shevlin and Hinrich Sch{\"u}tze and Hiromu Yakura and Hongming Zhang and Hubert Wong and Ian Aik-Soon Ng and Isaac Noble and Jaap Jumelet and Jack Geissinger and John Kernion and Jacob Hilton and Jaehoon Lee and Jaime Fern{\'a}ndez Fisac and J. Brooker Simon and James Koppel and James Zheng and James Zou and Jan Koco'n and Jana Thompson and Jared Kaplan and Jarema Radom and Jascha Narain Sohl-Dickstein and Jason Phang and Jason Wei and Jason Yosinski and Jekaterina Novikova and Jelle Bosscher and Jenni Marsh and Jeremy Kim and Jeroen Taal and Jesse Engel and Jesujoba Oluwadara Alabi and Jiacheng Xu and Jiaming Song and Jillian Tang and Jane W Waweru and John Burden and John Miller and John U. Balis and Jonathan Berant and Jorg Frohberg and Jos Rozen and Jos{\'e} Hern{\'a}ndez-Orallo and Joseph Boudeman and Joseph Jones and Joshua B. Tenenbaum and Joshua S. Rule and Joyce Chua and Kamil Kanclerz and Karen Livescu and Karl Krauth and Karthik Gopalakrishnan and Katerina Ignatyeva and Katja Markert and Kaustubh D. Dhole and Kevin Gimpel and Kevin Ochieng’ Omondi and Kory Wallace Mathewson and Kristen Chiafullo and Ksenia Shkaruta and Kumar Shridhar and Kyle McDonell and Kyle Richardson and Laria Reynolds and Leo Gao and Li Zhang and Liam Dugan and Lianhui Qin and Lidia Contreras-Ochando and Louis-Philippe Morency and Luca Moschella and Luca Lam and Lucy Noble and Ludwig Schmidt and Luheng He and Luis Oliveros Col'on and Luke Metz and Lutfi Kerem cSenel and Maarten Bosma and Maarten Sap and Maartje ter Hoeve and Madotto Andrea and Maheen Saleem Farooqi and Manaal Faruqui and Mantas Mazeika and Marco Baturan and Marco Marelli and Marco Maru and M Quintana and Marie Tolkiehn and Mario Giulianelli and Martha Lewis and Martin Potthast and Matthew Leavitt and Matthias Hagen and M'aty'as Schubert and Medina Baitemirova and Melissa Arnaud and Melvin Andrew McElrath and Michael A. Yee and Michael Cohen and Mi Gu and Michael I. Ivanitskiy and Michael Starritt and Michael Strube and Michal Swkedrowski and Michele Bevilacqua and Michihiro Yasunaga and Mihir Kale and Mike Cain and Mimee Xu and Mirac Suzgun and Monica Tiwari and Mohit Bansal and Moin Aminnaseri and Mor Geva and Mozhdeh Gheini and T MukundVarma and Nanyun Peng and Nathan Chi and Nayeon Lee and Neta Gur-Ari Krakover and Nicholas Cameron and Nicholas S. Roberts and Nicholas Doiron and Nikita Nangia and Niklas Deckers and Niklas Muennighoff and Nitish Shirish Keskar and Niveditha Iyer and Noah Constant and Noah Fiedel and Nuan Wen and Oliver Zhang and Omar Agha and Omar Elbaghdadi and Omer Levy and Owain Evans and Pablo Antonio Moreno Casares and Parth Doshi and Pascale Fung and Paul Pu Liang and Paul Vicol and Pegah Alipoormolabashi and Peiyuan Liao and Percy Liang and Peter W. Chang and Peter Eckersley and Phu Mon Htut and Pi-Bei Hwang and P. Milkowski and Piyush S. Patil and Pouya Pezeshkpour and Priti Oli and Qiaozhu Mei and QING LYU and Qinlang Chen and Rabin Banjade and Rachel Etta Rudolph and Raefer Gabriel and Rahel Habacker and Ram'on Risco Delgado and Rapha{\"e}l Milli{\`e}re and Rhythm Garg and Richard Barnes and Rif A. Saurous and Riku Arakawa and Robbe Raymaekers and Robert Frank and Rohan Sikand and Roman Novak and Roman Sitelew and Ronan Le Bras and Rosanne Liu and Rowan Jacobs and Rui Zhang and Ruslan Salakhutdinov and Ryan Chi and Ryan Lee and Ryan Stovall and Ryan Teehan and Rylan Yang and Sahib J. Singh and Saif M. Mohammad and Sajant Anand and Sam Dillavou and Sam Shleifer and Sam Wiseman and Samuel Gruetter and Sam Bowman and Samuel S. Schoenholz and Sanghyun Han and Sanjeev Kwatra and Sarah A. Rous and Sarik Ghazarian and Sayan Ghosh and Sean Casey and Sebastian Bischoff and Sebastian Gehrmann and Sebastian Schuster and Sepideh Sadeghi and Shadi Sameh Hamdan and Sharon Zhou and Shashank Srivastava and Sherry Shi and Shikhar Singh and Shima Asaadi and Shixiang Shane Gu and Shubh Pachchigar and Shubham Toshniwal and Shyam Upadhyay and Shyamolima Debnath and Siamak Shakeri and Simon Thormeyer and Simone Melzi and Siva Reddy and Sneha Priscilla Makini and Soo-hwan Lee and Spencer Bradley Torene and Sriharsha Hatwar and Stanislas Dehaene and Stefan Divic and Stefano Ermon and Stella Rose Biderman and Stephanie C. Lin and Stephen Prasad and Steven T. Piantadosi and Stuart M. Shieber and Summer Misherghi and Svetlana Kiritchenko and Swaroop Mishra and Tal Linzen and Tal Schuster and Tao Li and Tao Yu and Tariq A. Ali and Tatsuo Hashimoto and Te-Lin Wu and Theo Desbordes and Theodore Rothschild and Thomas Phan and Tianle Wang and Tiberius Nkinyili and Timo Schick and T. N. Kornev and Timothy Telleen-Lawton and Titus Tunduny and Tobias Gerstenberg and Trenton Chang and Trishala Neeraj and Tushar Khot and Tyler O. Shultz and Uri Shaham and Vedant Misra and Vera Demberg and Victoria Nyamai and Vikas Raunak and Vinay V. Ramasesh and Vinay Uday Prabhu and Vishakh Padmakumar and Vivek Srikumar and William Fedus and William Saunders and William Zhang and W Vossen and Xiang Ren and Xiaoyu F Tong and Xinyi Wu and Xudong Shen and Yadollah Yaghoobzadeh and Yair Lakretz and Yang Song and Yasaman Bahri and Ye Ji Choi and Yichi Yang and Yiding Hao and Yifu Chen and Yonatan Belinkov and Yu Hou and Yu Hou and Yushi Bai and Zachary Seid and Zhao Xinran and Zhuoye Zhao and Zi Fu Wang and Zijie J. Wang and Zirui Wang and Ziyi Wu and Sahib Singh and Uri Shaham},
journal={ArXiv},
year={2022},
volume={abs/2206.04615}
} | demelin/understanding_fables | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"arxiv:2206.04615",
"region:us"
] | 2022-07-14T17:52:15+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["multiple-choice", "text-generation", "text-understanding", "text-comprehension", "natural-language-understanding", "natural-language-generation"], "task_ids": ["multiple-choice-qa", "language-modeling"], "pretty_name": "Understanding Fables"} | 2022-07-17T14:04:16+00:00 |
3221702053e2bb473803a6fc25db782035951405 | This dataset was created by Deep Learning Brasil(www.deeplearningbrasil.com.br). I just published it on Hugging Face hub with the intention to share it with more people that are training brazilian portuguese models. The original link is here drive.google.com/file/d/1Q0IaIlv2h2BC468MwUFmUST0EyN7gNkn/view. | ArthurBaia/squad_v1_pt_br | [
"region:us"
] | 2022-07-14T18:55:08+00:00 | {} | 2022-11-09T15:34:43+00:00 |
1a87807da631e4197d77f7e720c38941abcf26d1 |
Queries for Lotte dataset from [ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | colbertv2/lotte | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | 2022-07-14T21:11:39+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "Lotte queries from ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", "tags": []} | 2022-08-04T16:55:59+00:00 |
3be7f857585299f1268d29d3591202d731ea84a1 |
Passages for the LoTTe dataset used for [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | colbertv2/lotte_passages | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | 2022-07-14T21:44:41+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "Lotte passages from ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction", "viewer": false, "tags": [], "dataset_info": {"features": [{"name": "doc_id", "dtype": "int32"}, {"name": "author", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "dev_collection", "num_bytes": 263355925, "num_examples": 268880}, {"name": "test_collection", "num_bytes": 105718627, "num_examples": 119458}], "download_size": 225568795, "dataset_size": 369074552}} | 2023-08-23T00:55:55+00:00 |
fe64fb6e160cef79ce72ce213ad449e1bf084bf1 | wjddmlfud/sample_dataaset | [
"license:afl-3.0",
"region:us"
] | 2022-07-15T00:36:05+00:00 | {"license": "afl-3.0"} | 2022-07-15T01:45:23+00:00 |
|
069ea5adedc59024af9dcb5ac0655604ad11681f | Raagul04/DocVQA_train | [
"license:afl-3.0",
"region:us"
] | 2022-07-15T05:54:18+00:00 | {"license": "afl-3.0"} | 2022-07-15T05:54:18+00:00 |
|
82958bf09d7d89df4057f4da29070ce88fb57b61 | # 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: pszemraj/led-large-book-summary
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-samsum-f90fd7b5-10915466 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T07:27:21+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-large-book-summary", "metrics": ["bleu"], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-07-15T08:35:16+00:00 |
017454cddb5c85def8062c929f4361b50f4491e8 | # 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: pszemraj/led-base-book-summary
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-samsum-f4288f9c-10925467 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T08:11:11+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": ["bleu"], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-07-15T08:38:05+00:00 |
936e8aef739add279dfb20352a24bfb9d388949f | # 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: pszemraj/led-base-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-899c0b5b-10935468 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T08:35:37+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": ["bleu"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}} | 2022-07-16T12:52:25+00:00 |
89b36b13527a745815e20ec785ddf270c52e64fc |
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Published version of dataset used for paper 'Towards an automatic requirements classification in a new Spanish dataset'
### Languages
Spanish
## Dataset Structure
### Data Fields
Project: Project's Identifier from which the requirements were obtained.
Requirement: Description of the software requirement.
Final label: Label of the requirement: F (functional requirement) and NF (non-functional requirement).
## Dataset Creation
### Initial Data Collection and Normalization
This dataset was created from a collection of functional and non-functional requirements extracted from 13 final degree and 2 master’s projects carried out from the University of A Coruna. It consist in 300 functional and 89 non-funtcional requirements.
## Additional Information
### Citation Information
https://doi.org/10.5281/zenodo.6556541
| MariaIsabel/FR_NFR_Spanish_requirements_classification | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"region:us"
] | 2022-07-15T11:01:21+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["es"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "Spanish requirements labeled in functional and non-functional classes."} | 2022-07-22T06:19:16+00:00 |
5157310f019772611e38adb57ce1ebe589a1f2d0 |
There are 50 music clips(of 3~5 seconds).
You can load them by the following code:
```python
from datasets import load_dataset
dataset = load_dataset('yongjian/music-clips-50')
clips = dataset['train'] # all 50 music clips
music_1_np_array = clips[0]['audio']['array'] # numpy array of shape=[N,]
```
Or you can directly download them from Google Drive: [music-clips-50.tar.gz](https://drive.google.com/file/d/154y_Z9p1Sfhrwzj7jc46UMbTaAmI17AT/view?usp=sharing). | yongjian/music-clips-50 | [
"multilinguality:other-music",
"language:en",
"language:zh",
"region:us"
] | 2022-07-15T11:40:23+00:00 | {"language": ["en", "zh"], "multilinguality": ["other-music"], "pretty_name": "music-clips-50"} | 2022-10-07T13:21:39+00:00 |
2dedccea0d29e34b977e54a4e3a9b106cfde86a3 | # 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: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-b5ccd808-10945470 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:45:37+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-book-summary", "metrics": ["bleu"], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}} | 2022-07-16T19:06:13+00:00 |
48c0cf425bd9298d153cafcaf02a9c9fc492c74f | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@postpandas](https://huggingface.co/postpandas) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-d66bcc95-10955472 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:45:49+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-15T11:46:34+00:00 |
1e31c791f31b55f5caec82618f5a69bf8471b9bc | # 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: pszemraj/bigbird-pegasus-large-K-booksum
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-5034faac-10965473 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:45:54+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/bigbird-pegasus-large-K-booksum", "metrics": ["perplexity"], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-16T07:47:50+00:00 |
716d9abaf7748fc0e34bef0986e4d3fba174f78c | # 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: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-e703e34d-10975474 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:46:01+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-book-summary", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-15T21:33:56+00:00 |
dc6e99653f818c6020880a66cc94a3901bebd738 | # 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-39317f76-10985475 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:46:08+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-16T16:30:51+00:00 |
f0c22bfb495043277bdc0cd682946f7fb642ff87 | # 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: pszemraj/pegasus-large-book-summary
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-111b8468-10995476 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:46:14+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/pegasus-large-book-summary", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-16T12:56:12+00:00 |
324635d6c6e6cd1affb2c09c89da530690c39d66 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: jsoutherland/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jsoutherland](https://huggingface.co/jsoutherland) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-emotion-21f117d5-11035480 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:47:04+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["emotion"], "eval_info": {"task": "multi_class_classification", "model": "jsoutherland/distilbert-base-uncased-finetuned-emotion", "metrics": [], "dataset_name": "emotion", "dataset_config": "default", "dataset_split": "test", "col_mapping": {"text": "text", "target": "label"}}} | 2022-07-15T11:47:33+00:00 |
5cc5981e1b29fb7740e4c2b1eb9310e30c286048 | # 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: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-f8e8ca08-11045481 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T11:48:01+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["cnn_dailymail"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-book-summary", "metrics": [], "dataset_name": "cnn_dailymail", "dataset_config": "3.0.0", "dataset_split": "test", "col_mapping": {"text": "article", "target": "highlights"}}} | 2022-07-16T19:19:05+00:00 |
9da298894c6039bb7bbb6809989ef5aece92cf51 | Rodekool/ornl26 | [
"license:gpl",
"region:us"
] | 2022-07-15T12:29:37+00:00 | {"license": "gpl"} | 2023-02-11T09:38:55+00:00 |
|
c5bc19d940ee20698ebb845bcca4cdb8dca6e488 | Dataset_chunked_5 : chunks of 05 seconds obtained from expert samples
Dataset_chunked_10 : chunks of 10 seconds obtained from expert samples
Dataset_expanded : chunks of 10 seconds obtained from whole samples
Data.zip : original dataset | nprime496/building_floor_classification | [
"region:us"
] | 2022-07-15T12:38:42+00:00 | {} | 2022-09-08T14:12:55+00:00 |
7d5c188c0bb71619f3966f8ba8f99df333f04168 | Source of dataset: [Kaggle](https://www.kaggle.com/datasets/l33tc0d3r/indian-food-classification)
This Dataset contains different images of food in 20 different classes. Some of the classes are of Indian food. All the images are extracted from google. Images per classes are little so Data augmentation and transfer learning will be best suited here.
Classes of the model: "burger", "butter_naan", "chai", "chapati", "chole_bhature", "dal_makhani", "dhokla", "fried_rice", "idli", "jalebi", "kaathi_rolls", "kadai_paneer", "kulfi", "masala_dosa", "momos", "paani_puri", "pakode", "pav_bhaji", "pizza", "samosa" | rajistics/indian_food_images | [
"task_categories:image-classification",
"region:us"
] | 2022-07-15T13:40:09+00:00 | {"task_categories": ["image-classification"]} | 2022-08-04T16:58:49+00:00 |
e524a8f5fc2bdd5e345a8bb992813952165d21bb | # 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: pszemraj/led-base-book-summary
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-samsum-22cb3f56-11055482 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T17:21:17+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-base-book-summary", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-07-15T17:48:48+00:00 |
6c07ee98f2e111eee37e96bf47af2bff73032d56 | # 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: pszemraj/led-large-book-summary
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-samsum-07954c9f-11065483 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T17:49:06+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "pszemraj/led-large-book-summary", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-07-15T18:55:26+00:00 |
5ef5ab57eb6f3b1c19493a6b9cc57c78638e6f1d | # 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: pszemraj/long-t5-tglobal-base-16384-book-summary
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-636bebc2-11085484 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T18:55:44+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-base-16384-book-summary", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-16T04:43:03+00:00 |
1362c265ff02ad01802147e1f33f60e353776404 | # 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: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP
* Dataset: kmfoda/booksum
* Config: kmfoda--booksum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. | autoevaluate/autoeval-staging-eval-project-kmfoda__booksum-f6c9ed7c-11095485 | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-15T19:09:22+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["kmfoda/booksum"], "eval_info": {"task": "summarization", "model": "pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP", "metrics": [], "dataset_name": "kmfoda/booksum", "dataset_config": "kmfoda--booksum", "dataset_split": "test", "col_mapping": {"text": "chapter", "target": "summary_text"}}} | 2022-07-17T00:12:42+00:00 |
0b174eb4c5ea22a11cbd4994499c2258dd0cc2b0 | Pogpotatofarmer/memes | [
"license:cc",
"region:us"
] | 2022-07-15T20:07:17+00:00 | {"license": "cc"} | 2022-07-15T20:11:34+00:00 |
|
bcbcef7de3b4e702e526352d825a4ff06de2becb | # MediaSum
## Description
This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries,
collected from interview transcripts and overview / topic descriptions from NPR and CNN.
### **NOTE: The authors have requested that this dataset be used for research purposes only**
## Homepage
https://github.com/zcgzcgzcg1/MediaSum
## Paper
https://arxiv.org/abs/2103.06410
## Authors
### Chenguang Zhu*, Yang Liu*, Jie Mei, Michael Zeng
#### Microsoft Cognitive Services Research Group
{chezhu,yaliu10,jimei,nzeng}@microsoft.com
## Citation
@article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
}
## Dataset size
Train: 443,596
Validation: 10,000
Test: 10,000
The splits were made by using the file located here: https://github.com/zcgzcgzcg1/MediaSum/tree/main/data
## Data details
- id (string): unique identifier
- program (string): the program this transcript came from
- date (string): date of program
- url (string): link to where audio and transcript are located
- title (string): title of the program. some datapoints do not have a title
- summary (string): summary of the program
- utt (list of string): list of utterances by the speakers in the program. corresponds with `speaker`
- speaker (list of string): list of speakers, corresponds with `utt`
Example:
```
{
"id": "NPR-11",
"program": "Day to Day",
"date": "2008-06-10",
"url": "https://www.npr.org/templates/story/story.php?storyId=91356794",
"title": "Researchers Find Discriminating Plants",
"summary": "The \"sea rocket\" shows preferential treatment to plants that are its kin. Evolutionary plant ecologist Susan Dudley of McMaster University in Ontario discusses her discovery.",
"utt": [
"This is Day to Day. I'm Madeleine Brand.",
"And I'm Alex Cohen.",
"Coming up, the question of who wrote a famous religious poem turns into a very unchristian battle.",
"First, remember the 1970s? People talked to their houseplants, played them classical music. They were convinced plants were sensuous beings and there was that 1979 movie, \"The Secret Life of Plants.\"",
"Only a few daring individuals, from the scientific establishment, have come forward with offers to replicate his experiments, or test his results. The great majority are content simply to condemn his efforts without taking the trouble to investigate their validity.",
...
"OK. Thank you.",
"That's Susan Dudley. She's an associate professor of biology at McMaster University in Hamilt on Ontario. She discovered that there is a social life of plants."
],
"speaker": [
"MADELEINE BRAND, host",
"ALEX COHEN, host",
"ALEX COHEN, host",
"MADELEINE BRAND, host",
"Unidentified Male",
..."
Professor SUSAN DUDLEY (Biology, McMaster University)",
"MADELEINE BRAND, host"
]
}
```
## Using the dataset
```python
from datasets import load_dataset
ds = load_dataset("nbroad/mediasum")
```
## Data location
https://drive.google.com/file/d/1ZAKZM1cGhEw2A4_n4bGGMYyF8iPjLZni/view?usp=sharing
## License
No license specified, but the authors have requested that this dataset be used for research purposes only. | nbroad/mediasum | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2103.06410",
"region:us"
] | 2022-07-15T20:42:51+00:00 | {"language": ["en"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization"]} | 2022-10-25T09:40:11+00:00 |
97cfbaf63ee4ea7128b5f7d95ec2af38a2f2f369 | # SCP Text+ Embeddings
This dataset is adapted from the [SCP 1to 7 corpus from Kaggle](https://www.kaggle.com/datasets/czzzzzzz/scp1to7)
We concatenated the title, state, text, and image captions columns. We also removed any rows that contained a deleted page, which trims the results down from 6999 -> 6618.
The embeddings were generated using [sentence-transformers/multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1)
Feel free to use the dataset for semantic search or text generation tasks! | hevia/scp-embeddings | [
"region:us"
] | 2022-07-15T20:51:04+00:00 | {} | 2022-07-15T21:01:22+00:00 |
1bb08f5f5c2479942aa77e567cf6c58e2104cab6 | ivv101/test_dataset | [
"license:cc0-1.0",
"region:us"
] | 2022-07-15T23:00:29+00:00 | {"license": "cc0-1.0"} | 2022-07-15T23:00:30+00:00 |
|
14fb2fd3f3e1cf02d60b4bb756ab877f8c410481 | oyk100/ChaSES-data | [
"license:cc0-1.0",
"region:us"
] | 2022-07-15T23:09:01+00:00 | {"license": "cc0-1.0"} | 2022-07-21T00:55:24+00:00 |
|
02b0b146ca6f20117bf48e19678536ace6362254 | IRVLUTD/few-sol | [
"license:mit",
"region:us"
] | 2022-07-16T02:16:24+00:00 | {"license": "mit"} | 2022-07-16T02:16:24+00:00 |
|
b18612dee0007b1f7129731dbf2f5f2ed4039ad3 |
# Dataset Card for "tner/conll2003"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://www.aclweb.org/anthology/W03-0419/](https://www.aclweb.org/anthology/W03-0419/)
- **Dataset:** CoNLL 2003
- **Domain:** News
- **Number of Entity:** 3
### Dataset Summary
CoNLL-2003 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `ORG`, `PER`, `LOC`, `MISC`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': ['SOCCER','-', 'JAPAN', 'GET', 'LUCKY', 'WIN', ',', 'CHINA', 'IN', 'SURPRISE', 'DEFEAT', '.'],
'tokens': [0, 0, 5, 0, 0, 0, 0, 3, 0, 0, 0, 0]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/conll2003/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-ORG": 1,
"B-MISC": 2,
"B-PER": 3,
"I-PER": 4,
"B-LOC": 5,
"I-ORG": 6,
"I-MISC": 7,
"I-LOC": 8
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|conll2003|14041| 3250|3453|
### Licensing Information
From the [CoNLL2003 shared task](https://www.clips.uantwerpen.be/conll2003/ner/) page:
> The English data is a collection of news wire articles from the Reuters Corpus. The annotation has been done by people of the University of Antwerp. Because of copyright reasons we only make available the annotations. In order to build the complete data sets you will need access to the Reuters Corpus. It can be obtained for research purposes without any charge from NIST.
The copyrights are defined below, from the [Reuters Corpus page](https://trec.nist.gov/data/reuters/reuters.html):
> The stories in the Reuters Corpus are under the copyright of Reuters Ltd and/or Thomson Reuters, and their use is governed by the following agreements:
>
> [Organizational agreement](https://trec.nist.gov/data/reuters/org_appl_reuters_v4.html)
>
> This agreement must be signed by the person responsible for the data at your organization, and sent to NIST.
>
> [Individual agreement](https://trec.nist.gov/data/reuters/ind_appl_reuters_v4.html)
>
> This agreement must be signed by all researchers using the Reuters Corpus at your organization, and kept on file at your organization.
### Citation Information
```
@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F. and De Meulder, Fien",
booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
year = "2003",
url = "https://www.aclweb.org/anthology/W03-0419",
pages = "142--147",
}
``` | tner/conll2003 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T09:39:09+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "CoNLL-2003"} | 2022-07-17T23:43:28+00:00 |
cf9ef57ad260810be1298ba795d83c09a915e959 |
# Dataset Card for "tner/ontonotes5"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/N06-2015/](https://aclanthology.org/N06-2015/)
- **Dataset:** Ontonotes5
- **Domain:** News
- **Number of Entity:** 8
### Dataset Summary
Ontonotes5 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `CARDINAL`, `DATE`, `PERSON`, `NORP`, `GPE`, `LAW`, `PERCENT`, `ORDINAL`, `MONEY`, `WORK_OF_ART`, `FAC`, `TIME`, `QUANTITY`, `PRODUCT`, `LANGUAGE`, `ORG`, `LOC`, `EVENT`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 5, 0, 0, 0, 0, 11, 12, 12, 12, 12, 0, 0, 7, 0, 0, 0, 0, 0],
'tokens': ['``', 'It', "'s", 'very', 'costly', 'and', 'time', '-', 'consuming', ',', "''", 'says', 'Phil', 'Rosen', ',', 'a', 'partner', 'in', 'Fleet', '&', 'Leasing', 'Management', 'Inc.', ',', 'a', 'Boston', 'car', '-', 'leasing', 'company', '.']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/onotonotes5/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-CARDINAL": 1,
"B-DATE": 2,
"I-DATE": 3,
"B-PERSON": 4,
"I-PERSON": 5,
"B-NORP": 6,
"B-GPE": 7,
"I-GPE": 8,
"B-LAW": 9,
"I-LAW": 10,
"B-ORG": 11,
"I-ORG": 12,
"B-PERCENT": 13,
"I-PERCENT": 14,
"B-ORDINAL": 15,
"B-MONEY": 16,
"I-MONEY": 17,
"B-WORK_OF_ART": 18,
"I-WORK_OF_ART": 19,
"B-FAC": 20,
"B-TIME": 21,
"I-CARDINAL": 22,
"B-LOC": 23,
"B-QUANTITY": 24,
"I-QUANTITY": 25,
"I-NORP": 26,
"I-LOC": 27,
"B-PRODUCT": 28,
"I-TIME": 29,
"B-EVENT": 30,
"I-EVENT": 31,
"I-FAC": 32,
"B-LANGUAGE": 33,
"I-PRODUCT": 34,
"I-ORDINAL": 35,
"I-LANGUAGE": 36
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|ontonotes5|59924| 8528|8262|
### Citation Information
```
@inproceedings{hovy-etal-2006-ontonotes,
title = "{O}nto{N}otes: The 90{\%} Solution",
author = "Hovy, Eduard and
Marcus, Mitchell and
Palmer, Martha and
Ramshaw, Lance and
Weischedel, Ralph",
booktitle = "Proceedings of the Human Language Technology Conference of the {NAACL}, Companion Volume: Short Papers",
month = jun,
year = "2006",
address = "New York City, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N06-2015",
pages = "57--60",
}
``` | tner/ontonotes5 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:07:45+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Ontonotes5"} | 2022-07-17T23:43:55+00:00 |
068c8163eee17ea24bdc86211efeaa9001b57c33 |
# Dataset Card for "tner/wnut2017"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/W17-4418/](https://aclanthology.org/W17-4418/)
- **Dataset:** WNUT 2017
- **Domain:** Twitter, Reddit, YouTube, and StackExchange
- **Number of Entity:** 6
### Dataset Summary
WNUT 2017 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `creative-work`, `corporation`, `group`, `location`, `person`, `product`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'],
'tags': [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 3, 9, 9, 12, 3, 12, 12, 12, 12, 12, 12, 12, 12]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wnut2017/raw/main/dataset/label.json).
```python
{
"B-corporation": 0,
"B-creative-work": 1,
"B-group": 2,
"B-location": 3,
"B-person": 4,
"B-product": 5,
"I-corporation": 6,
"I-creative-work": 7,
"I-group": 8,
"I-location": 9,
"I-person": 10,
"I-product": 11,
"O": 12
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|wnut2017 | 2395| 1009|1287|
### Citation Information
```
@inproceedings{derczynski-etal-2017-results,
title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
author = "Derczynski, Leon and
Nichols, Eric and
van Erp, Marieke and
Limsopatham, Nut",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4418",
doi = "10.18653/v1/W17-4418",
pages = "140--147",
abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}
``` | tner/wnut2017 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:08:24+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1k<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "WNUT 2017"} | 2022-08-06T22:30:30+00:00 |
e79eb66d7f3ee016c31e70ad9d48e33f15047786 |
# Dataset Card for "tner/fin"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
- **Dataset:** FIN
- **Domain:** Financial News
- **Number of Entity:** 4
### Dataset Summary
FIN NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
FIN dataset contains training (FIN5) and test (FIN3) only, so we randomly sample a half size of test instances from the training set to create validation set.
- Entity Types: `ORG`, `LOC`, `PER`, `MISC`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
"tags": [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"tokens": ["1", ".", "1", ".", "4", "Borrower", "engages", "in", "criminal", "conduct", "or", "is", "involved", "in", "criminal", "activities", ";"]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-PER": 1,
"B-LOC": 2,
"B-ORG": 3,
"B-MISC": 4,
"I-PER": 5,
"I-LOC": 6,
"I-ORG": 7,
"I-MISC": 8
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|fin |1014 | 303| 150|
### Citation Information
```
@inproceedings{salinas-alvarado-etal-2015-domain,
title = "Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment",
author = "Salinas Alvarado, Julio Cesar and
Verspoor, Karin and
Baldwin, Timothy",
booktitle = "Proceedings of the Australasian Language Technology Association Workshop 2015",
month = dec,
year = "2015",
address = "Parramatta, Australia",
url = "https://aclanthology.org/U15-1010",
pages = "84--90",
}
``` | tner/fin | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | 2022-07-16T10:08:45+00:00 | {"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "FIN"} | 2022-08-15T16:50:31+00:00 |
8d75081cb3dae70b3f59db7e8d851dbc42f9275d |
# Dataset Card for "tner/bionlp2004"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/U15-1010.pdf](https://aclanthology.org/U15-1010.pdf)
- **Dataset:** BioNLP2004
- **Domain:** Biochemical
- **Number of Entity:** 5
### Dataset Summary
BioNLP2004 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
BioNLP2004 dataset contains training and test only, so we randomly sample a half size of test instances from the training set to create validation set.
- Entity Types: `DNA`, `protein`, `cell_type`, `cell_line`, `RNA`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [0, 0, 0, 0, 3, 0, 9, 10, 0, 0, 0, 0, 0, 7, 8, 0, 3, 0, 0, 9, 10, 10, 0, 0],
'tokens': ['In', 'the', 'presence', 'of', 'Epo', ',', 'c-myb', 'mRNA', 'declined', 'and', '20', '%', 'of', 'K562', 'cells', 'synthesized', 'Hb', 'regardless', 'of', 'antisense', 'myb', 'RNA', 'expression', '.']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/fin/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-DNA": 1,
"I-DNA": 2,
"B-protein": 3,
"I-protein": 4,
"B-cell_type": 5,
"I-cell_type": 6,
"B-cell_line": 7,
"I-cell_line": 8,
"B-RNA": 9,
"I-RNA": 10
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|bionlp2004 |16619 | 1927| 3856|
### Citation Information
```
@inproceedings{collier-kim-2004-introduction,
title = "Introduction to the Bio-entity Recognition Task at {JNLPBA}",
author = "Collier, Nigel and
Kim, Jin-Dong",
booktitle = "Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications ({NLPBA}/{B}io{NLP})",
month = aug # " 28th and 29th",
year = "2004",
address = "Geneva, Switzerland",
publisher = "COLING",
url = "https://aclanthology.org/W04-1213",
pages = "73--78",
}
``` | tner/bionlp2004 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:08:59+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "BioNLP2004"} | 2022-08-10T00:01:51+00:00 |
f68cdc7db924369241e7868656f583072acd4e90 |
# Dataset Card for "tner/bc5cdr"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true](https://academic.oup.com/database/article/doi/10.1093/database/baw032/2630271?login=true)
- **Dataset:** BioCreative V CDR
- **Domain:** Biomedical
- **Number of Entity:** 2
### Dataset Summary
BioCreative V CDR NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
The original dataset consists of long documents which cannot be fed on LM because of the length, so we split them into sentences to reduce their size.
- Entity Types: `Chemical`, `Disease`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0],
'tokens': ['Fasciculations', 'in', 'six', 'areas', 'of', 'the', 'body', 'were', 'scored', 'from', '0', 'to', '3', 'and', 'summated', 'as', 'a', 'total', 'fasciculation', 'score', '.']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/bc5cdr/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-Chemical": 1,
"B-Disease": 2,
"I-Disease": 3,
"I-Chemical": 4
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|bc5cdr|5228| 5330|5865|
### Citation Information
```
@article{wei2016assessing,
title={Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task},
author={Wei, Chih-Hsuan and Peng, Yifan and Leaman, Robert and Davis, Allan Peter and Mattingly, Carolyn J and Li, Jiao and Wiegers, Thomas C and Lu, Zhiyong},
journal={Database},
volume={2016},
year={2016},
publisher={Oxford Academic}
}
``` | tner/bc5cdr | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:09:16+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "BioCreative V CDR"} | 2022-07-17T23:43:04+00:00 |
d35f3cd11c9c5c1754ef66bfcbcb6a8e632216a6 |
# Dataset Card for "tner/mit_movie_trivia"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Dataset:** MIT Movie
- **Domain:** Movie
- **Number of Entity:** 12
### Dataset Summary
MIT Movie NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `Actor`, `Plot`, `Opinion`, `Award`, `Year`, `Genre`, `Origin`, `Director`, `Soundtrack`, `Relationship`, `Character_Name`, `Quote`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [0, 13, 14, 0, 0, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4],
'tokens': ['a', 'steven', 'spielberg', 'film', 'featuring', 'a', 'bluff', 'called', 'devil', 's', 'tower', 'and', 'a', 'spectacular', 'mothership']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/mit_movie_trivia/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-Actor": 1,
"I-Actor": 2,
"B-Plot": 3,
"I-Plot": 4,
"B-Opinion": 5,
"I-Opinion": 6,
"B-Award": 7,
"I-Award": 8,
"B-Year": 9,
"B-Genre": 10,
"B-Origin": 11,
"I-Origin": 12,
"B-Director": 13,
"I-Director": 14,
"I-Genre": 15,
"I-Year": 16,
"B-Soundtrack": 17,
"I-Soundtrack": 18,
"B-Relationship": 19,
"I-Relationship": 20,
"B-Character_Name": 21,
"I-Character_Name": 22,
"B-Quote": 23,
"I-Quote": 24
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|mit_movie_trivia |6816 | 1000| 1953|
| tner/mit_movie_trivia | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:12:14+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MIT Movie"} | 2022-07-18T09:24:52+00:00 |
538663410a86a70f788b0c193d42320de330cc0d |
# Dataset Card for "tner/mit_restaurant"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Dataset:** MIT restaurant
- **Domain:** Restaurant
- **Number of Entity:** 8
### Dataset Summary
MIT Restaurant NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `Rating`, `Amenity`, `Location`, `Restaurant_Name`, `Price`, `Hours`, `Dish`, `Cuisine`.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tags': [0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 0],
'tokens': ['can', 'you', 'find', 'the', 'phone', 'number', 'for', 'the', 'closest', 'family', 'style', 'restaurant']
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/mit_restaurant/raw/main/dataset/label.json).
```python
{
"O": 0,
"B-Rating": 1,
"I-Rating": 2,
"B-Amenity": 3,
"I-Amenity": 4,
"B-Location": 5,
"I-Location": 6,
"B-Restaurant_Name": 7,
"I-Restaurant_Name": 8,
"B-Price": 9,
"B-Hours": 10,
"I-Hours": 11,
"B-Dish": 12,
"I-Dish": 13,
"B-Cuisine": 14,
"I-Price": 15,
"I-Cuisine": 16
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|mit_restaurant |6900 | 760| 1521|
| tner/mit_restaurant | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"region:us"
] | 2022-07-16T10:12:45+00:00 | {"language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "MIT Restaurant"} | 2022-08-10T10:25:17+00:00 |
6a4318523b3f89dc8cadde67a48397563d8e6c7e | spy24/Lie_Detection | [
"region:us"
] | 2022-07-16T11:08:58+00:00 | {} | 2022-07-16T11:12:02+00:00 |
|
aa16462095062503b338cff04757d6cfe7ff1194 | Chamroush/Exclusive | [
"license:artistic-2.0",
"region:us"
] | 2022-07-16T13:25:35+00:00 | {"license": "artistic-2.0"} | 2022-07-16T13:25:35+00:00 |
|
e1e1c9c6df62fc24117639ec35e02e06abb9c493 |
# Dataset Card for "SumPubmed"
## Original Dataset Description
- **Repository:** [https://github.com/vgupta123/sumpubmed](https://github.com/vgupta123/sumpubmed)
- **Paper:** [More Information Needed](https://vgupta123.github.io/docs/121_paper.pdf)
## Description of dataset processing
5 rows were dropped from the original dataset taken from KAGGLE as they were missing the respective 'shorter_abstract' entries.
The 'line_text' and 'filename_text' columns were left untouched while the remaining ones were processed to remove the '\n' (many repetitions of those present in the original dataset), '\<dig\>', '\<cit\>', 'BACKGROUND', 'RESULTS' and 'CONCLUSIONS' matching strings which were deemed not necessary for the purpose of summarization. Additionally, extra spaces were removed and spacing around punctuations was fixed.
| Blaise-g/SumPubmed | [
"language:en",
"region:us"
] | 2022-07-16T14:09:11+00:00 | {"language": ["en"], "pretty_name": "SumPubmed", "train-eval-index": [{"config": "Blaise-g--SumPubmed", "task": "summarization", "task_id": "summarization", "splits": {"eval_split": "test"}, "col_mapping": {"text": "text", "abstract": "target"}}]} | 2022-07-28T18:53:40+00:00 |
979af3bcd84565e3f47b9eca752d8ec112824953 |
# Data source
This data has been collected through a standard scraping process from the [Medium website](https://medium.com/), looking for published articles.
# Data description
Each row in the data is a different article published on Medium. For each article, you have the following features:
- **title** *[string]*: The title of the article.
- **text** *[string]*: The text content of the article.
- **url** *[string]*: The URL associated to the article.
- **authors** *[list of strings]*: The article authors.
- **timestamp** *[string]*: The publication datetime of the article.
- **tags** *[list of strings]*: List of tags associated to the article.
# Data analysis
You can find a very quick data analysis in this [notebook](https://www.kaggle.com/code/fabiochiusano/medium-articles-simple-data-analysis).
# What can I do with this data?
- A multilabel classification model that assigns tags to articles.
- A seq2seq model that generates article titles.
- Text analysis.
- Finetune text generation models on the general domain of Medium, or on specific domains by filtering articles by the appropriate tags.
# Collection methodology
Scraping has been done with Python and the requests library. Starting from a random article on Medium, the next articles to scrape are selected by visiting:
1. The author archive pages.
2. The publication archive pages (if present).
3. The tags archives (if present).
The article HTML pages have been parsed with the [newspaper Python library](https://github.com/codelucas/newspaper).
Published articles have been filtered for English articles only, using the Python [langdetect library](https://pypi.org/project/langdetect/).
As a consequence of the collection methodology, the scraped articles are coming from a not uniform publication date distribution. This means that there are articles published in 2016 and in 2022, but the number of articles in this dataset published in 2016 is not the same as the number of articles published in 2022. In particular, there is a strong prevalence of articles published in 2020. Have a look at the [accompanying notebook](https://www.kaggle.com/code/fabiochiusano/medium-articles-simple-data-analysis) to see the distribution of the publication dates. | fabiochiu/medium-articles | [
"license:mit",
"region:us"
] | 2022-07-16T14:34:11+00:00 | {"license": "mit"} | 2022-07-17T14:17:09+00:00 |
716b0ac78c49c4bfb32b449dbd394397fc0f0d69 | This dataset is based on the dataset originally posted in [Kaggle](https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets?resource=download) | jvanz/portuguese_sentiment_analysis | [
"region:us"
] | 2022-07-16T17:27:31+00:00 | {} | 2022-09-05T19:23:58+00:00 |
1ee9d2501a656d9e59c31f9620e979d3669bb2c0 |
# esCorpius Multilingual
In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, they present important shortcomings for languages different from English, as they are either too small, or present a low quality derived from sub-optimal cleaning and deduplication. In this repository, we introduce esCorpius-m, a multilingual crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in some of the languages covered with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius-m has been released under CC BY-NC-ND 4.0 license.
## Usage
Replace `revision` with the language of your choice (in this case, `it` for Italian):
```
dataset = load_dataset('LHF/escorpius-m', split='train', streaming=True, revision='it')
```
## Other corpora
- esCorpius-mr multilingual *raw* corpus (not deduplicated): https://huggingface.co/datasets/LHF/escorpius-mr
- esCorpius original *Spanish only* corpus (deduplicated): https://huggingface.co/datasets/LHF/escorpius
## Citation
Link to paper: https://www.isca-speech.org/archive/pdfs/iberspeech_2022/gutierrezfandino22_iberspeech.pdf / https://arxiv.org/abs/2206.15147
Cite this work:
```
@inproceedings{gutierrezfandino22_iberspeech,
author={Asier Gutiérrez-Fandiño and David Pérez-Fernández and Jordi Armengol-Estapé and David Griol and Zoraida Callejas},
title={{esCorpius: A Massive Spanish Crawling Corpus}},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
year=2022,
booktitle={Proc. IberSPEECH 2022},
pages={126--130},
doi={10.21437/IberSPEECH.2022-26}
}
```
## Disclaimer
We did not perform any kind of filtering and/or censorship to the corpus. We expect users to do so applying their own methods. We are not liable for any misuse of the corpus.
| LHF/escorpius-m | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"multilinguality:multilingual",
"size_categories:100B<n<1T",
"source_datasets:original",
"language:af",
"language:ar",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:gl",
"language:hi",
"language:hr",
"language:it",
"language:ja",
"language:ko",
"language:mt",
"language:nl",
"language:no",
"language:oc",
"language:pa",
"language:pl",
"language:pt",
"language:ro",
"language:sl",
"language:sr",
"language:sv",
"language:tr",
"language:uk",
"language:ur",
"license:cc-by-nc-nd-4.0",
"arxiv:2206.15147",
"region:us"
] | 2022-07-16T17:37:38+00:00 | {"language": ["af", "ar", "bn", "ca", "cs", "da", "de", "el", "eu", "fa", "fi", "fr", "gl", "hi", "hr", "it", "ja", "ko", "mt", "nl", false, "oc", "pa", "pl", "pt", "ro", "sl", "sr", "sv", "tr", "uk", "ur"], "license": "cc-by-nc-nd-4.0", "multilinguality": ["multilingual"], "size_categories": ["100B<n<1T"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"]} | 2023-05-11T21:28:28+00:00 |
e96ea6ddaa0e40b764b322ca4ed15981343fbfce | [Needs More Information]
# Dataset Card for Old Bailey Proceedings
## 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://www.dhi.ac.uk/projects/old-bailey/
- **Repository:** https://www.dhi.ac.uk/san/data/oldbailey/
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** The University of Sheffield
Digital Humanities Institute
34 Gell Street
Sheffield S3 7QY
### Dataset Summary
**Note** We are making this dataset available via the HuggingFace hub to open it up to more users and use cases. We have focused primarily on making an initial version of this dataset available, focusing on some potential use cases. If you think there are other configurations this dataset should support, please use the community tab to open an issue.
The dataset consists of 2,163 transcriptions of the Proceedings and 475 Ordinary's Accounts marked up in TEI-XML, and contains some documentation covering the data structure and variables. Each Proceedings file represents one session of the court (1674-1913), and each Ordinary's Account file represents a single pamphlet (1676-1772).
### Supported Tasks and Leaderboards
- `language-modeling`: This dataset can be used to contribute to the training or evaluation of language models for historical texts. Since it represents transcription from court proceedings, the language in this dataset may better represent the variety of language used at the time.
- `text-classification`: This dataset can be used to classify what style of English some text is in
- `named-entity-recognition`: Some of the text contains names of people and places. We don't currently provide the token IDs for these entities but do provide the tokens themselves. This means this dataset has the potential to be used to evaluate the performance of other Named Entity Recognition models on this dataset.
### Languages
`en`
## Dataset Structure
### Data Instances
An example of one instance from the dataset:
```python
{
'id': 'OA16760517',
'text': "THE CONFESSION AND EXECUTION Of the Prisoners at TYBURN On Wednesday the 17May1676. Viz. Henry Seabrook , Elizabeth Longman Robert Scot , Condemned the former Sessions. Edward Wall , and Edward Russell . Giving a full
and satisfactory Account of their Crimes, Behaviours, Discourses in Prison, and last Words (as neer as could be taken) at the place of Execution. Published for a Warning, to all that read it, to avoid the like wicked Courses, which brought these poor people to this shameful End. THE CONFESSION AND EXECUTION Of the Prisoners at TYBURN On Wednesday the 17th of May, 1676. Viz. Henry Seabrook , Elizabeth Longman Robert Scot , Condemned the former Sessions. Edward Wall , and Edward Russell . Giving a full and satisfactory Account of their Crimes, Behaviours, Discourses in Prison, and last Words (as neer as could be taken) at the place of Execution. Published for a Warning, to all that read it, to avoid the like wicked Courses, which brought these poor people to this shameful End. However, Mercy so far interposed after the Sentence of Justice, that only Five of them actually suffered: Amongst whom was Elizabeth Longman , an old Offendor, having been above a Dozen several times in Newgate : Some time since she was convicted, and obtained the benefit and favour of Transportation, and was accordingly carried into Virginia : But Clum, non Animutant, qu: trans mare currunt. She had not been there above Fourteen Moneths, before she
procured Monies remitted from some of the Brotherhood here, wherewith she bought off her Servitude, and ever she comes again into England , long before the term of her Sentence was expired. Nor was she content to violate the Law only in that point, bur returned to her old Trade (for so these people call stealing) as well as to her Countrey; and was soon after her Arrival conducted to Newgate , for mistaking several parcels of Silk, upon which being Convicted, and pleading her Belly, she was
set by the last Sessions before this: But now it appearing that she was highly accessary (though all the while in Newgate ) to the Robbery of a Person of Quality, and that she was wholly incorrigible, not to be reclaimed by any Warnings, she was brought down again to the Bar, and demanded, what she could say for her self, why she should not suffer Death, according to Law, upon her old Judgment. To which she still pleaded, that she was quick with Child. But being searched by a Jury of Matrons, they found no such thing; so that she was carried with the rest into the Hole, and ordered for Execution. As for her behaviour, I am sorry no better account can be given of it; for truely she did not seem so sensible of her End, or to make that serious preparation for it, as night be expected from a Person in her condition: yet were not the charitable assistances and endeavours of the Ordinary and several other Ministers wanting towards her, though 'tis feared they did not make the wisht-for Impressions upon her Spirit. Two others viz. Edward Wall and Edward Russel that suffered, were brought to this untimely and ignominious End, by the means and seducements of this unhappy Woman. For they together with one A. M. going after the former Sessions to a Gentlemans House, to sollicite and engage his Interest, in order to the obtaining of a Reprieve for a Woman that past for one of their Wives, and was then under Condemnation, they chanced to spie the Maid a scowring a very considerable quantity of Plate, the glittering sight whereof so much affected them, that when they came back to Newgate , to give an account of their business, amongst other discourse, they mentioned what abundance of Plate they saw. And will you only see it? (says this Besse Longman , being by) then you deserve to starve indeed, when Fortune puts Booty, as it were, in your Mouths, and you are such Cowards, that you dare not take it: With these and many other words to that purpose, she animated them on so far, till by her Instigation and the Devils together, they resolved upon the Villany, and accordingly went the next Night, broke open the Gentlemans House, and took thence a great quantity of Plate: But upon description and search, A. M: was taken next Morning on saffron-hill , with a Silver Ladle, a Silver Porringer, and that famous Engine of Wickedness, called Betty. He was carried for the present to New prison , and there kept till he had discovered the othe. Parties; and upon his ingenu u Confession obtained the Mercy of a Repeve from that Execution, which his Fellow Criminals now suffer'd. The other person executed, was Henry Sea brooke : He was condemned the former Sessions for robbing the Merchant at Dukes Place ; but upon his pretending to discover the rest of the Cabal, and other great matters, was kept from the Gibbet all this, while; but now failing to verifie those pretentions, he was ordered by the Court to receive his punishment according to his former Sentence, with the resof the Prisoners condemned this Sessions. Of these poor wretches, two, viz Wall and Russell, as they ingenuously pleaded guilty to their Indictment at the Bar, so they behaved themselves very modestly at their Condemnation; and afterwards in Prison when Ministers' came to visit and discourse with them, in order to their Souls everlasting good, they received them with great expressions of joy and este, attending with much reverence and seeming heed to their Spiritual Instruction, who with most necessary and importunate Exhortations pressed them to a speedy and hearty Repentance, Since it stood them so much in hand, being upon the brink of Eternity, they told them, Their Condition was sad, as being justly sentenced by Men to a temporal Death; but that was infinitely short of being condemned by God, and suffering Eternal Death under the ury of his Wrath: that though it was vin for them to flatter themselves with hopes of onger life in this world, yet there were
means est to secure them of Everlasting Life in the ext: and that to such vile sinners as they nd been, it was an unspeakable Mercy, that hey had yet a little space left them, wherein make their peace with Heaven; and what ould the damned Souls, weltring without pe in Eternal Flames, give or do for such a recious opportunity? With such and many her pious Admonitions and Prescriptions did ese Spiritual Physicians endeavour to cure e Ulcers of their Souls, and excite them to row off the peccant matter, and wash away i Iniquities with tears of a sincere Repennce, proceeding not from a sense of approa- ching Punishment, but of trouble for the Evil itself, and their provoking of God thereby. To all which they gave very great attention, promising to put that blessed Advice in practice; and so continued in a very serious and laudable frame till the time of Execution, which was the 17May, being then conducted to Tyburn with vest numbers of people following the Carts to behold the last
sad Scene of their deplorable Tragedy. Being come to the Gallows, and the usual Prayers and Solemnities being performed, one of them spoke a pretty while to the Multitude, protesting, This was the first Face that he was ever actually guilty of, though he had been accessary to divers others, and had been all his days a very ill Liver; so that he could not but acknowledge that he suffer'd justly. He very much admonish'd all persons to consider their ways; especially warning Youth not to misspend their time in Idleness, or Disobedience to Parents or Masters; and to have a care of being seduced and drawn away by led women. affirming that such Courses and their Temptations, and to satisfie their Luxury, had been originally the cause of his destruction, and that shameful death he was now going to suffer. The rest said very few words, unless to some particular Acquaintance; but by their Gestures seemed to pray secretly, and so were all Executed according to Sentence.",
'places': ['TYBURN', 'TYBURN', 'Newgate', 'Virginia', 'England', 'Newgate', 'Newgate', 'Newgate', 'saffron-hill', 'New prison', 'Dukes Place', 'Tyburn'],
'type': 'OA',
'persons': ['Henry Seabrook', 'Elizabeth Longman', 'Robert Scot', 'Edward Wall', 'Edward Russell', 'Henry Seabrook', 'Elizabeth Longman', 'Robert Scot', 'Edward Wall', 'Edward Russell', 'Elizabeth Longman', 'Edward Wall', 'Edward Russel', 'Besse Longman', 'Henry Sea brooke'],
'date': '16760517'}
```
### Data Fields
- `id`: A unique identifier for the data point (in this case, a trial)
- `text`: The text of the proceeding
- `places`: The places mentioned in the text
- `type`: This can be either 'OA' or 'OBP'. OA is "Ordinary's Accounts" and OBP is "Sessions Proceedings"
- `persons`: The persons named in the text
- `date`: The date of the text
### Data Splits
This dataset only contains a single split:
Train: `2638` examples
## Dataset Creation
### Curation Rationale
Between 1674 and 1913 the Proceedings of the Central Criminal Court in London, the Old Bailey, were published eight times a year. These records detail 197,000 individual trials and contain 127 million words in 182,000 pages. They represent the largest single source of information about non-elite lives and behaviour ever published and provide a wealth of detail about everyday life, as well as valuable systematic evidence of the circumstances surrounding the crimes and lives of victims and the accused, and their trial outcomes. This project created a fully digitised and structured version of all surviving published trial accounts between 1674 and 1913, and made them available as a searchable online resource.
### Source Data
#### Initial Data Collection and Normalization
Starting with microfilms of the original Proceedings and Ordinary's Accounts, page images were scanned to create high definition, 400dpi TIFF files, from which GIF and JPEG files have been created for transmission over the internet. The uncompressed TIFF files will be preserved for archival purposes and should eventually be accessible over the web once data transmission speeds improve. A GIF format has been used to transmit image files for the Proceedings published between 1674 and 1834.
#### Who are the source language producers?
The text of the 1674 to October 1834 Proceedings was manually typed by the process known as "double rekeying", whereby the text is typed in twice, by two different typists. Then the two transcriptions are compared by computer. Differences are identified and then resolved manually. This process was also used to create a transcription of the Ordinary's Accounts. This process means this text data contains fewer errors than many historical text corpora produced using Optical Character Recognition.
### Annotations
#### Annotation process
The markup was done by a combination of automated and manual processes.
Most of the 1674 to October 1834 markup was done manually by a team of five data developers working at the Humanities Research Institute at the University of Sheffield (see project staff).
However, person names were tagged using an automated markup programme, GATE, developed by the Department of Computer Science at the University of Sheffield and specially customised to process the text of the Proceedings. Most of the 1674-1834 trial proceedings were run through GATE, which was able to identify approximately 80-90% of the names in the text. GATE was asked only to identify names where both a forename (not just an initial) and surname were given. The names not identified by this programme were not regularly marked up manually unless they were the names of defendants or victims.
The November 1834 to 1913 text was first run through an automated markup process. This process was carried out by the Digital Humanities Institute Sheffield.
Remaining markup, including checking of the results of the automated markup, was carried out by a team of eight data developers employed by the University of Hertfordshire (see project staff).
#### Who are the annotators?
- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).
- The Project Manager is Dr Sharon Howard.
- The technical officer responsible for programming the search engines is Jamie McLaughlin.
- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.
- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, Nicola Wilcox, and Catherine Wright.
- The London researcher was Mary Clayton.
- The technical officers responsible for the automated markup were Ed MacKenzie and Katherine Rogers.
- Project staff who worked on the 1674-1834 phase of the project include Dr Louise Henson (Senior Data Developer), Dr John Black, Dr Edwina Newman, Kay O'Flaherty, and Gwen Smithson.
### Personal and Sensitive Information
-This dataset contains personal information of people involved in criminal proceedings during the time period
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
- "Virtually every aspect of English life between 1674 and 1913 was influenced by gender, and this includes behaviour documented in the Old Bailey Proceedings. Long-held views about the particular strengths, weaknesses, and appropriate responsibilities of each sex shaped everyday lives, patterns of crime, and responses to crime." This dataset contains text that adheres to those stereotypes.
- "The make-up of London's population changed and changed again during the course of the two and a half centuries after 1674. European Protestant refugees, blacks discharged from the armies of a growing empire, and Jews from Spain and Eastern Europe, Irish men and women, Lascars and political refugees from the revolutions of the nineteenth century contributed to the ragout of communities that made up this world city. Information about all these communities, and several more besides, can be found in the Proceedings"
### Other Known Limitations
## Additional Information
### Dataset Curators
- The directors of this project, and authors of all the historical background pages, are Professor Clive Emsley (Open University), Professor Tim Hitchcock (University of Sussex) and Professor Robert Shoemaker (University of Sheffield).
- The Project Manager is Dr Sharon Howard.
- The technical officer responsible for programming the search engines is Jamie McLaughlin.
- The Senior Data Developer, in charge of all the tagging procedures, was Dr Philippa Hardman.
- The other Data Developers were Anna Bayman, Eilidh Garrett, Carol Lewis-Roylance, Susan Parkinson, Anna Simmons, Gwen Smithson, - Nicola Wilcox, and Catherine Wright.
### Licensing Information
[CC-NY-04](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
@article{Howard2017,
author = "Sharon Howard",
title = "{Old Bailey Online XML Data}",
year = "2017",
month = "4",
url = "https://figshare.shef.ac.uk/articles/dataset/Old_Bailey_Online_XML_Data/4775434",
doi = "10.15131/shef.data.4775434.v2"
}
Thanks to [@shamikbose](https://github.com/shamikbose) for adding this dataset. | biglam/old_bailey_proceedings | [
"task_categories:text-classification",
"task_categories:text-generation",
"task_ids:multi-class-classification",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-16T19:14:17+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated", "machine-generated"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification", "text-generation"], "task_ids": ["multi-class-classification", "language-modeling", "masked-language-modeling"], "pretty_name": "Old Bailey Proceedings", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "places", "sequence": "string"}, {"name": "type", "dtype": "string"}, {"name": "persons", "sequence": "string"}, {"name": "date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 719949847, "num_examples": 2638}], "download_size": 370751172, "dataset_size": 719949847}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2024-01-08T15:39:17+00:00 |
ef520080129df6ec7fda0df347b5f7eacdf0dc1c | For test purposes!
Preprocessed version of https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-dataset
Images resized to have max 512 | ceyda/fashion-products-small | [
"region:us"
] | 2022-07-16T20:04:41+00:00 | {} | 2022-07-21T07:24:03+00:00 |
6e98d95ddee00d17778472d8e3ad7da227168901 |
# Dataset Card for Large Logo Dataset (LLD)
## Description
Adapted from the original [LLD dataset](https://data.vision.ee.ethz.ch/sagea/lld/). Original description:
> Designing a logo for a new brand is a lengthy and tedious back-and-forth process between a designer and a client. In this paper we explore to what extent machine learning can solve the creative task of the designer. For this, we build a dataset -- LLD -- of 600k+ logos crawled from the world wide web. Training Generative Adversarial Networks (GANs) for logo synthesis on such multi-modal data is not straightforward and results in mode collapse for some state-of-the-art methods. We propose the use of synthetic labels obtained through clustering to disentangle and stabilize GAN training. We are able to generate a high diversity of plausible logos and we demonstrate latent space exploration techniques to ease the logo design task in an interactive manner. Moreover, we validate the proposed clustered GAN training on CIFAR 10, achieving state-of-the-art Inception scores when using synthetic labels obtained via clustering the features of an ImageNet classifier. GANs can cope with multi-modal data by means of synthetic labels achieved through clustering, and our results show the creative potential of such techniques for logo synthesis and manipulation.
## Schema
``` yaml
- name: <string> Name of the company / organization
- description: <string> Description of what the organization does
- images: <np.uint8, shape(3, 400, 400)> Three logo images of 400x400
```
## Citations
``` text
@misc{sage2017logodataset,
author={Sage, Alexander and Agustsson, Eirikur and Timofte, Radu and Van Gool, Luc},
title = {LLD - Large Logo Dataset - version 0.1},
year = {2017},
howpublished = "\url{https://data.vision.ee.ethz.ch/cvl/lld}"}
```
| diwank/lld | [
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | 2022-07-17T06:33:12+00:00 | {"language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "pretty_name": "Large Logo Dataset"} | 2022-08-09T09:48:34+00:00 |
9660f10339cfc46a369a4d95fccb301a739c3fa8 |
# Dataset Card for Flores200
## Table of Contents
- [Dataset Card for Flores200](#dataset-card-for-flores200)
- [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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Home:** [Flores](https://github.com/facebookresearch/flores)
- **Repository:** [Github](https://github.com/facebookresearch/flores)
### Dataset Summary
FLORES is a benchmark dataset for machine translation between English and low-resource languages.
>The creation of FLORES200 doubles the existing language coverage of FLORES-101.
Given the nature of the new languages, which have less standardization and require
more specialized professional translations, the verification process became more complex.
This required modifications to the translation workflow. FLORES-200 has several languages
which were not translated from English. Specifically, several languages were translated
from Spanish, French, Russian and Modern Standard Arabic. Moreover, FLORES-200 also
includes two script alternatives for four languages. FLORES-200 consists of translations
from 842 distinct web articles, totaling 3001 sentences. These sentences are divided
into three splits: dev, devtest, and test (hidden). On average, sentences are approximately
21 words long.
**Disclaimer**: *The Flores200 dataset is hosted by the Facebook and licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/).
### Supported Tasks and Leaderboards
#### Multilingual Machine Translation
Refer to the [Dynabench leaderboard](https://dynabench.org/flores/Flores%20MT%20Evaluation%20(FULL)) for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on [Large-Scale Multilingual Machine Translation](http://www.statmt.org/wmt21/large-scale-multilingual-translation-task.html). Flores 200 is an extention of this.
### Languages
The dataset contains parallel sentences for 200 languages, as mentioned in the original [Github](https://github.com/facebookresearch/flores/blob/master/README.md) page for the project. Languages are identified with the ISO 639-3 code (e.g. `eng`, `fra`, `rus`) plus an additional code describing the script (e.g., "eng_Latn", "ukr_Cyrl"). See [the webpage for code descriptions](https://github.com/facebookresearch/flores/blob/main/flores200/README.md).
Use the configuration `all` to access the full set of parallel sentences for all the available languages in a single command.
Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-ukr_Cyrl" will provide sentences in the format below).
## Dataset Structure
### Data Instances
A sample from the `dev` split for the Ukrainian language (`ukr_Cyrl` config) is provided below. All configurations have the same structure, and all sentences are aligned across configurations and splits.
```python
{
'id': 1,
'sentence': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.',
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0
}
```
When using a hyphenated pairing or using the `all` function, data will be presented as follows:
```python
{
'id': 1,
'URL': 'https://en.wikinews.org/wiki/Scientists_say_new_medical_diagnostic_chip_can_sort_cells_anywhere_with_an_inkjet',
'domain': 'wikinews',
'topic': 'health',
'has_image': 0,
'has_hyperlink': 0,
'sentence_eng_Latn': 'On Monday, scientists from the Stanford University School of Medicine announced the invention of a new diagnostic tool that can sort cells by type: a tiny printable chip that can be manufactured using standard inkjet printers for possibly about one U.S. cent each.',
'sentence_ukr_Cyrl': 'У понеділок, науковці зі Школи медицини Стенфордського університету оголосили про винайдення нового діагностичного інструменту, що може сортувати клітини за їх видами: це малесенький друкований чіп, який можна виготовити за допомогою стандартних променевих принтерів десь по одному центу США за штуку.'
}
```
The text is provided as-in the original dataset, without further preprocessing or tokenization.
### Data Fields
- `id`: Row number for the data entry, starting at 1.
- `sentence`: The full sentence in the specific language (may have _lang for pairings)
- `URL`: The URL for the English article from which the sentence was extracted.
- `domain`: The domain of the sentence.
- `topic`: The topic of the sentence.
- `has_image`: Whether the original article contains an image.
- `has_hyperlink`: Whether the sentence contains a hyperlink.
### Data Splits
| config| `dev`| `devtest`|
|-----------------:|-----:|---------:|
|all configurations| 997| 1012:|
### Dataset Creation
Please refer to the original article [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) for additional information on dataset creation.
## Additional Information
### Dataset Curators
See paper for details.
### Licensing Information
Licensed with Creative Commons Attribution Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@article{nllb2022,
author = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},
title = {No Language Left Behind: Scaling Human-Centered Machine Translation},
year = {2022}
}
```
| Muennighoff/flores200 | [
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|flores",
"language:ace",
"language:acm",
"language:acq",
"language:aeb",
"language:afr",
"language:ajp",
"language:aka",
"language:als",
"language:amh",
"language:apc",
"language:arb",
"language:ars",
"language:ary",
"language:arz",
"language:asm",
"language:ast",
"language:awa",
"language:ayr",
"language:azb",
"language:azj",
"language:bak",
"language:bam",
"language:ban",
"language:bel",
"language:bem",
"language:ben",
"language:bho",
"language:bjn",
"language:bod",
"language:bos",
"language:bug",
"language:bul",
"language:cat",
"language:ceb",
"language:ces",
"language:cjk",
"language:ckb",
"language:crh",
"language:cym",
"language:dan",
"language:deu",
"language:dik",
"language:dyu",
"language:dzo",
"language:ell",
"language:eng",
"language:epo",
"language:est",
"language:eus",
"language:ewe",
"language:fao",
"language:fij",
"language:fin",
"language:fon",
"language:fra",
"language:fur",
"language:fuv",
"language:gaz",
"language:gla",
"language:gle",
"language:glg",
"language:grn",
"language:guj",
"language:hat",
"language:hau",
"language:heb",
"language:hin",
"language:hne",
"language:hrv",
"language:hun",
"language:hye",
"language:ibo",
"language:ilo",
"language:ind",
"language:isl",
"language:ita",
"language:jav",
"language:jpn",
"language:kab",
"language:kac",
"language:kam",
"language:kan",
"language:kas",
"language:kat",
"language:kaz",
"language:kbp",
"language:kea",
"language:khk",
"language:khm",
"language:kik",
"language:kin",
"language:kir",
"language:kmb",
"language:kmr",
"language:knc",
"language:kon",
"language:kor",
"language:lao",
"language:lij",
"language:lim",
"language:lin",
"language:lit",
"language:lmo",
"language:ltg",
"language:ltz",
"language:lua",
"language:lug",
"language:luo",
"language:lus",
"language:lvs",
"language:mag",
"language:mai",
"language:mal",
"language:mar",
"language:min",
"language:mkd",
"language:mlt",
"language:mni",
"language:mos",
"language:mri",
"language:mya",
"language:nld",
"language:nno",
"language:nob",
"language:npi",
"language:nso",
"language:nus",
"language:nya",
"language:oci",
"language:ory",
"language:pag",
"language:pan",
"language:pap",
"language:pbt",
"language:pes",
"language:plt",
"language:pol",
"language:por",
"language:prs",
"language:quy",
"language:ron",
"language:run",
"language:rus",
"language:sag",
"language:san",
"language:sat",
"language:scn",
"language:shn",
"language:sin",
"language:slk",
"language:slv",
"language:smo",
"language:sna",
"language:snd",
"language:som",
"language:sot",
"language:spa",
"language:srd",
"language:srp",
"language:ssw",
"language:sun",
"language:swe",
"language:swh",
"language:szl",
"language:tam",
"language:taq",
"language:tat",
"language:tel",
"language:tgk",
"language:tgl",
"language:tha",
"language:tir",
"language:tpi",
"language:tsn",
"language:tso",
"language:tuk",
"language:tum",
"language:tur",
"language:twi",
"language:tzm",
"language:uig",
"language:ukr",
"language:umb",
"language:urd",
"language:uzn",
"language:vec",
"language:vie",
"language:war",
"language:wol",
"language:xho",
"language:ydd",
"language:yor",
"language:yue",
"language:zho",
"language:zsm",
"language:zul",
"license:cc-by-sa-4.0",
"conditional-text-generation",
"arxiv:2207.04672",
"region:us"
] | 2022-07-17T07:11:54+00:00 | {"annotations_creators": ["found"], "language_creators": ["expert-generated"], "language": ["ace", "acm", "acq", "aeb", "afr", "ajp", "aka", "als", "amh", "apc", "arb", "ars", "ary", "arz", "asm", "ast", "awa", "ayr", "azb", "azj", "bak", "bam", "ban", "bel", "bem", "ben", "bho", "bjn", "bod", "bos", "bug", "bul", "cat", "ceb", "ces", "cjk", "ckb", "crh", "cym", "dan", "deu", "dik", "dyu", "dzo", "ell", "eng", "epo", "est", "eus", "ewe", "fao", "fij", "fin", "fon", "fra", "fur", "fuv", "gaz", "gla", "gle", "glg", "grn", "guj", "hat", "hau", "heb", "hin", "hne", "hrv", "hun", "hye", "ibo", "ilo", "ind", "isl", "ita", "jav", "jpn", "kab", "kac", "kam", "kan", "kas", "kat", "kaz", "kbp", "kea", "khk", "khm", "kik", "kin", "kir", "kmb", "kmr", "knc", "kon", "kor", "lao", "lij", "lim", "lin", "lit", "lmo", "ltg", "ltz", "lua", "lug", "luo", "lus", "lvs", "mag", "mai", "mal", "mar", "min", "mkd", "mlt", "mni", "mos", "mri", "mya", "nld", "nno", "nob", "npi", "nso", "nus", "nya", "oci", "ory", "pag", "pan", "pap", "pbt", "pes", "plt", "pol", "por", "prs", "quy", "ron", "run", "rus", "sag", "san", "sat", "scn", "shn", "sin", "slk", "slv", "smo", "sna", "snd", "som", "sot", "spa", "srd", "srp", "ssw", "sun", "swe", "swh", "szl", "tam", "taq", "tat", "tel", "tgk", "tgl", "tha", "tir", "tpi", "tsn", "tso", "tuk", "tum", "tur", "twi", "tzm", "uig", "ukr", "umb", "urd", "uzn", "vec", "vie", "war", "wol", "xho", "ydd", "yor", "yue", "zho", "zsm", "zul"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual", "translation"], "size_categories": ["unknown"], "source_datasets": ["extended|flores"], "task_categories": ["text2text-generation", "translation"], "task_ids": [], "paperswithcode_id": "flores", "pretty_name": "flores200", "tags": ["conditional-text-generation"]} | 2024-01-07T18:12:19+00:00 |
85b9612b440ac0158d5722d0d45b849a012468ec |
Opensource DataSet form a Kaggle competition https://www.kaggle.com/datasets/andreibuliga1/gdpr-fines-20182020-updated-23012021
GDPR-fines is a dataset with summary of GDPR cases from companies that were find between 2018 and 2021. You will find the summary plus the Articles violated in the cases (3 most importants + "Others" regrouping the rest of articles).
Raw text and lemmatized text available plus multi-labels. | Maxmioti/GDRP-fines | [
"license:other",
"region:us"
] | 2022-07-17T08:57:46+00:00 | {"license": "other"} | 2022-07-17T09:03:34+00:00 |
20aac0fbc008c17c735fe27c2eb410edbdc84381 |
# Dataset Summary
The Great Books dataset is a set of texts based on the [St. John's College Great Books Program](https://www.sjc.edu/academic-programs/undergraduate/classes/seminar/annapolis-undergraduate-readings). It includes 83 works from authors on the Program. | erikanesse/great_books | [
"license:unlicense",
"region:us"
] | 2022-07-17T11:59:45+00:00 | {"license": "unlicense"} | 2022-07-17T12:47:12+00:00 |
630cc1387afa15f1d91d99f27e061b2a7cf39a0a | noob123/imdb10000 | [
"region:us"
] | 2022-07-17T12:30:06+00:00 | {} | 2022-07-17T12:33:20+00:00 |
|
e98b9216f60fc8dbabfe766e014534a08ff01949 |
## XWinograd
Multilingual winograd schema challenge as used in [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786).
### Languages & Samples
- "en": 2325
- "fr": 83
- "jp": 959
- "pt": 263
- "ru": 315
- "zh": 504
### Dataset creation
The Winograd schema challenges in this dataset combine winograd schemas from the XWinograd dataset introduced in Tikhonov et al and as it only contains 16 Chinese schemas, we add 488 Chinese schemas from `clue/cluewsc2020`.
If you only want the original xwinograd chinese schemas only, do:
`load_dataset("Muennighoff/xwinograd", "zh")["test"][0][:16]`
## Additional Information
### Citation Information
```bibtex
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{tikhonov2021heads,
title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning},
author={Alexey Tikhonov and Max Ryabinin},
year={2021},
eprint={2106.12066},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### License
Like the original [English winograd schema challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html), this dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). I.e. you can use it for commercial purposes etc. :)
### Contributions
Thanks to Jordan Clive, @yongzx & @khalidalt for support on adding Chinese.
| Muennighoff/xwinograd | [
"language:en",
"language:fr",
"language:ja",
"language:pt",
"language:ru",
"language:zh",
"license:cc-by-4.0",
"arxiv:2211.01786",
"arxiv:2106.12066",
"region:us"
] | 2022-07-17T14:20:09+00:00 | {"language": ["en", "fr", "ja", "pt", "ru", "zh"], "license": "cc-by-4.0"} | 2023-07-07T07:27:03+00:00 |
787af29673533c61886956a44fb0093850abed52 |
# Dataset Card for OpenFire
## 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://pyronear.org/pyro-vision/datasets.html#openfire
- **Repository:** https://github.com/pyronear/pyro-vision
- **Point of Contact:** Pyronear <https://pyronear.org/en/>
### Dataset Summary
OpenFire is an image classification dataset for wildfire detection, collected
from web searches.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image URL and its binary label.
```
{
'image_url': 'https://cdn-s-www.ledauphine.com/images/13C08274-6BA6-4577-B3A0-1E6C1B2A573C/FB1200/photo-1338240831.jpg',
'is_wildfire': true,
}
```
### Data Fields
- `image_url`: the download URL of the image.
- `is_wildfire`: a boolean value specifying whether there is an ongoing wildfire on the image.
### Data Splits
The data is split into training and validation sets. The training set contains 7143 images and the validation set 792 images.
## Dataset Creation
### Curation Rationale
The curators state that the current wildfire classification datasets typically contain close-up shots of wildfires, with limited variations of weather conditions, luminosity and backrgounds,
making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping
with sun flares, foggy / cloudy weather conditions and small scale.
### Source Data
#### Initial Data Collection and Normalization
OpenFire was collected using images publicly indexed by the search engine DuckDuckGo using multiple relevant queries. The images were then manually cleaned to remove errors.
### Annotations
#### Annotation process
Each web search query was designed to yield a single label (with wildfire or without), and additional human verification was used to remove errors.
#### Who are the annotators?
François-Guillaume Fernandez
### 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
François-Guillaume Fernandez
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Pyronear_PyroVision_2019,
title={Pyrovision: wildfire early detection},
author={Pyronear contributors},
year={2019},
month={October},
publisher = {GitHub},
howpublished = {\url{https://github.com/pyronear/pyro-vision}}
}
```
| pyronear/openfire | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:apache-2.0",
"region:us"
] | 2022-07-17T15:11:22+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": [], "license": ["apache-2.0"], "multilinguality": [], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": [], "pretty_name": "Wildfire image classification dataset collected using images from web searches."} | 2022-12-11T22:25:43+00:00 |
3f8fe90b59fe1958fe39583b5d74e398d882f1ed | # Dataset Card for clmet_3_1
**NOTES**:
- Some of the annotations in the `class` and `pos` configs are not properly formed. These are indicated with warning messages when the dataset is loaded.
- In addition to the classes mentioned in the README for the dataset, there is an additional class in the `class` dataset called `QUOT`. As far as I can tell, this is used for tagging all quotation marks
- When the `class` and `pos` configs are loaded, the available class/pos tags are shown at the top
## Dataset Statistics:
The following table summarises the corpus make-up:
|PERIOD | #authors | #texts |CQP3.1 | non-PUNC |
|-----------|----------|---------------------|--------|---------|
|1710-1780 | 51 | 88 | 12,182,064 | 10,415,721|
|1780-1850 | 70 | 99 | 13,300,457 | 11,269,977|
|1850-1920 | 91 | 146 | 14,858,239 | 12,657,159|
|TOTAL | 212 | 333 | 40,340,760 | 34,342,857|
|GENRE (all tokens):| | | |
|---|---|---|---|
| | **1710-1780**| **1780-1850** | **1850-1920** |
|Narrative fiction | 5,405,645 | 5,780,352 | 7,561,339 |
|Narrative non-fiction | 2,145,946 | 2,261,485 | 1,097,487 |
|Drama | 523,318 | 441,040 | 763,352 |
|Letters | 1,208,219 | 842,795 | 554,046 |
|Treatise | 1,263,090 | 1,927,272 | 2,030,210 |
|Other | 1,635,846 | 2,047,513 | 2,851,805 |
## 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:** http://fedora.clarin-d.uni-saarland.de/clmet/clmet.html
- **Repository:** [Needs More Information]
- **Paper:** https://icame.info/icame_static/ij29/ij29-page69-82.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Henrik De Smet](https://www.arts.kuleuven.be/ling/func/members/hendrik-desmet/func)
### Dataset Summary
The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�, as an offshoot of a bigger project developing a database of text descriptors (Diller, De Smet & Tyrkk� 2011). CLMET3.1 is a principled collection of public domain texts drawn from various online archiving projects. In total, the corpus contains some 34 million words of running text. It incorporates CLMET, CLMETEV, and CLMET3.0, and has been compiled following roughly the same principles, that is:
- The corpus covers the period 17101920, divided into three 70-year sub-periods.
- The texts making up the corpus have all been written by British and Irish authors who are native speakers of English.
- The corpus never contains more than three texts by the same author.
- The texts within each sub-period have been written by authors born within a correspondingly restricted sub-period.
### Supported Tasks and Leaderboards
- `named-entity-recognition`: Since this dataset is tagged, it can be used for performing NER
- `text-classification`: Each text comes with the date of the text and can be used to perform stylistic classification of texts
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`
## Dataset Structure
### Data Instances
A `plain` sample looks as follows:
```
{'text': "\nFAME AND THE POET\n \nDRAMATIS PERSONAE�\n \nHarry de Reves , a Poet .\n \n( This name , though of course of French origin , has become anglicised and is pronounced de Reevs . )\n \nDick Prattle , a Lieutenant-Major of the Royal Horse Marines .\n \nFame .\n \nScene\n \nThe Poet 's rooms in London .\nWindows in back .\nA high screen in a corner .\n \nTime : February 30th .\n \nThe Poet is sitting at a table writing .\n \n[ Enter Dick Prattle .\n \nPrattle : Hullo , Harry .\n \nde
Reves : Hullo , Dick .\nGood Lord , where are you from ?\n \nPrattle ( casually ) : The ends of the earth .\n \nde Reves : Well , I 'm damned !\n \nPrattle : Thought I 'd drop in and see how you were getting on .\n \nde Reves : Well , that 's splendid .\nWhat are you doing in London ?\n \nPrattle : Well , I wanted to see if I could get one or two decent ties to wear - you can get nothing out there - then I thought I 'd have a look and see how London was getting on .\n \nde Reves : Splendid !\nHow 's everybody ?\n \nPrattle : All going strong .\n \nde Reves : That 's good .\n \nPrattle ( seeing paper and ink ) : But what are you doing ?\n \nde Reves : Writing .\n \nPrattle : Writing ?\nI did n't know you wrote .\n \nde Reves : Yes , I
've taken to it rather .\n \nPrattle : I say - writing 's no good .\nWhat do you write ?\n \nde Reves : Oh , poetry .\n \nPrattle : Poetry !\nGood Lord !\n \nde Reves : Yes , that sort of thing , you know .\n \nPrattle : Good Lord !\nDo you make any money by it ?\n \nde Reves : No .\nHardly any .\n \nPrattle : I say - why do n't you chuck it ?\n \nde Reves : Oh , I do n't know .\nSome people seem to like my stuff , rather .\nThat 's why I go on .\n \nPrattle : I 'd chuck it if there 's no money in it .\n \nde Reves : Ah , but then it 's hardly in your line , is it ?\nYou 'd hardly approve of poetry if there was money in it .\n \nPrattle : Oh , I do n't say that .\nIf I could make as much by poetry as I can by betting I do n't say I would n't try the poetry touch , only - -\n \nde Reves : Only what ?\n \nPrattle : Oh , I do n't know .\nOnly there seems more sense in betting , somehow .\n \nde Reves : Well , yes .\nI suppose it 's easier to tell what an earthly horse is going to
do , than to tell what Pegasus - -\n \nPrattle : What 's Pegasus ?\n \nde Reves : Oh , the winged horse of poets .\n \nPrattle : I say !\nYou do n't believe in a winged horse , do you ?\n \nde Reves : In our trade we believe in all fabulous things
.\nThey all represent some large truth to us .\nAn emblem like Pegasus is as real a thing to a poet as a Derby winner would be to you .\n \nPrattle : I say .\n( Give me a cigarette .\nThanks . )\nWhat ?\nThen you 'd believe in nymphs and fauns , and Pan , and all those kind of birds ?\n \nde Reves : Yes .\nYes .\nIn all of them .\n \nPrattle : Good Lord !\n \nde Reves : You believe in the Lord Mayor of London , do n't you ?\n \nPrattle : Yes , of course ; but what has - -\n \nde Reves : Four million people or so made him Lord Mayor , did n't they ?\nAnd he represents to them the wealth and dignity and tradition of - -\n \nPrattle : Yes ; but , I say , what has all this - -\n \nde Reves : Well , he stands for an idea to them , and they made him Lord Mayor , and so he is one ...\n \nPrattle : Well , of course he is .\n \nde Reves : In the same way Pan has been made what he is by millions ; by millions to whom he represents world-old traditions .\n \nPrattle ( rising from his chair and stepping backwards , laughing and looking at the Poet in a kind of assumed wonder ) : I say ... I say ... You old heathen ... but Good Lord ...\n \n[ He bumps into the high screen behind , pushing it back a little .\n \nde Reves : Look out !\nLook out !\n \nPrattle : What ?\nWhat 's the matter ?\n \nde Reves : The screen !\n \nPrattle : Oh , sorry , yes .\nI 'll put it right .\n \n[ He is about to go round behind it .\n \nde Reves : No , do n't go round there .\n \nPrattle : What ?\nWhy not ?\n \nde Reves : Oh , you would n't understand .\n \nPrattle : Would n't understand ?\nWhy , what have you got ?\n \nde Reves : Oh , one of those things ... You would n't understand .\n \nPrattle : Of course I 'd understand .\nLet 's have a look .\n \n[ The Poet walks towards Prattle and the screen .\nHe protests no further .\nPrattle looks round the corner of the screen .\n \nAn altar .\n \nde Reves ( removing the screen altogether ) : That is all .\nWhat do you make of it ?\n \n[ An
altar of Greek design , shaped like a pedestal , is revealed .\nPapers litter the floor all about it .\n \nPrattle : I say - you always were an untidy devil .\n \nde Reves : Well , what do you make of it ?\n \nPrattle : It reminds me of your room at Eton .\n \nde Reves : My room at Eton ?\n \nPrattle : Yes , you always had papers all over your floor .\n \nde Reves : Oh , yes - -\n \nPrattle : And what are these ?\n \nde Reves : All these are poems ; and this is my altar to Fame .\n \nPrattle : To Fame ?\n \nde Reves : The same that Homer knew .\n \nPrattle : Good Lord !\n \nde Reves : Keats never saw her .\nShelley died too young .\nShe came late at the best of times , now scarcely ever .\n \nPrattle : But , my dear fellow , you do n't mean that you think there really is such a person ?\n \nde Reves : I offer all my songs to her .\n \nPrattle : But you do n't mean you think you could actually see Fame ?\n \nde Reves : We poets personify abstract things , and not poets only but
sculptors7 and painters too .\nAll the great things of the world are those abstract things .\n \nPrattle : But what I mean is , they 're not really there , like you or me .\n \nde Reves : To us these things are more real than men , they outlive generations , they watch the passing of kingdoms : we go by them like dust ; they are still there , unmoved , unsmiling .\n \nPrattle : But , but , you ca n't think that you could see Fame , you do n't expect to see it ?\n \nde Reves : Not to me .\nNever to me .\nShe of the golden trumpet and Greek dress will never appear to me ... We all have our dreams .\n \nPrattle : I say - what have you been doing all day ?\n \nde Reves : I ?\nOh , only writing a sonnet .\n \nPrattle : Is it a long one ?\n \nde Reves : Not very .\n \nPrattle : About how long is it ?\n \nde Reves : About fourteen lines .\n \nPrattle ( impressively ) : I tell you what it is .\n \nde Reves : Yes ?\n \nPrattle : I tell you what .\nYou 've been overworking yourself .\nI
once got like that on board the Sandhurst , working for the passing-out exam .\nI got so bad that I could have seen anything .\n \nde Reves : Seen anything ?\n \nPrattle : Lord , yes ; horned pigs , snakes with wings ; anything ; one of your winged horses even .\nThey gave me some stuff called bromide for it .\nYou take a rest .\n \nde Reves : But my dear fellow , you do n't understand at all .\nI merely said that abstract things are to a poet as near and real and visible as one of your bookmakers or barmaids .\n \nPrattle : I know .\nYou take a rest .\n \nde Reves : Well , perhaps I will .\nI 'd come with you to that musical comedy you 're going to see , only I 'm a bit tired after writing this ; it 's a tedious job .\nI 'll come another night .\n \nPrattle : How do you know I 'm going to see a musical comedy ?\n \nde Reves : Well , where would you go ?\nHamlet 's 8 on at the Lord Chamberlain 's .\nYou 're not going there .\n \nPrattle : Do I look like it ?\n \nde Reves : No .\n \nPrattle : Well , you 're quite right .\nI 'm going to see `` The Girl from Bedlam . ''\nSo long .\nI must push off now .\nIt 's getting late .\nYou take a rest .\nDo n't add another line to that sonnet ; fourteen 's quite enough .\nYou take a
rest .\nDo n't have any dinner to-night , just rest .\nI was like that once myself .\nSo long .\n \nde Reves : So long .\n \n[ Exit Prattle .\nde Reves returns to his table and sits down .\n \nGood old Dick !\nHe 's the same as ever .\nLord , how time passes .\n \nHe takes his pen and his sonnet and makes a few alterations .\n \nWell , that 's finished .\nI ca n't do any more to it .\n \n[ He rises and goes to the screen ; he draws back part of it and goes up to the altar .\nHe is about to place his sonnet reverently at the foot of the altar amongst his other verses .\n \nNo , I will not put it there .\nThis one is worthy of the altar .\n \n[ He places the sonnet upon the altar itself .\n \nIf that sonnet does not give me fame , nothing that I have done before will give it to me , nothing that I ever will do .\n \n[ He replaces the screen and returns to his chair at the table .\nTwilight is coming on .\nHe sits with his elbow on the table , his head on his hand , or however the actor pleases .\n \nWell , well .\nFancy seeing Dick again .\nWell , Dick enjoys his life , so he 's no fool .\nWhat was that he said ?\n`` There 's no money in poetry .\nYou 'd better chuck it . ''\nTen years ' work and what have I to show for it ?\nThe admiration of men who care for poetry , and how many of them are there ?\nThere 's a bigger demand for smoked glasses to look at eclipses of the sun .\nWhy should Fame come to me ?\nHave n't I given up my days for her ?\nThat is enough to keep her away .\nI am a poet ; that is enough reason for her to slight me .\nProud and aloof and cold as marble , what does Fame care for us ?\nYes , Dick is right .\nIt 's a poor game chasing illusions , hunting the intangible , pursuing dreams .\nDreams ?\nWhy , we are ourselves dreams .\n \n[ He leans back in his chair .\n \nWe are such stuff As dreams are made on , and our little life Is rounded with a sleep .\n[ He is silent for a while .\nSuddenly he lifts his head .\n \nMy room at Eton , Dick said .\nAn untidy mess .\n \n[ As he lifts his head and says these words , twilight gives place to broad daylight , merely as a hint that the author of the play may have been mistaken , and the whole thing may have been no more than a poet
's dream .\n \nSo it was , and it 's an untidy mess there ( looking at screen ) too .\nDick 's right .\nI 'll tidy it up .\nI 'll burn the whole damned heap ,\n \n[ He advances impetuously towards the screen .\n \nevery damned poem that I was ever
fool enough to waste my time on .\n \n[ He pushes back the screen .\nFame in a Greek dress with a long golden trumpet in her hand is seen standing motionless on the altar like a marble goddess .\n \nSo ... you have come !\n \n[ For a while he stands thunderstruck .\nThen he approaches the altar .\n \nDivine fair lady , you have come .\n \n[ He holds up his hand to her and leads her down from the altar and into the centre of the stage .\nAt whatever moment the actor finds it most convenient , he repossesses himself of the sonnet that he had placed on the altar .\nHe now offers it to Fame .\n \nThis is my sonnet .\nIs it well done ?\n \n[ Fame takes it and reads it in silence , while the Poet watches her rapturously .\n \nFame : You 're a bit of all right .\n \nde Reves : What ?\n \nFame : Some poet .\n \nde Reves : I - I - scarcely ... understand .\n \nFame : You 're IT .\n \nde Reves : But ... it is not possible ... are you she that knew Homer ?\n \nFame : Homer ?\nLord , yes .\nBlind old bat , ' e could n't see a yard .\n \nde Reves : O Heavens !\n \n[ Fame walks beautifully to the window .\nShe opens it and puts her head out .\n \nFame ( in a voice with which a woman in an upper storey would cry for help if the house was well alight ) : Hi !\nHi !\nBoys !\nHi !\nSay , folks !\nHi !\n \n[ The murmur of a gathering crowd is heard .\nFame blows her trumpet .\n \nFame : Hi , he 's a poet !\n( Quickly , over her shoulder . )\nWhat 's your name ?\n \nde Reves : De Reves .\n \nFame : His name 's de Reves .\n \nde Reves : Harry de Reves .\n \nFame : His pals call him Harry .\n \nThe Crowd : Hooray !\nHooray !\nHooray !\n \nFame : Say , what 's your favourite colour ?\n \nde Reves : I ... I ... I do n't quite understand .\n \nFame : Well , which do you like best , green or blue ?\n \nde Reves : Oh - er - blue .\n \n[ She blows her trumpet out of the window .\n \nNo - er - I think green .\n \nFame : Green is his favourite colour .\n \nThe Crowd : Hooray !\nHooray !\nHooray !\n \nFame : ` Ere , tell us something .\nThey want to know all about yer .\n \nde Reves : Would n't 9 you perhaps ... would they care to hear my sonnet , if you would - er ...\n \nFame ( picking up quill ) : Here , what 's this ?\n \nde Reves : Oh , that 's my pen .\n \nFame ( after another blast on her trumpet ) : He writes with a quill .\n \n[ Cheers from the Crowd .\n \nFame ( going to a cupboard ) : Here , what have you got in here ?\n \nde Reves : Oh ... er ... those are my breakfast things .\n \nFame ( finding a dirty plate ) : What have yer had on this one ?\n \nde Reves ( mournfully ) : Oh , eggs and bacon .\n \nFame ( at the window ) : He has eggs and bacon for breakfast .\n \nThe Crowd : Hip hip hip , hooray !\nHip hip hip , hooray !\nHip hip hip , hooray !\nFame : Hi , and what 's this ?\n \nde Reves ( miserably ) : Oh , a golf stick .\n \nFame : He 's a man 's man !\nHe 's a virile man !\nHe 's a manly man !\n \n[ Wild cheers from the Crowd , this time only from women 's voices .\n \nde Reves : Oh , this is terrible .\nThis is terrible .\nThis is terrible .\n \n[ Fame gives another peal on her horn .\nShe is about to speak .\n \nde Reves ( solemnly and mournfully ) : One moment , one moment ...\n \nFame : Well , out with it .\n \nde Reves : For ten years , divine lady , I have worshipped you , offering all my songs ... I find ... I find I am not worthy ...\n \nFame : Oh , you 're all right .\n \nde Reves : No , no , I am not worthy .\nIt can not be .\nIt can not possibly be .\nOthers deserve you more .\nI must say it !\nI can not possibly love you .\nOthers are worthy .\nYou will find others .\nBut I , no , no , no .\nIt can not be .\nIt can not be .\nOh , pardon me , but it must not .\n \n[ Meanwhile Fame has been lighting one of his cigarettes .\nShe sits in a comfortable chair , leans right back , and puts her feet right up on the table amongst the poet 's papers .\n \nOh , I fear I offend you .\nBut - it can not be .\n \nFame : Oh , that 's all right , old bird ; no offence .\nI ai n't going to leave you .\n \nde Reves : But - but - but - I do not understand .\n \nFame : I 've come to stay , I have .\n \n[ She blows a puff of smoke through her trumpet .\n \nCURTAIN .\n", 'genre': 'Drama', 'subgenre': 'drama', 'year': '1919', 'quarter_cent': '1900-1924', 'decade': '1910s', 'title': 'Fame and the poet', 'author': 'Dunsany [Edward John Moreton Drax Plunkett]', 'notes': '', 'comments': 'selected from larger
file', 'period': '1850-1920', 'id': '317'}
```
A `pos` sample looks as follows:
```
{'text': ['FAME', 'AND', 'THE', 'POET', 'DRAMATIS', 'PERSONAE�', 'Harry', 'de', 'Reves', ',', 'a', 'Poet', '.', '(', 'This', 'name', ',', 'though', 'of', 'course', 'of', 'French', 'origin', ',', 'has', 'become', 'anglicised', 'and', 'is', 'pronounced', 'de', 'Reevs', '.', ')', 'Dick', 'Prattle', ',', 'a', 'Lieutenant-Major', 'of', 'the', 'Royal', 'Horse', 'Marines', '.', 'Fame', '.', 'Scene', 'The', 'Poet', "'s", 'rooms', 'in', 'London', '.', 'Windows', 'in', 'back', '.', 'A', 'high', 'screen', 'in', 'a', 'corner', '.', 'Time', ':', 'February', '30th', '.', 'The', 'Poet', 'is', 'sitting', 'at', 'a', 'table', 'writing', '.', '[', 'Enter', 'Dick', 'Prattle', '.', 'Prattle', ':', 'Hullo', ',', 'Harry', '.', 'de', 'Reves', ':', 'Hullo', ',', 'Dick', '.', 'Good', 'Lord', ',', 'where', 'are', 'you', 'from', '?', 'Prattle', '(', 'casually', ')', ':', 'The', 'ends', 'of', 'the', 'earth', '.', 'de', 'Reves', ':', 'Well', ',', 'I', "'m", 'damned', '!', 'Prattle', ':', 'Thought', 'I', "'d", 'drop', 'in', 'and', 'see', 'how', 'you', 'were', 'getting', 'on', '.', 'de', 'Reves', ':', 'Well', ',', 'that', "'s", 'splendid', '.', 'What', 'are', 'you', 'doing', 'in', 'London', '?', 'Prattle', ':', 'Well', ',', 'I', 'wanted', 'to', 'see',
'if', 'I', 'could', 'get', 'one', 'or', 'two', 'decent', 'ties', 'to', 'wear', '-', 'you', 'can', 'get', 'nothing', 'out', 'there', '-', 'then', 'I', 'thought', 'I', "'d", 'have', 'a', 'look', 'and', 'see', 'how', 'London', 'was', 'getting', 'on',
'.', 'de', 'Reves', ':', 'Splendid', '!', 'How', "'s", 'everybody', '?', 'Prattle', ':', 'All', 'going', 'strong', '.', 'de', 'Reves', ':', 'That', "'s", 'good', '.', 'Prattle', '(', 'seeing', 'paper', 'and', 'ink', ')', ':', 'But', 'what', 'are',
'you', 'doing', '?', 'de', 'Reves', ':', 'Writing', '.', 'Prattle', ':', 'Writing', '?', 'I', 'did', "n't", 'know', 'you', 'wrote', '.', 'de', 'Reves', ':', 'Yes', ',', 'I', "'ve", 'taken', 'to', 'it', 'rather', '.', 'Prattle', ':', 'I', 'say', '-', 'writing', "'s", 'no', 'good', '.', 'What', 'do', 'you', 'write', '?', 'de', 'Reves', ':', 'Oh', ',', 'poetry', '.', 'Prattle', ':', 'Poetry', '!', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'Yes', ',', 'that', 'sort', 'of', 'thing', ',', 'you', 'know', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'Do', 'you', 'make', 'any', 'money', 'by', 'it', '?', 'de', 'Reves', ':', 'No', '.', 'Hardly', 'any', '.', 'Prattle', ':', 'I', 'say', '-', 'why', 'do', "n't", 'you', 'chuck', 'it', '?', 'de', 'Reves', ':', 'Oh', ',', 'I', 'do', "n't", 'know', '.', 'Some', 'people', 'seem', 'to', 'like', 'my', 'stuff', ',', 'rather', '.', 'That', "'s", 'why', 'I', 'go', 'on', '.', 'Prattle', ':', 'I', "'d", 'chuck', 'it', 'if', 'there', "'s", 'no', 'money', 'in', 'it', '.', 'de', 'Reves', ':', 'Ah', ',', 'but', 'then', 'it', "'s", 'hardly', 'in', 'your', 'line', ',', 'is', 'it', '?', 'You', "'d", 'hardly', 'approve', 'of', 'poetry', 'if', 'there', 'was', 'money', 'in', 'it', '.', 'Prattle', ':', 'Oh', ',', 'I', 'do', "n't", 'say', 'that', '.', 'If', 'I', 'could', 'make', 'as', 'much', 'by', 'poetry', 'as', 'I', 'can', 'by', 'betting', 'I', 'do', "n't", 'say', 'I', 'would', "n't", 'try', 'the', 'poetry', 'touch', ',', 'only', '-', '-', 'de', 'Reves', ':', 'Only', 'what', '?', 'Prattle', ':', 'Oh', ',', 'I', 'do', "n't", 'know', '.', 'Only', 'there', 'seems', 'more', 'sense', 'in', 'betting', ',', 'somehow', '.', 'de', 'Reves', ':', 'Well', ',', 'yes', '.', 'I', 'suppose', 'it', "'s", 'easier', 'to', 'tell', 'what', 'an', 'earthly', 'horse', 'is', 'going', 'to', 'do', ',', 'than', 'to', 'tell', 'what', 'Pegasus', '-', '-', 'Prattle', ':', 'What', "'s", 'Pegasus', '?', 'de', 'Reves', ':', 'Oh', ',', 'the', 'winged', 'horse', 'of', 'poets', '.', 'Prattle', ':', 'I', 'say', '!', 'You', 'do', "n't", 'believe', 'in', 'a', 'winged', 'horse', ',', 'do', 'you', '?', 'de', 'Reves', ':', 'In', 'our', 'trade', 'we', 'believe', 'in', 'all', 'fabulous', 'things', '.', 'They', 'all', 'represent', 'some', 'large', 'truth', 'to', 'us', '.', 'An', 'emblem', 'like', 'Pegasus', 'is', 'as', 'real', 'a', 'thing', 'to', 'a', 'poet', 'as', 'a', 'Derby', 'winner', 'would', 'be', 'to', 'you', '.', 'Prattle', ':', 'I', 'say', '.', '(', 'Give', 'me', 'a', 'cigarette', '.', 'Thanks', '.', ')', 'What', '?', 'Then', 'you', "'d", 'believe', 'in', 'nymphs', 'and', 'fauns', ',', 'and', 'Pan', ',', 'and', 'all', 'those', 'kind', 'of', 'birds', '?', 'de', 'Reves', ':', 'Yes', '.', 'Yes', '.', 'In',
'all', 'of', 'them', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'You', 'believe', 'in', 'the', 'Lord', 'Mayor', 'of', 'London', ',', 'do', "n't", 'you', '?', 'Prattle', ':', 'Yes', ',', 'of', 'course', ';', 'but', 'what', 'has',
'-', '-', 'de', 'Reves', ':', 'Four', 'million', 'people', 'or', 'so', 'made', 'him', 'Lord', 'Mayor', ',', 'did', "n't", 'they', '?', 'And', 'he', 'represents', 'to', 'them', 'the', 'wealth', 'and', 'dignity', 'and', 'tradition', 'of', '-', '-', 'Prattle', ':', 'Yes', ';', 'but', ',', 'I', 'say', ',', 'what', 'has', 'all', 'this', '-', '-', 'de', 'Reves', ':', 'Well', ',', 'he', 'stands', 'for', 'an', 'idea', 'to', 'them', ',', 'and', 'they', 'made', 'him', 'Lord', 'Mayor', ',', 'and', 'so', 'he', 'is', 'one', '...', 'Prattle', ':', 'Well', ',', 'of', 'course', 'he', 'is', '.', 'de', 'Reves', ':', 'In', 'the', 'same', 'way', 'Pan', 'has', 'been', 'made', 'what', 'he', 'is', 'by', 'millions', ';', 'by', 'millions', 'to', 'whom', 'he', 'represents', 'world-old', 'traditions', '.', 'Prattle', '(', 'rising', 'from', 'his', 'chair', 'and', 'stepping', 'backwards', ',', 'laughing', 'and', 'looking', 'at', 'the', 'Poet', 'in', 'a', 'kind', 'of', 'assumed', 'wonder', ')', ':', 'I', 'say', '...', 'I', 'say', '...', 'You', 'old', 'heathen', '...', 'but', 'Good', 'Lord', '...', '[', 'He', 'bumps', 'into', 'the', 'high', 'screen', 'behind', ',', 'pushing', 'it', 'back', 'a', 'little', '.', 'de', 'Reves', ':', 'Look', 'out', '!', 'Look', 'out', '!', 'Prattle', ':', 'What', '?', 'What', "'s", 'the', 'matter', '?', 'de', 'Reves', ':', 'The', 'screen', '!', 'Prattle', ':', 'Oh', ',', 'sorry', ',', 'yes', '.', 'I', "'ll", 'put', 'it', 'right', '.', '[', 'He', 'is', 'about', 'to', 'go', 'round', 'behind', 'it', '.', 'de', 'Reves', ':', 'No', ',', 'do', "n't", 'go', 'round', 'there', '.', 'Prattle', ':', 'What', '?', 'Why', 'not', '?', 'de', 'Reves', ':', 'Oh', ',', 'you', 'would', "n't", 'understand', '.', 'Prattle', ':', 'Would', "n't", 'understand', '?', 'Why', ',', 'what', 'have', 'you', 'got', '?', 'de', 'Reves', ':', 'Oh', ',', 'one', 'of', 'those', 'things', '...', 'You', 'would', "n't", 'understand', '.', 'Prattle', ':', 'Of', 'course', 'I', "'d", 'understand', '.', 'Let', "'s", 'have', 'a', 'look', '.', '[', 'The', 'Poet', 'walks', 'towards', 'Prattle', 'and', 'the', 'screen', '.', 'He', 'protests', 'no', 'further', '.', 'Prattle', 'looks', 'round', 'the', 'corner', 'of', 'the', 'screen', '.', 'An', 'altar', '.', 'de', 'Reves', '(', 'removing', 'the', 'screen', 'altogether', ')', ':', 'That', 'is', 'all', '.', 'What', 'do', 'you', 'make', 'of', 'it', '?', '[', 'An', 'altar', 'of', 'Greek', 'design', ',', 'shaped', 'like', 'a', 'pedestal', ',', 'is', 'revealed', '.', 'Papers', 'litter', 'the', 'floor', 'all', 'about', 'it', '.', 'Prattle', ':', 'I', 'say', '-', 'you', 'always', 'were', 'an', 'untidy', 'devil', '.', 'de', 'Reves', ':', 'Well', ',', 'what', 'do', 'you', 'make', 'of', 'it', '?', 'Prattle', ':', 'It', 'reminds', 'me', 'of', 'your', 'room', 'at', 'Eton', '.', 'de', 'Reves', ':', 'My', 'room', 'at', 'Eton', '?', 'Prattle', ':', 'Yes', ',', 'you', 'always', 'had', 'papers', 'all', 'over', 'your', 'floor', '.', 'de', 'Reves', ':', 'Oh', ',', 'yes', '-', '-', 'Prattle', ':', 'And', 'what', 'are', 'these', '?', 'de', 'Reves', ':', 'All', 'these', 'are', 'poems', ';', 'and', 'this', 'is', 'my', 'altar', 'to', 'Fame', '.', 'Prattle', ':', 'To', 'Fame', '?', 'de', 'Reves', ':', 'The', 'same', 'that', 'Homer', 'knew', '.', 'Prattle', ':', 'Good', 'Lord', '!', 'de', 'Reves', ':', 'Keats', 'never', 'saw', 'her', '.', 'Shelley', 'died', 'too', 'young', '.', 'She', 'came', 'late', 'at', 'the', 'best', 'of', 'times', ',', 'now', 'scarcely', 'ever', '.', 'Prattle', ':', 'But', ',', 'my', 'dear', 'fellow', ',', 'you', 'do', "n't", 'mean', 'that', 'you', 'think', 'there', 'really', 'is', 'such', 'a', 'person', '?', 'de', 'Reves', ':', 'I', 'offer', 'all', 'my', 'songs', 'to', 'her', '.', 'Prattle', ':', 'But', 'you', 'do', "n't", 'mean', 'you', 'think', 'you', 'could', 'actually', 'see', 'Fame', '?', 'de', 'Reves', ':', 'We', 'poets', 'personify', 'abstract', 'things', ',', 'and', 'not', 'poets', 'only', 'but', 'sculptors7', 'and', 'painters', 'too', '.', 'All', 'the', 'great', 'things', 'of', 'the', 'world', 'are', 'those', 'abstract', 'things', '.', 'Prattle', ':', 'But', 'what', 'I', 'mean', 'is', ',', 'they', "'re", 'not', 'really', 'there', ',', 'like', 'you', 'or', 'me', '.', 'de', 'Reves', ':', 'To', 'us', 'these', 'things', 'are', 'more', 'real', 'than', 'men', ',', 'they', 'outlive', 'generations', ',', 'they', 'watch', 'the', 'passing', 'of', 'kingdoms', ':', 'we', 'go', 'by', 'them', 'like', 'dust', ';', 'they', 'are', 'still', 'there', ',', 'unmoved', ',', 'unsmiling', '.', 'Prattle', ':', 'But', ',', 'but', ',', 'you', 'ca', "n't", 'think', 'that', 'you', 'could', 'see', 'Fame', ',', 'you', 'do', "n't", 'expect', 'to', 'see',
'it', '?', 'de', 'Reves', ':', 'Not', 'to', 'me', '.', 'Never', 'to', 'me', '.', 'She', 'of', 'the', 'golden', 'trumpet', 'and', 'Greek', 'dress', 'will', 'never', 'appear', 'to', 'me', '...', 'We', 'all', 'have', 'our', 'dreams', '.', 'Prattle', ':', 'I', 'say', '-', 'what', 'have', 'you', 'been', 'doing', 'all', 'day', '?', 'de', 'Reves', ':', 'I', '?', 'Oh', ',', 'only', 'writing', 'a', 'sonnet', '.', 'Prattle', ':', 'Is', 'it', 'a', 'long', 'one', '?', 'de', 'Reves', ':', 'Not', 'very',
'.', 'Prattle', ':', 'About', 'how', 'long', 'is', 'it', '?', 'de', 'Reves', ':', 'About', 'fourteen', 'lines', '.', 'Prattle', '(', 'impressively', ')', ':', 'I', 'tell', 'you', 'what', 'it', 'is', '.', 'de', 'Reves', ':', 'Yes', '?', 'Prattle', ':', 'I', 'tell', 'you', 'what', '.', 'You', "'ve", 'been', 'overworking', 'yourself', '.', 'I', 'once', 'got', 'like', 'that', 'on', 'board', 'the', 'Sandhurst', ',', 'working', 'for', 'the', 'passing-out', 'exam', '.', 'I', 'got', 'so', 'bad', 'that', 'I', 'could', 'have', 'seen', 'anything', '.', 'de', 'Reves', ':', 'Seen', 'anything', '?', 'Prattle', ':', 'Lord', ',', 'yes', ';', 'horned', 'pigs', ',', 'snakes', 'with', 'wings', ';', 'anything', ';', 'one', 'of', 'your', 'winged', 'horses', 'even', '.', 'They', 'gave', 'me', 'some', 'stuff', 'called', 'bromide', 'for', 'it', '.', 'You', 'take', 'a', 'rest', '.', 'de', 'Reves', ':', 'But', 'my', 'dear', 'fellow', ',', 'you', 'do', "n't", 'understand', 'at', 'all', '.', 'I', 'merely', 'said', 'that', 'abstract', 'things', 'are', 'to', 'a', 'poet', 'as', 'near', 'and', 'real', 'and', 'visible', 'as', 'one', 'of', 'your', 'bookmakers', 'or', 'barmaids', '.', 'Prattle', ':', 'I', 'know', '.', 'You', 'take', 'a', 'rest', '.', 'de', 'Reves', ':', 'Well', ',', 'perhaps', 'I', 'will', '.', 'I', "'d", 'come', 'with', 'you', 'to', 'that', 'musical', 'comedy', 'you', "'re", 'going', 'to', 'see', ',', 'only', 'I', "'m", 'a', 'bit', 'tired', 'after', 'writing', 'this', ';', 'it', "'s", 'a', 'tedious', 'job', '.', 'I', "'ll", 'come', 'another', 'night', '.', 'Prattle', ':', 'How', 'do', 'you', 'know', 'I', "'m", 'going', 'to', 'see', 'a', 'musical', 'comedy', '?', 'de', 'Reves', ':', 'Well', ',', 'where', 'would', 'you', 'go', '?', 'Hamlet', "'s", '8', 'on', 'at', 'the', 'Lord', 'Chamberlain', "'s", '.', 'You', "'re", 'not', 'going', 'there', '.', 'Prattle', ':', 'Do', 'I', 'look', 'like', 'it', '?', 'de', 'Reves', ':', 'No', '.', 'Prattle', ':', 'Well', ',', 'you', "'re", 'quite', 'right', '.', 'I', "'m", 'going', 'to', 'see', '``', 'The', 'Girl', 'from', 'Bedlam', '.', "''", 'So', 'long', '.', 'I', 'must', 'push', 'off', 'now', '.', 'It', "'s", 'getting', 'late', '.', 'You', 'take', 'a', 'rest', '.', 'Do', "n't", 'add', 'another', 'line', 'to', 'that', 'sonnet', ';', 'fourteen', "'s", 'quite', 'enough', '.', 'You', 'take', 'a', 'rest', '.', 'Do', "n't", 'have', 'any', 'dinner', 'to-night', ',', 'just', 'rest', '.', 'I', 'was', 'like', 'that', 'once', 'myself', '.', 'So', 'long', '.', 'de', 'Reves', ':', 'So', 'long', '.', '[', 'Exit', 'Prattle', '.', 'de', 'Reves', 'returns', 'to', 'his', 'table', 'and', 'sits', 'down', '.', 'Good', 'old', 'Dick', '!', 'He', "'s", 'the', 'same', 'as', 'ever', '.', 'Lord', ',', 'how', 'time', 'passes', '.', 'He', 'takes', 'his', 'pen', 'and', 'his', 'sonnet', 'and', 'makes', 'a', 'few', 'alterations', '.', 'Well', ',', 'that', "'s", 'finished', '.', 'I', 'ca', "n't", 'do', 'any', 'more', 'to', 'it', '.', '[', 'He', 'rises', 'and', 'goes', 'to', 'the', 'screen', ';', 'he', 'draws', 'back', 'part', 'of', 'it', 'and', 'goes', 'up', 'to', 'the', 'altar', '.', 'He', 'is', 'about', 'to', 'place', 'his', 'sonnet', 'reverently', 'at', 'the', 'foot', 'of', 'the', 'altar', 'amongst', 'his', 'other', 'verses', '.', 'No', ',', 'I', 'will', 'not', 'put', 'it', 'there', '.', 'This', 'one', 'is', 'worthy', 'of', 'the', 'altar', '.', '[', 'He', 'places', 'the', 'sonnet', 'upon', 'the', 'altar', 'itself', '.', 'If', 'that', 'sonnet', 'does', 'not', 'give', 'me', 'fame', ',', 'nothing', 'that', 'I', 'have', 'done', 'before', 'will', 'give', 'it', 'to', 'me', ',', 'nothing', 'that', 'I', 'ever', 'will', 'do', '.', '[', 'He', 'replaces', 'the', 'screen', 'and', 'returns', 'to', 'his', 'chair', 'at', 'the', 'table', '.', 'Twilight', 'is', 'coming', 'on', '.', 'He', 'sits', 'with', 'his', 'elbow', 'on', 'the', 'table', ',', 'his', 'head', 'on', 'his', 'hand', ',', 'or', 'however', 'the', 'actor', 'pleases', '.', 'Well', ',', 'well', '.', 'Fancy', 'seeing', 'Dick', 'again', '.', 'Well', ',', 'Dick', 'enjoys', 'his', 'life', ',', 'so', 'he', "'s", 'no', 'fool', '.', 'What', 'was', 'that', 'he', 'said', '?', '``', 'There', "'s", 'no', 'money', 'in', 'poetry', '.', 'You', "'d", 'better', 'chuck', 'it', '.', "''", 'Ten', 'years', "'", 'work', 'and', 'what', 'have', 'I', 'to', 'show', 'for', 'it', '?', 'The', 'admiration', 'of', 'men', 'who', 'care', 'for', 'poetry', ',', 'and', 'how', 'many', 'of', 'them', 'are', 'there', '?', 'There', "'s", 'a', 'bigger', 'demand', 'for', 'smoked', 'glasses', 'to', 'look', 'at', 'eclipses', 'of', 'the', 'sun', '.', 'Why', 'should', 'Fame', 'come', 'to', 'me', '?', 'Have', "n't", 'I', 'given', 'up', 'my', 'days', 'for', 'her', '?', 'That', 'is', 'enough', 'to', 'keep', 'her', 'away', '.', 'I', 'am', 'a', 'poet', ';', 'that', 'is', 'enough', 'reason', 'for', 'her', 'to', 'slight', 'me', '.', 'Proud', 'and', 'aloof', 'and', 'cold', 'as', 'marble', ',', 'what', 'does', 'Fame', 'care', 'for', 'us', '?', 'Yes', ',', 'Dick', 'is', 'right', '.', 'It', "'s", 'a', 'poor', 'game', 'chasing', 'illusions', ',', 'hunting', 'the', 'intangible', ',', 'pursuing', 'dreams', '.', 'Dreams', '?', 'Why', ',', 'we', 'are', 'ourselves', 'dreams', '.', '[', 'He', 'leans', 'back', 'in', 'his', 'chair', '.', 'We', 'are', 'such', 'stuff', 'As', 'dreams', 'are', 'made', 'on', ',', 'and', 'our', 'little', 'life', 'Is', 'rounded', 'with', 'a', 'sleep', '.', '[', 'He', 'is', 'silent', 'for', 'a', 'while', '.', 'Suddenly', 'he', 'lifts', 'his', 'head', '.', 'My', 'room', 'at', 'Eton', ',', 'Dick', 'said', '.', 'An', 'untidy', 'mess', '.', '[', 'As', 'he', 'lifts', 'his', 'head', 'and', 'says', 'these', 'words', ',', 'twilight', 'gives', 'place', 'to', 'broad', 'daylight', ',', 'merely', 'as', 'a', 'hint', 'that', 'the', 'author', 'of', 'the', 'play', 'may', 'have', 'been', 'mistaken', ',', 'and', 'the', 'whole', 'thing', 'may', 'have', 'been', 'no', 'more', 'than', 'a', 'poet', "'s", 'dream', '.', 'So', 'it', 'was', ',', 'and', 'it', "'s", 'an', 'untidy', 'mess', 'there', '(', 'looking', 'at', 'screen', ')', 'too', '.', 'Dick', "'s", 'right', '.', 'I', "'ll", 'tidy', 'it', 'up', '.', 'I', "'ll", 'burn', 'the', 'whole', 'damned', 'heap', ',', '[', 'He', 'advances', 'impetuously', 'towards', 'the', 'screen', '.', 'every', 'damned', 'poem', 'that', 'I', 'was', 'ever', 'fool', 'enough', 'to', 'waste', 'my', 'time', 'on', '.', '[', 'He', 'pushes', 'back', 'the', 'screen', '.', 'Fame', 'in', 'a', 'Greek', 'dress', 'with', 'a', 'long', 'golden', 'trumpet', 'in', 'her', 'hand', 'is', 'seen', 'standing', 'motionless', 'on', 'the', 'altar', 'like', 'a', 'marble', 'goddess', '.', 'So', '...', 'you', 'have', 'come', '!', '[', 'For', 'a', 'while', 'he', 'stands', 'thunderstruck', '.', 'Then', 'he', 'approaches', 'the', 'altar', '.', 'Divine', 'fair', 'lady', ',', 'you', 'have', 'come', '.', '[', 'He', 'holds', 'up', 'his', 'hand', 'to', 'her', 'and', 'leads', 'her', 'down', 'from', 'the', 'altar', 'and', 'into', 'the', 'centre', 'of', 'the', 'stage', '.', 'At', 'whatever', 'moment', 'the', 'actor', 'finds', 'it', 'most', 'convenient', ',', 'he', 'repossesses', 'himself', 'of',
'the', 'sonnet', 'that', 'he', 'had', 'placed', 'on', 'the', 'altar', '.', 'He', 'now', 'offers', 'it', 'to', 'Fame', '.', 'This', 'is', 'my', 'sonnet', '.', 'Is', 'it', 'well', 'done', '?', '[', 'Fame', 'takes', 'it', 'and', 'reads', 'it', 'in', 'silence', ',', 'while', 'the', 'Poet', 'watches', 'her', 'rapturously', '.', 'Fame', ':', 'You', "'re", 'a', 'bit', 'of', 'all', 'right', '.', 'de', 'Reves', ':', 'What', '?', 'Fame', ':', 'Some', 'poet', '.', 'de', 'Reves', ':', 'I', '-', 'I', '-', 'scarcely', '...', 'understand', '.', 'Fame', ':', 'You', "'re", 'IT', '.', 'de', 'Reves', ':', 'But', '...', 'it', 'is', 'not', 'possible', '...', 'are', 'you', 'she', 'that', 'knew', 'Homer', '?', 'Fame', ':', 'Homer', '?', 'Lord', ',', 'yes',
'.', 'Blind', 'old', 'bat', ',', "'", 'e', 'could', "n't", 'see', 'a', 'yard', '.', 'de', 'Reves', ':', 'O', 'Heavens', '!', '[', 'Fame', 'walks', 'beautifully', 'to', 'the', 'window', '.', 'She', 'opens', 'it', 'and', 'puts', 'her', 'head', 'out', '.', 'Fame', '(', 'in', 'a', 'voice', 'with', 'which', 'a', 'woman', 'in', 'an', 'upper', 'storey', 'would', 'cry', 'for', 'help', 'if', 'the', 'house', 'was', 'well', 'alight', ')', ':', 'Hi', '!', 'Hi', '!', 'Boys', '!', 'Hi', '!', 'Say', ',', 'folks', '!', 'Hi', '!', '[', 'The', 'murmur', 'of', 'a', 'gathering', 'crowd', 'is', 'heard', '.', 'Fame', 'blows', 'her', 'trumpet', '.', 'Fame', ':', 'Hi', ',', 'he', "'s", 'a', 'poet', '!', '(', 'Quickly', ',', 'over', 'her', 'shoulder', '.', ')', 'What', "'s", 'your', 'name', '?', 'de', 'Reves', ':', 'De', 'Reves', '.', 'Fame', ':', 'His', 'name', "'s", 'de', 'Reves', '.', 'de', 'Reves', ':', 'Harry', 'de', 'Reves', '.', 'Fame', ':', 'His', 'pals', 'call', 'him', 'Harry', '.', 'The', 'Crowd', ':', 'Hooray', '!', 'Hooray', '!', 'Hooray', '!', 'Fame', ':', 'Say', ',', 'what', "'s", 'your', 'favourite', 'colour', '?', 'de', 'Reves', ':', 'I', '...', 'I', '...', 'I', 'do', "n't", 'quite', 'understand', '.', 'Fame', ':', 'Well', ',', 'which', 'do', 'you', 'like', 'best', ',', 'green', 'or', 'blue', '?', 'de', 'Reves', ':', 'Oh', '-', 'er', '-', 'blue', '.', '[', 'She', 'blows', 'her', 'trumpet', 'out', 'of', 'the', 'window', '.', 'No', '-', 'er', '-', 'I', 'think', 'green', '.', 'Fame', ':', 'Green', 'is', 'his', 'favourite', 'colour', '.', 'The', 'Crowd', ':', 'Hooray', '!', 'Hooray', '!', 'Hooray', '!', 'Fame', ':', '`', 'Ere', ',', 'tell', 'us', 'something', '.', 'They', 'want', 'to', 'know', 'all', 'about', 'yer', '.', 'de', 'Reves', ':', 'Would', "n't", '9', 'you', 'perhaps', '...', 'would', 'they', 'care', 'to', 'hear', 'my', 'sonnet', ',', 'if', 'you', 'would', '-', 'er', '...', 'Fame', '(', 'picking', 'up', 'quill', ')', ':', 'Here', ',', 'what', "'s", 'this', '?', 'de', 'Reves', ':', 'Oh', ',', 'that', "'s", 'my', 'pen', '.', 'Fame', '(', 'after', 'another', 'blast', 'on', 'her', 'trumpet', ')', ':', 'He', 'writes', 'with', 'a', 'quill', '.', '[', 'Cheers', 'from', 'the', 'Crowd', '.', 'Fame', '(',
'going', 'to', 'a', 'cupboard', ')', ':', 'Here', ',', 'what', 'have', 'you', 'got', 'in', 'here', '?', 'de', 'Reves', ':', 'Oh', '...', 'er', '...', 'those', 'are', 'my', 'breakfast', 'things', '.', 'Fame', '(', 'finding', 'a', 'dirty', 'plate', ')', ':', 'What', 'have', 'yer', 'had', 'on', 'this', 'one', '?', 'de', 'Reves', '(', 'mournfully', ')', ':', 'Oh', ',', 'eggs', 'and', 'bacon', '.', 'Fame', '(', 'at', 'the', 'window', ')', ':', 'He', 'has', 'eggs', 'and', 'bacon', 'for', 'breakfast', '.', 'The', 'Crowd', ':', 'Hip', 'hip', 'hip', ',', 'hooray', '!', 'Hip', 'hip', 'hip', ',', 'hooray', '!', 'Hip', 'hip', 'hip', ',', 'hooray', '!', 'Fame', ':', 'Hi', ',', 'and', 'what', "'s", 'this', '?', 'de', 'Reves', '(', 'miserably', ')', ':', 'Oh', ',', 'a', 'golf', 'stick', '.', 'Fame', ':', 'He', "'s", 'a', 'man', "'s", 'man', '!', 'He', "'s", 'a', 'virile', 'man', '!', 'He', "'s", 'a', 'manly', 'man', '!', '[', 'Wild', 'cheers', 'from', 'the', 'Crowd', ',', 'this', 'time', 'only', 'from', 'women', "'s", 'voices', '.', 'de', 'Reves', ':', 'Oh', ',', 'this', 'is', 'terrible', '.', 'This', 'is', 'terrible', '.', 'This', 'is', 'terrible', '.', '[', 'Fame', 'gives', 'another', 'peal', 'on', 'her', 'horn', '.', 'She', 'is', 'about', 'to', 'speak', '.', 'de', 'Reves', '(', 'solemnly', 'and', 'mournfully', ')', ':', 'One', 'moment', ',', 'one', 'moment', '...', 'Fame', ':', 'Well', ',', 'out', 'with', 'it', '.', 'de', 'Reves', ':', 'For', 'ten', 'years', ',', 'divine', 'lady', ',', 'I', 'have', 'worshipped', 'you', ',', 'offering', 'all', 'my', 'songs', '...', 'I', 'find', '...', 'I', 'find', 'I', 'am', 'not', 'worthy', '...', 'Fame', ':', 'Oh', ',', 'you', "'re", 'all', 'right', '.', 'de', 'Reves', ':', 'No', ',', 'no', ',', 'I', 'am', 'not', 'worthy', '.', 'It', 'can', 'not', 'be', '.', 'It', 'can', 'not', 'possibly', 'be', '.', 'Others', 'deserve', 'you', 'more', '.', 'I', 'must', 'say', 'it', '!', 'I', 'can', 'not', 'possibly', 'love', 'you', '.', 'Others', 'are', 'worthy', '.', 'You', 'will', 'find', 'others', '.', 'But', 'I', ',', 'no', ',', 'no', ',', 'no', '.', 'It', 'can', 'not', 'be', '.', 'It', 'can', 'not', 'be', '.', 'Oh', ',', 'pardon', 'me', ',', 'but', 'it', 'must', 'not', '.', '[', 'Meanwhile', 'Fame', 'has', 'been', 'lighting', 'one', 'of', 'his', 'cigarettes', '.', 'She', 'sits', 'in', 'a', 'comfortable', 'chair', ',', 'leans', 'right', 'back', ',', 'and', 'puts', 'her', 'feet', 'right', 'up', 'on', 'the', 'table', 'amongst', 'the', 'poet', "'s", 'papers', '.', 'Oh', ',', 'I', 'fear', 'I', 'offend', 'you', '.', 'But', '-', 'it', 'can', 'not', 'be', '.', 'Fame', ':', 'Oh', ',', 'that', "'s", 'all', 'right', ',', 'old', 'bird', ';', 'no', 'offence', '.', 'I', 'ai', "n't", 'going', 'to', 'leave', 'you', '.', 'de', 'Reves', ':', 'But', '-', 'but', '-', 'but', '-', 'I', 'do', 'not', 'understand', '.', 'Fame', ':', 'I', "'ve", 'come', 'to', 'stay', ',', 'I', 'have', '.', '[', 'She', 'blows', 'a', 'puff', 'of', 'smoke', 'through', 'her', 'trumpet', '.', 'CURTAIN', '.'], 'pos_tags': [10, 0, 2, 12, 12, 12, 12, 12, 12, 38, 2, 12, 38, 41, 2, 10, 38, 18, 5, 10, 5, 6, 10, 38, 30, 29, 29, 0, 30, 6, 12, 12, 38, 42, 12, 12, 38, 2, 12, 5, 2, 12, 12, 13, 38, 12, 38, 10, 2, 12, 15, 11, 5, 12, 38, 11, 5, 18, 38, 2, 6, 10, 5, 2, 10, 38, 10, 38, 12, 6, 38, 2, 12, 30, 28, 5, 2, 10, 10, 38, 41, 12, 12, 12, 38, 10, 38, 12, 38, 12, 38, 12, 12, 38, 12, 38, 12, 38, 6, 12, 38, 35, 31, 16, 5, 22, 10, 41, 18, 42, 38, 2, 11, 5, 2, 10, 38, 12, 12, 38, 25, 38, 16, 31, 29, 22, 10, 38, 27, 16, 9, 26, 21, 0, 26, 35, 16, 27, 28, 5, 38, 12, 12, 38, 25, 38, 32, 30, 6, 38, 33, 31, 16, 28, 5, 12, 22, 10, 38, 18, 38, 16, 27, 24, 26, 5, 16, 9, 26, 1, 0, 1, 6, 11, 24, 26, 38, 16, 9, 26, 10, 21, 18, 38, 18, 16, 27, 16, 9, 26, 2, 10, 0, 26, 35, 12, 27, 28, 5, 38, 12, 12, 38, 6, 22, 35, 30, 10, 22, 10, 38, 2, 28, 6, 38, 12, 12, 38, 32, 30, 6, 38, 10, 41, 28, 10, 0, 10, 42, 38, 0, 33, 31, 16, 28, 22, 12, 12, 38, 28, 38, 10, 38, 28,
22, 16, 27, 36, 26, 16, 27, 38, 12, 12, 38, 25, 38, 16, 31, 29, 24, 16, 18, 38, 10, 38, 16, 31, 38, 28, 30, 18, 6, 38, 33, 31, 16, 26, 22, 12, 12, 38, 25, 38, 10, 38, 10, 38, 10, 22, 6, 12, 22, 12, 12, 38, 25, 38, 2, 10, 5, 10, 38, 16, 31, 38, 10,
38, 6, 12, 22, 26, 16, 26, 2, 10, 5, 16, 22, 12, 12, 38, 25, 38, 18, 18, 38, 10, 38, 16, 31, 38, 35, 31, 36, 16, 31, 16, 22, 12, 12, 38, 25, 38, 16, 31, 36, 26, 38, 2, 11, 31, 24, 26, 17, 10, 38, 18, 38, 2, 30, 35, 16, 31, 5, 38, 10, 38, 16, 9, 26, 16, 5, 3, 30, 2, 10, 5, 16, 38, 12, 12, 38, 25, 38, 0, 18, 16, 30, 18, 5, 17, 10, 38, 30, 16, 22, 16, 9, 18, 26, 5, 10, 5, 3, 27, 10, 5, 16, 38, 10, 38, 25, 38, 16, 31, 36, 26, 2, 38, 5, 16, 9, 26, 18, 18, 5, 10, 5, 16, 31, 5, 28, 16, 31, 36, 26,
16, 9, 36, 26, 2, 10, 10, 38, 18, 38, 38, 12, 12, 38, 18, 33, 22, 10, 38, 25, 38, 16, 31, 36, 26, 38, 18, 3, 30, 7, 10, 5, 28, 38, 18, 38, 12, 12, 38, 25, 38, 25, 38, 16, 31, 16, 30, 7, 24, 26, 33, 2, 6, 10, 30, 28, 24, 26, 38, 5, 24, 26, 33, 12, 38, 38, 10, 38, 33, 30, 12, 22, 12, 12, 38, 25, 38, 2, 29, 10, 5, 11, 38, 10, 38, 16, 31, 22, 16, 31, 36, 26, 5, 2, 29, 10, 38, 31, 16, 22, 12, 12, 38, 5, 17, 10, 16, 31, 5, 2, 6, 11, 38, 16, 18, 31, 2, 6, 10, 24, 16, 38, 2, 10, 5, 12, 30, 18, 6, 2, 10, 24, 2, 10, 5, 2, 12, 10, 9, 26, 24, 16, 38, 10, 38, 16, 31, 38, 41, 26, 16, 2, 10, 38, 11, 38, 42, 33, 22, 18, 16, 9, 26, 5, 11, 0, 11, 38, 0, 12, 38, 0, 14, 2, 10, 5, 11, 22, 12, 12, 38, 25, 38, 25, 38, 5, 2, 5, 16, 38, 10, 38, 6, 12, 22, 12, 12, 38, 16, 31, 5, 2, 12, 12, 5, 12, 38, 31, 36, 16, 22, 10, 38, 25, 38, 5, 10, 38, 0, 33, 30, 38, 38, 12, 12, 38, 1, 1, 11, 0, 18, 27, 16, 12, 12, 38, 27, 36, 16, 22, 0, 16, 30, 24, 16, 2, 10, 0, 10, 0, 10, 5, 38, 38, 10, 38, 25, 38, 0, 38, 16, 31, 38, 33, 30, 14, 2, 38, 38, 12, 12, 38, 25, 38, 16, 30, 5, 2, 10, 24, 16, 38, 0, 16, 27, 16, 12, 12, 38, 0, 18, 16, 30, 1, -1, 10, 38, 18, 38, 5, 10, 16, 30, 38, 12, 12, 38, 5, 2, 6, 10, 12, 30, 29, 29, 33, 16, 30, 5, 11, 38, 5, 11, 24, 33, 16, 30, 6, 11, 38, 10, 41, 28, 5, 17, 10, 0, 28, 18, 38, 28, 0, 28, 5, 2, 12, 5, 2, 10, 5, 6, 10, 42, 38, 16, 31, -1, 16, 31, -1, 16, 6, 11, -1, 0, 12, 12, -1, 41, 16, 30, 5, 2, 6, 10, 18, 38, 28, 16, 18, 2, 6, 38, 12, 12, 38, 31, 21, 22, 26, 21, 22, 10, 38, 33, 22, 33, 30, 2, 10, 22, 12, 12, 38, 2, 10, 22, 10, 38, 25, 38, 18, 38, 25, 38, 16, 9, 26, 16, 18, 38, 41, 16, 30, 18, 24, 26, 10, 5, 16, 38, 12, 12, 38, 25, 38, 31, 36, 26, 10, 18, 38, 10, 38, 33, 22, 35, 36, 22, 12, 12, 38, 25, 38, 16, 9, 36, 26, 38, 10, 38, 9, 36, 26, 22, 35, 38, 33, 31, 16, 27, 22, 12, 12, 38, 25, 38, 1, 5, 2, 11, -1, 16, 9, 36, 26, 38, 10, 38, 5, 10, 16, 9, 26, 38, 26, 30, 26, 2, 10, 38, 41, 12, 12, 30, 5, 12, 0, 2, 10, 38, 16, 30, 18, 7, 38, 10, 11, 31, 2, 10,
5, 2, 10, 38, 2, 10, 38, 12, 12, 41, 28, 2, 10, 18, 42, 38, 32, 30, 18, 38, 33, 31, 16, 26, 5, 16, 22, 41, 2, 10, 5, 6, 10, 38, 29, 5, 2, 10, 38, 30, 29, 38, 11, 31, 2, 10, 18, 5, 16, 38, 10, 38, 16, 31, 38, 16, 18, 27, 2, 6, 10, 38, 12, 12, 38, 25, 38, 33, 31, 16, 26, 5, 16, 22, 10, 38, 16, 30, 16, 5, 17, 10, 5, 12, 38, 12, 12, 38, 17, 10, 5, 12, 22, 10, 38, 25, 38, 16, 18, 27, 11, 18, 5, 17, 10, 38, 12, 12, 38, 25, 38, 25, 38, 38, 10, 38, 0, 33, 31, 2, 22, 12, 12, 38, 14, 2, 31, 11, 38, 0, 2, 30, 17, 10, 24, 12, 38, 10, 38, 24, 12, 22, 12, 12, 38, 2, 6, 5, 12, 27, 38, 10, 38, 6, 12, 22, 12, 12, 38, 12, 18, 27, 16, 38, 12, 27, 18, 6, 38, 16, 27, 18, 5, 2, 8, 5, 11, 38, 18, 18, 18, 38, 10, 38, 0, 38, 17, 6, 10, 38, 16, 31, 36, 26, 5,
16, 31, 3, 18, 30, 14, 2, 10, 22, 12, 12, 38, 16, 31, 14, 17, 11, 24, 16, 38, 10, 38, 0, 16, 31, 36, 26, 16, 31, 16, 9, 18, 26, 12, 22, 12, 12, 38, 16, 11, 31, 6, 11, 38, 0, 36, 11, 6, 0, 6, 0, 11, 18, 38, 14, 2, 6, 11, 5, 2, 10, 31, 2, 6, 11, 38,
10, 38, 0, 33, 16, 31, 30, 38, 16, 31, 36, 18, 18, 38, 5, 16, 0, 16, 38, 12, 12, 38, 24, 16, 2, 11, 31, 19, 6, 5, 11, 38, 16, 31, 11, 38, 16, 31, 2, 10, 5, 11, 38, 16, 31, 5, 16, 31, 10, 38, 16, 31, 18, 18, 38, 6, 38, 12, 38, 10, 38, 0, 38, 18, 38, 16, 9, 36, 26, 5, 16, 9, 26, 12, 38, 16, 31, 36, 26, 24, 26, 16, 22, 12, 12, 38, 36, 24, 16, 38, 18, 24, 16, 38, 16, 5, 2, 6, 10, 0, 6, 10, 9, 18, 26, 24, 16, -1, 16, 18, 31, 17, 11, 38, 10, 38, 16, 31, 38, 33, 31, 16, 29, 28, 2, 10, 22, 12, 12, 38, 16, 22, 25, 38, 18, 28, 2, 10, 38, 10, 38, 30, 16, 2, 6, 1, 22, 12, 12, 38, 36, 18, 38, 10, 38, 18, 35, 18, 30, 16, 22, 12, 12, 38, 5, 10, 11, 38, 10, 41, 18, 42, 38, 16, 26, 16, 33, 16, 30, 38, 12, 12, 38, 25, 22, 10, 38, 16, 26, 16, 33, 38, 16, 31, 29, 28, 16, 38, 16, 18, 27, 5, 5, 5, 10, 2, 12, 38, 28, 5, 2, 6, 10, 38, 16, 27, 18, 6, 5, 16, 9, 26, 29, 10, 38, 12, 12, 38, 29, 10, 22, 10, 38, 12, 38, 25, 38, 29, 11, 38, 11, 5, 11, 38, 10, 38, 1, 5, 17, 29, 11, 18, 38, 16, 27, 16, 2, 10,
27, 10, 5, 16, 38, 16, 31, 2, 10, 38, 12, 12, 38, 0, 17, 6, 10, 38, 16, 31, 36, 26, 5, 2, 38, 16, 18, 27, 5, 6, 11, 31, 24, 2, 10, 5, 6, 0, 6, 0, 6, 5, 1, 5, 17, 11, 0, 11, 38, 10, 38, 16, 31, 38, 16, 31, 2, 10, 38, 12, 12, 38, 25, 38, 18, 16, 9, 38, 16, 9, 26, 5, 16, 24, 2, 6, 10, 16, 31, 28, 24, 26, 38, 18, 16, 31, 2, 10, 29, 5, 28, 2, 38, 16, 30, 2, 6, 10, 38, 16, 9, 26, 2, 10, 38, 10, 38, 35, 31, 16, 31, 16, 31, 28, 24, 26, 2, 6, 10, 22, 12, 12, 38, 25, 38, 35, 9, 16, 26, 22, 12, 30, 1,
5, 5, 2, 12, 12, 15, 38, 16, 31, 36, 28, 18, 38, 10, 38, 31, 16, 31, 5, 16, 22, 12, 12, 38, 25, 38, 10, 38, 18, 38, 16, 31, 18, 6, 38, 16, 31, 28, 24, 26, 39, 2, 12, 5, 12, 38, 40, 18, 18, 38, 16, 9, 26, 21, 18, 38, 16, 30, 28, 18, 38, 16, 31, 2, 10, 38, 31, 36, 26, 2, 10, 24, 2, 10, 38, 10, 30, 18, 6, 38, 16, 31, 2, 10, 38, 31, 36, 26, 2, 10, 10, 38, 18, 10, 38, 16, 27, 6, 5, 5, 16, 38, 18, 18, 38, 12, 12, 38, 18, 18, 38, 41, 10, 12, 38, 12, 12, 30, 24, 17, 10, 0, 30, 21, 38, 6, 6, 12, 22,
16, 30, 2, 6, 18, 18, 38, 12, 38, 35, 10, 30, 38, 16, 30, 17, 10, 0, 17, 10, 0, 30, 2, 6, 11, 38, 18, 38, 32, 30, 29, 38, 16, 9, 36, 26, 2, 19, 24, 16, 38, 41, 16, 30, 0, 30, 24, 2, 10, 38, 16, 30, 18, 10, 5, 16, 0, 30, 21, 24, 2, 10, 38, 16, 30, 18, 24, 26, 17, 10, 18, 5, 2, 10, 5, 2, 10, 5, 17, 6, 11, 38, 25, 38, 16, 9, 36, 26, 16, 18, 38, 2, 1, 30, 6, 5, 2, 10, 38, 41, 16, 30, 2, 10, 5, 2, 10, 16, 38, 5, 2, 10, 30, 36, 26, 16, 10, 38, 10, 5, 16, 31, 29, 18, 9, 26, 16, 24, 16, 38, 10, 5, 16, 18, 9, 26, 38, 41, 16, 30, 2, 10, 0, 11, 24, 17, 10, 5, 2, 10, 38, 10, 30, 28, 21, 38, 16, 30, 5, 17, 10, 5, 2, 10, 38, 17, 10, 5, 17, 10, 38, 0, 18, 2, 10, 30, 38, 25, 38, 25, 38, 6, 28, 12, 18, 38, 18, 38, 12, 30, 17, 10, 38, 18, 16, 30, 2, 10, 38, 33, 27, 5, 16, 27, 22, 39, 3, 30, 2, 10, 5, 10, 38, 16, 9, 19, 26, 16, 38, 40, 1, 11, 15, 10, 0, 33, 31, 16, 24, 26, 5, 16, 22, 2, 10, 5, 11, 33, 31, 5, 10, 38, 0, 35, 6, 5, 16, 31, 18, 22, 3, 30, 2, 7, 10, 5, 29, 11, 24, 26, 5, 11, 5, 2, 10, 38, 35, 9, 12, 26, 24, 16, 22, 31, 36, 16, 29, 21, 17, 11, 5, 16, 22, 2, 30, 6, 24, 26, 16, 21, 38, 16, 31, 2, 10, 38, 32, 30, 18, 10, 5, 16, 24, 26, 16, 38, 6, 0, 6, 0, 6, 5, 10, 38, 33, 30, 12, 10, 5, 16, 22, 25, 38, 12, 30, 6, 38, 16, 30, 2, 6, 10, 28, 11, 38, 28, 2, 10, 38, 28, 11, 38, 11, 22, 35, 38, 16, 31, 16, 30, 38, 41, 16, 30, 18, 5, 17, 10, 38, 16, 31, 6, 10, 5, 11, 31, 29, 5, 38, 0, 17, 6, 10, 30, 29, 5, 2, 10, 38, 41, 16, 30, 6, 5, 2, 10, 38, 18, 16, 30, 17, 10, 38, 17, 10, 5,
12, 38, 12, 27, 38, 2, 6, 10, 38, 41, 5, 16, 30, 17, 10, 0, 30, 2, 11, 38, 10, 30, 10, 24, 6, 10, 38, 18, 5, 2, 10, 5, 2, 10, 5, 2, 10, 9, 26, 29, 29, 38, 0, 2, 6, 10, 9, 26, 29, 18, 7, 5, 2, 10, 15, 10, 38, 18, 16, 27, 38, 0, 16, 30, 2, 6, 10, 18, 41, 28, 5, 10, 42, 18, 38, 12, 15, 10, 38, 16, 9, 26, 16, 21, 38, 16, 9, 26, 2, 6, 6, 10, 38, 41, 16, 30, 18, 5, 2, 10, 38, 2, 6, 10, 5, 16, 27, 18, 6, 18, 24, 26, 17, 10, 21, 38, 41, 16, 30, 18, 2, 10, 38, 10, 5, 2, 6, 10, 5, 2, 6, 6, 10, 5, 17,
10, 30, 29, 28, 6, 5, 2, 10, 5, 2, 10, 10, 38, 18, -1, 16, 31, 29, 22, 41, 5, 2, 5, 16, 30, 6, 38, 18, 16, 30, 2, 10, 38, 12, 6, 10, 38, 16, 31, 29, 38, 41, 16, 30, 21, 17, 10, 24, 16, 0, 30, 16, 21, 5, 2, 10, 0, 5, 2, 10, 5, 2, 10, 38, 5, 32, 10,
2, 10, 30, 16, 20, 6, 38, 16, 30, 16, 5, 2, 10, 5, 16, 27, 29, 5, 2, 10, 38, 16, 18, 30, 16, 24, 12, 38, 2, 30, 17, 10, 38, 30, 16, 18, 29, 22, 41, 12, 30, 16, 0, 30, 16, 5, 10, 38, 5, 2, 12, 30, 16, 18, 38, 10, 38, 16, 31, 2, 10, 5, 2, 10, 38, 12, 12, 38, 33, 22, 10, 38, 2, 10, 38, 12, 12, 38, 16, 38, 16, 38, 18, -1, 26, 38, 10, 38, 16, 31, 16, 38, 12, 12, 38, 0, -1, 16, 30, 36, 6, -1, 31, 16, 16, 32, 27, 12, 22, 10, 38, 10, 22, 12, 38, 25, 38, 6, 6, 10, 38, 40, 12, 9, 36, 26, 2, 10, 38, 12, 12, 38, 12, 12, 22, 41, 12, 30, 18, 24, 2, 10, 38, 16, 30, 16, 0, 30, 17, 10, 21, 38, 12, 41, 5, 2, 10, 5, 32, 2, 10, 5, 2, 6, 10, 9, 26, 5, 10, 5, 2, 10, 27, 18, 6, 42, 38, 25, 22, 25, 22, 13, 22, 25, 22, 26, 38, 11, 22, 25, 22, 41, 2, 10, 5, 2, 10, 10, 30, 29, 38, 12, 30, 17, 10, 38, 12, 38, 25, 38, 16, 30, 2, 10, 22, 41, 18, 38, 5, 17, 10, 38, 42, 33, 30, 17, 10, 22, 12, 12, 38, 12, 12, 38, 10, 38, 16, 31, 30, 12, 12, 38, 12, 12, 38, 12, 12, 12, 38, 10, 38, 16, 30, 26, 16, 12, 38, 2, 10, 38, 11, 22, 11, 22, 11, 22, 10, 38, 26, 38, 33, 30, 17, 6, 10, 22, 12, 12, 38, 16, -1, 16, -1, 16, 31, 36, 18, 26, 38, 10, 38, 18, 38, 32, 31, 16, 5, 8, 38, 6, 0, 6, 22, 12, 12, 38, 25, 38, 25, 38, 6, 38, 41, 16, 30, 17, 10, 21, 5, 2, 10, 38, 25, 38, 25, 38, 16, 31, 6, 38, 10, 38, 12, 30, 17, 6, 10, 38, 2, 10, 38, 11, 22, 11, 22, 11, 22, 12, 38, 39, 6, 38, 26, 16, 10, 38, 16, 31, 24, 26, 2, 18, 6, 38, 12, 12, 38, 9, 36, 1, 16, 18, -1, 9, 16, 26, 24, 26, 17, 10, 38, 5, 16, 9, 38, 25, -1, 12, 41, 28, 21, 10, 42, 38, 18, 38, 33, 30, 2, 22, 12, 12, 38, 25, 38, 32, 30, 17, 10, 38, 12, 41, 5, 2, 10, 5, 16, 31, 42, 38, 16, 30, 5, 2, 10, 38, 41, 12, 5, 2, 10, 38, 12, 41, 28, 24, 2, 10, 42, 38, 18, 38, 33, 31, 16, 29, 5, 18, 22, 12, 12, 38,
25, -1, 25, -1, 2, 31, 17, 10, 11, 38, 12, 41, 28, 2, 6, 10, 42, 38, 33, 31, 18, 29, 5, 2, 1, 22, 12, 12, 41, 18, 42, 38, 25, 38, 11, 0, 10, 38, 12, 41, 5, 2, 10, 42, 38, 16, 30, 11, 0, 10, 5, 10, 38, 2, 10, 38, 6, 10, 10, 38, 11, 22, 6, 6, 10, 38, 11, 22, 6, 6, 10, 38, 11, 22, 12, 38, 25, 38, 0, 33, 30, 2, 22, 12, 12, 41, 18, 42, 38, 25, 38, 2, 10, 10, 38, 10, 38, 16, 30, 2, 10, 15, 10, 22, 16, 30, 2, 6, 10, 22, 16, 30, 2, 6, 10, 22, 41, 12, 11, 5, 2, 12, 38, 2, 10, 18, 5, 11, 15, 11, 38, 12, 12, 38, 25, 38, 2, 30, 6, 38, 2, 30, 6, 38, 2, 30, 6, 38, 41, 12, 30, 2, 10, 5, 17, 10, 38, 16, 30, 18, 24, 26, 38, 12, 12, 41, 18, 0, 18, 42, 38, 1, 10, 38, 1, 10, -1, 10, 38, 18, 38, 18, 5, 16, 38, 12, 12, 38, 5, 1, 11, 38, 6, 10, 38, 16, 31,
29, 16, 38, 28, 14, 17, 11, -1, 16, 31, -1, 16, 31, 16, 31, 36, 6, -1, 12, 38, 25, 38, 16, 31, 2, 10, 38, 12, 12, 38, 25, 38, 25, 38, 16, 31, 36, 6, 38, 16, 9, 36, 26, 38, 16, 31, 36, 18, 26, 38, 11, 31, 16, 7, 38, 16, 9, 26, 16, 22, 16, 31, 36, 18, 26, 16, 38, 11, 31, 6, 38, 16, 9, 26, 11, 38, 0, 16, 38, 25, 38, 25, 38, 25, 38, 16, 9, 36, 26, 38, 16, 9, 36, 26, 38, 25, 38, 26, 16, 38, 0, 16, 9, 36, 38, 41, 18, 12, 30, 29, 28, 1, 5, 17, 11, 38, 16, 30, 5, 2, 6, 10, 38, 30, 18, 18, 38, 0, 30, 17, 11, 18, 18, 5, 2, 10, 5, 2, 10, 15, 11, 38, 25, 38, 16, 31, 16, 26, 16, 38, 0, 38, 16, 9, 36, 26, 38, 12, 38, 25, 38, 32, 30, 18, 6, 38, 6, 10, 38, 2, 10, 38, 16, 31, 36, 28, 24, 26, 16, 38, 12, 12, 38, 0, 38, 18, 38, 18, 38, 16, 31, 36, 26, 38, 10, 38, 16, 31, 29, 24, 26, 38, 16, 31, 38, 41, 16, 30, 2, 10, 5, 10, 5, 17, 10, 38, 10, 38], 'genre': 'Drama', 'subgenre': 'drama', 'year': '1919', 'quarter_cent': '1900-1924', 'decade': '1910s', 'title': 'Fame and the poet', 'author': 'Dunsany [Edward John Moreton Drax Plunkett]', 'notes': '', 'comments': 'selected from larger file', 'period': '1850-1920', 'id': '317'}
```
### Data Fields
There are three configs in this dataset- `plain`, `class` and `pos`. `plain` is a simple text dataset whereas `pos` and `class` are both annotated datasets containing pos tagging. A `plain` data point has the following fields:
```
{
"text": The text in the sample("string"),
"genre": The genre of the text("string"),
"subgenre": The subgenre of the text("string"),
"year": The year the text was produced("string"),
"quarter_cent": The quarter century in which the text was produced("string"),
"decade": The decade the text was produced("string"),
"title": The title of the text("string"),
"author": The author of the text("string"),
"notes": Notes about the text, if any("string"),
"comments": Commentsabout the text, if any("string"),
"period": 70-year period during which the text was produced("string"),
"id": Unqiue identifier("string"),
}
```
A typical `pos`/`class` data point has the following fields:
```
{
"text": The tokens in the sample(list("string")),
"pos_tags": Corresponding POS tags for the tokens (list("string"))
"genre": The genre of the text("string"),
"subgenre": The subgenre of the text("string"),
"year": The year the text was produced("string"),
"quarter_cent": The quarter century in which the text was produced("string"),
"decade": The decade the text was produced("string"),
"title": The title of the text("string"),
"author": The author of the text("string"),
"notes": Notes about the text, if any("string"),
"comments": Commentsabout the text, if any("string"),
"period": 70-year period during which the text was produced("string"),
"id": Unqiue identifier("string"),
}
```
### Data Splits
Train: 333
## Dataset Creation
### Curation Rationale
The Corpus of Late Modern English Texts (CLMET) is a corpus of roughly 35 million words of
British English from 17101920, grouped into three 70-year periods (De Smet 2005; Diller et
al. 2011). The history, versions and specifics of corpus composition can be followed up by
referring to the CLMET3.0 website. CLMET3.0 is currently distributed in three formats: (i)
plain text, (ii) plain text with one sentence per line, and (iii) a tagged version (one sentence
per line).
Version CLMET3.1 is the result of making CLMET available in a CQP format for use in
CWB and CQPweb-based corpus environments (Evert & Hardie 2011; Evert 2010a). While
there is no change to the selection of texts, CLMET3.1 includes additions and changes in
linguistic annotation. The changes in CLMET3.1 are of three general types: (a) retokenization
and retagging, (b) fixing of some systematic issues that come with historical data, and (c)
enhancing annotation by adding lemmas and simplified part-of-speech class tags
### Source Data
#### Initial Data Collection and Normalization
The initial data is from OCR of texts in English from 1710-1920
#### Who are the source language producers?
The text was produced by the authors of the original work and then OCRd
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
This dataset does not contain any personal information as these are historic texts. Some content might be sensitive
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
Dealing with historical data, tagging remains problematic in all areas, and should be treated
with caution (especially with noun recognition) and/or combined with more coarse-grained
class queries. Also bear in mind that the lemmas for unknown items are in lower
case, while proper names that the tagger did recognize are not necessarily all lower case. In
addition, lemmatization may not be consistent, e.g. in the area of -ize/ise spellings; these were
not homogenized to preserve as much of the original orthography as possible.
## Additional Information
### Dataset Curators
The Corpus of Late Modern English Texts, version 3.1 (CLMET3.1) has been created by Hendrik De Smet, Susanne Flach, Hans-J�rgen Diller and Jukka Tyrkk�
### Licensing Information
Creative Commons Attribution Non Commercial Share Alike 4.0 International
### Citation Information
[Needs More Information] | biglam/clmet_3_1 | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-label-classification",
"task_ids:masked-language-modeling",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-07-17T22:27:04+00:00 | {"annotations_creators": ["expert-generated", "machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": ["original"], "task_categories": ["text-classification", "fill-mask"], "task_ids": ["multi-label-classification", "masked-language-modeling"], "pretty_name": "Corpus of Late Modern English Texts v3.1"} | 2022-07-18T01:14:38+00:00 |
9baf6183ae9aeecfd261cb36f0d001e90bc77c57 |
# PubLayNet
PubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations. The source of the documents is [PubMed Central Open Access Subset (commercial use collection)](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). The annotations are automatically generated by matching the PDF format and the XML format of the articles in the PubMed Central Open Access Subset. More details are available in our paper ["PubLayNet: largest dataset ever for document layout analysis."](https://arxiv.org/abs/1908.07836).
The public dataset is in tar.gz format which doesn't fit nicely with huggingface streaming. Modifications have been made to optimise the delivery of the dataset for the hugginface datset api. The original files can be found [here](https://developer.ibm.com/exchanges/data/all/publaynet/).
Licence: [Community Data License Agreement – Permissive – Version 1.0 License](https://cdla.dev/permissive-1-0/)
Author: IBM
GitHub: https://github.com/ibm-aur-nlp/PubLayNet
@article{ zhong2019publaynet,
title = { PubLayNet: largest dataset ever for document layout analysis },
author = { Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno },
journal = { arXiv preprint arXiv:1908.07836},
year. = { 2019 }
} | jordanparker6/publaynet | [
"task_categories:image-to-text",
"size_categories:100B<n<1T",
"language:en",
"license:other",
"arxiv:1908.07836",
"region:us"
] | 2022-07-17T22:32:26+00:00 | {"annotations_creators": [], "language": ["en"], "license": "other", "size_categories": ["100B<n<1T"], "source_datasets": [], "task_categories": ["image-to-text"], "task_ids": [], "title": "PubLayNet"} | 2022-07-19T03:20:00+00:00 |
a8a496123b40fa739da2acf9b0dae339d30c7bae |
# Dataset Card for Imagenette
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
### Dataset Summary
A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'tench',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|----------|----:|---------:|
|imagenette| 9469| 3925|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
| frgfm/imagenette | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:apache-2.0",
"region:us"
] | 2022-07-17T23:13:35+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": [], "size_categories": ["1K<n<10K"], "source_datasets": ["extended"], "task_categories": ["image-classification"], "task_ids": [], "paperswithcode_id": "imagenette", "pretty_name": "Imagenette"} | 2022-12-11T22:26:06+00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.