Upload batch 215 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-f407ed-1527355152.md)
Browse files- huggingface_dataset/Dataset_Card/Cohere_miracl-te-queries-22-12.md +152 -0
- huggingface_dataset/Dataset_Card/CyranoB_polarity.md +159 -0
- huggingface_dataset/Dataset_Card/Datatang_3D_Facial_Expressions_Recognition_Data.md +126 -0
- huggingface_dataset/Dataset_Card/MicPie_unpredictable_cluster22.md +250 -0
- huggingface_dataset/Dataset_Card/ai2_arc.md +270 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-f407ed-1527355152.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-adversarial_qa-0243fffc-1303549871.md +35 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-36bd0b51-8375120.md +31 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-samsum-0c52930e-12115616.md +33 -0
- huggingface_dataset/Dataset_Card/babelbox_babelbox_voice.md +145 -0
- huggingface_dataset/Dataset_Card/code_x_glue_cc_cloze_testing_maxmin.md +358 -0
- huggingface_dataset/Dataset_Card/codeparrot_github-jupyter.md +47 -0
- huggingface_dataset/Dataset_Card/fewshot-goes-multilingual_cs_czech-court-decisions-ner.md +82 -0
- huggingface_dataset/Dataset_Card/huggingface_semantic-segmentation-test-sample.md +1 -0
- huggingface_dataset/Dataset_Card/irds_clinicaltrials_2019.md +35 -0
- huggingface_dataset/Dataset_Card/irds_medline_2017_trec-pm-2018.md +49 -0
- huggingface_dataset/Dataset_Card/nchlt.md +399 -0
- huggingface_dataset/Dataset_Card/neuralspace_citizen_nlu.md +166 -0
- huggingface_dataset/Dataset_Card/pragmeval.md +812 -0
- huggingface_dataset/Dataset_Card/qa4pc_QA4PC.md +25 -0
huggingface_dataset/Dataset_Card/Cohere_miracl-te-queries-22-12.md
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| 1 |
+
---
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| 2 |
+
annotations_creators:
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| 3 |
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- expert-generated
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| 4 |
+
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| 5 |
+
language:
|
| 6 |
+
- te
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| 7 |
+
|
| 8 |
+
multilinguality:
|
| 9 |
+
- multilingual
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| 10 |
+
|
| 11 |
+
size_categories: []
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| 12 |
+
source_datasets: []
|
| 13 |
+
tags: []
|
| 14 |
+
|
| 15 |
+
task_categories:
|
| 16 |
+
- text-retrieval
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| 17 |
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| 18 |
+
license:
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| 19 |
+
- apache-2.0
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| 20 |
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| 21 |
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task_ids:
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| 22 |
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- document-retrieval
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| 23 |
+
---
|
| 24 |
+
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| 25 |
+
# MIRACL (te) embedded with cohere.ai `multilingual-22-12` encoder
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| 26 |
+
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| 27 |
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We encoded the [MIRACL dataset](https://huggingface.co/miracl) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
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| 28 |
+
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| 29 |
+
The query embeddings can be found in [Cohere/miracl-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-corpus-22-12).
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| 30 |
+
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For the orginal datasets, see [miracl/miracl](https://huggingface.co/datasets/miracl/miracl) and [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
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| 32 |
+
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| 33 |
+
|
| 34 |
+
Dataset info:
|
| 35 |
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> MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
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+
>
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| 37 |
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> The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
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| 38 |
+
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| 39 |
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## Embeddings
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| 40 |
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We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
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| 42 |
+
|
| 43 |
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## Loading the dataset
|
| 44 |
+
|
| 45 |
+
In [miracl-te-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-te-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
|
| 46 |
+
|
| 47 |
+
You can either load the dataset like this:
|
| 48 |
+
```python
|
| 49 |
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from datasets import load_dataset
|
| 50 |
+
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train")
|
| 51 |
+
```
|
| 52 |
+
|
| 53 |
+
Or you can also stream it without downloading it before:
|
| 54 |
+
```python
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| 55 |
+
from datasets import load_dataset
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| 56 |
+
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train", streaming=True)
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| 57 |
+
|
| 58 |
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for doc in docs:
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| 59 |
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docid = doc['docid']
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| 60 |
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title = doc['title']
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| 61 |
+
text = doc['text']
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| 62 |
+
emb = doc['emb']
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| 63 |
+
```
|
| 64 |
+
|
| 65 |
+
## Search
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| 66 |
+
|
| 67 |
+
Have a look at [miracl-te-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-te-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
|
| 68 |
+
|
| 69 |
+
To search in the documents, you must use **dot-product**.
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| 70 |
+
|
| 71 |
+
|
| 72 |
+
And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
|
| 73 |
+
|
| 74 |
+
A full search example:
|
| 75 |
+
```python
|
| 76 |
+
# Attention! For large datasets, this requires a lot of memory to store
|
| 77 |
+
# all document embeddings and to compute the dot product scores.
|
| 78 |
+
# Only use this for smaller datasets. For large datasets, use a vector DB
|
| 79 |
+
|
| 80 |
+
from datasets import load_dataset
|
| 81 |
+
import torch
|
| 82 |
+
|
| 83 |
+
#Load documents + embeddings
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| 84 |
+
docs = load_dataset(f"Cohere/miracl-te-corpus-22-12", split="train")
|
| 85 |
+
doc_embeddings = torch.tensor(docs['emb'])
|
| 86 |
+
|
| 87 |
+
# Load queries
|
| 88 |
+
queries = load_dataset(f"Cohere/miracl-te-queries-22-12", split="dev")
|
| 89 |
+
|
| 90 |
+
# Select the first query as example
|
| 91 |
+
qid = 0
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| 92 |
+
query = queries[qid]
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| 93 |
+
query_embedding = torch.tensor(queries['emb'])
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| 94 |
+
|
| 95 |
+
# Compute dot score between query embedding and document embeddings
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| 96 |
+
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
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| 97 |
+
top_k = torch.topk(dot_scores, k=3)
|
| 98 |
+
|
| 99 |
+
# Print results
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| 100 |
+
print("Query:", query['query'])
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| 101 |
+
for doc_id in top_k.indices[0].tolist():
|
| 102 |
+
print(docs[doc_id]['title'])
|
| 103 |
+
print(docs[doc_id]['text'])
|
| 104 |
+
```
|
| 105 |
+
|
| 106 |
+
You can get embeddings for new queries using our API:
|
| 107 |
+
```python
|
| 108 |
+
#Run: pip install cohere
|
| 109 |
+
import cohere
|
| 110 |
+
co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
|
| 111 |
+
texts = ['my search query']
|
| 112 |
+
response = co.embed(texts=texts, model='multilingual-22-12')
|
| 113 |
+
query_embedding = response.embeddings[0] # Get the embedding for the first text
|
| 114 |
+
```
|
| 115 |
+
|
| 116 |
+
## Performance
|
| 117 |
+
|
| 118 |
+
In the following table we compare the cohere multilingual-22-12 model with Elasticsearch version 8.6.0 lexical search (title and passage indexed as independent fields). Note that Elasticsearch doesn't support all languages that are part of the MIRACL dataset.
|
| 119 |
+
|
| 120 |
+
|
| 121 |
+
We compute nDCG@10 (a ranking based loss), as well as hit@3: Is at least one relevant document in the top-3 results. We find that hit@3 is easier to interpret, as it presents the number of queries for which a relevant document is found among the top-3 results.
|
| 122 |
+
|
| 123 |
+
Note: MIRACL only annotated a small fraction of passages (10 per query) for relevancy. Especially for larger Wikipedias (like English), we often found many more relevant passages. This is know as annotation holes. Real nDCG@10 and hit@3 performance is likely higher than depicted.
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| 124 |
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|
| 125 |
+
|
| 126 |
+
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 | ES 8.6.0 nDCG@10 | ES 8.6.0 acc@3 |
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| 127 |
+
|---|---|---|---|---|
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| 128 |
+
| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
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| 129 |
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| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
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| 130 |
+
| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
|
| 131 |
+
| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
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| 132 |
+
| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
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| 133 |
+
| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
|
| 134 |
+
| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
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| 135 |
+
| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
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| 136 |
+
| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
|
| 137 |
+
| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
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| 138 |
+
| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
|
| 139 |
+
|
| 140 |
+
Further languages (not supported by Elasticsearch):
|
| 141 |
+
| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
|
| 142 |
+
|---|---|---|
|
| 143 |
+
| miracl-fa | 44.8 | 53.6 |
|
| 144 |
+
| miracl-ja | 49.0 | 61.0 |
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| 145 |
+
| miracl-ko | 50.9 | 64.8 |
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| 146 |
+
| miracl-sw | 61.4 | 74.5 |
|
| 147 |
+
| miracl-te | 67.8 | 72.3 |
|
| 148 |
+
| miracl-th | 60.2 | 71.9 |
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| 149 |
+
| miracl-yo | 56.4 | 62.2 |
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| 150 |
+
| miracl-zh | 43.8 | 56.5 |
|
| 151 |
+
| **Avg** | 54.3 | 64.6 |
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| 152 |
+
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huggingface_dataset/Dataset_Card/CyranoB_polarity.md
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| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language_creators:
|
| 5 |
+
- crowdsourced
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- apache-2.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 1M<n<10M
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- sentiment-classification
|
| 20 |
+
pretty_name: Amazon Review Polarity
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# Dataset Card for Amazon Review Polarity
|
| 24 |
+
|
| 25 |
+
## Table of Contents
|
| 26 |
+
- [Dataset Description](#dataset-description)
|
| 27 |
+
- [Dataset Summary](#dataset-summary)
|
| 28 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 29 |
+
- [Languages](#languages)
|
| 30 |
+
- [Dataset Structure](#dataset-structure)
|
| 31 |
+
- [Data Instances](#data-instances)
|
| 32 |
+
- [Data Fields](#data-fields)
|
| 33 |
+
- [Data Splits](#data-splits)
|
| 34 |
+
- [Dataset Creation](#dataset-creation)
|
| 35 |
+
- [Curation Rationale](#curation-rationale)
|
| 36 |
+
- [Source Data](#source-data)
|
| 37 |
+
- [Annotations](#annotations)
|
| 38 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 39 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 40 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 41 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 42 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 43 |
+
- [Additional Information](#additional-information)
|
| 44 |
+
- [Dataset Curators](#dataset-curators)
|
| 45 |
+
- [Licensing Information](#licensing-information)
|
| 46 |
+
- [Citation Information](#citation-information)
|
| 47 |
+
- [Contributions](#contributions)
|
| 48 |
+
|
| 49 |
+
## Dataset Description
|
| 50 |
+
|
| 51 |
+
- **Homepage:** https://registry.opendata.aws/
|
| 52 |
+
- **Repository:** https://github.com/zhangxiangxiao/Crepe
|
| 53 |
+
- **Paper:** https://arxiv.org/abs/1509.01626
|
| 54 |
+
- **Leaderboard:** [Needs More Information]
|
| 55 |
+
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
|
| 56 |
+
|
| 57 |
+
### Dataset Summary
|
| 58 |
+
|
| 59 |
+
The Amazon reviews dataset consists of reviews from amazon.
|
| 60 |
+
The data span a period of 18 years, including ~35 million reviews up to March 2013.
|
| 61 |
+
Reviews include product and user information, ratings, and a plaintext review.
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
Mainly English.
|
| 70 |
+
|
| 71 |
+
## Dataset Structure
|
| 72 |
+
|
| 73 |
+
### Data Instances
|
| 74 |
+
|
| 75 |
+
A typical data point, comprises of a title, a content and the corresponding label.
|
| 76 |
+
|
| 77 |
+
An example from the AmazonPolarity test set looks as follows:
|
| 78 |
+
|
| 79 |
+
```
|
| 80 |
+
{
|
| 81 |
+
'title':'Great CD',
|
| 82 |
+
'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
|
| 83 |
+
'label':1
|
| 84 |
+
}
|
| 85 |
+
```
|
| 86 |
+
|
| 87 |
+
### Data Fields
|
| 88 |
+
|
| 89 |
+
- 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
|
| 90 |
+
- 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
|
| 91 |
+
- 'label': either 1 (positive) or 0 (negative) rating.
|
| 92 |
+
|
| 93 |
+
### Data Splits
|
| 94 |
+
|
| 95 |
+
The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.
|
| 96 |
+
|
| 97 |
+
## Dataset Creation
|
| 98 |
+
|
| 99 |
+
### Curation Rationale
|
| 100 |
+
|
| 101 |
+
The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
|
| 102 |
+
|
| 103 |
+
### Source Data
|
| 104 |
+
|
| 105 |
+
#### Initial Data Collection and Normalization
|
| 106 |
+
|
| 107 |
+
[Needs More Information]
|
| 108 |
+
|
| 109 |
+
#### Who are the source language producers?
|
| 110 |
+
|
| 111 |
+
[Needs More Information]
|
| 112 |
+
|
| 113 |
+
### Annotations
|
| 114 |
+
|
| 115 |
+
#### Annotation process
|
| 116 |
+
|
| 117 |
+
[Needs More Information]
|
| 118 |
+
|
| 119 |
+
#### Who are the annotators?
|
| 120 |
+
|
| 121 |
+
[Needs More Information]
|
| 122 |
+
|
| 123 |
+
### Personal and Sensitive Information
|
| 124 |
+
|
| 125 |
+
[Needs More Information]
|
| 126 |
+
|
| 127 |
+
## Considerations for Using the Data
|
| 128 |
+
|
| 129 |
+
### Social Impact of Dataset
|
| 130 |
+
|
| 131 |
+
[Needs More Information]
|
| 132 |
+
|
| 133 |
+
### Discussion of Biases
|
| 134 |
+
|
| 135 |
+
[Needs More Information]
|
| 136 |
+
|
| 137 |
+
### Other Known Limitations
|
| 138 |
+
|
| 139 |
+
[Needs More Information]
|
| 140 |
+
|
| 141 |
+
## Additional Information
|
| 142 |
+
|
| 143 |
+
### Dataset Curators
|
| 144 |
+
|
| 145 |
+
[Needs More Information]
|
| 146 |
+
|
| 147 |
+
### Licensing Information
|
| 148 |
+
|
| 149 |
+
Apache License 2.0
|
| 150 |
+
|
| 151 |
+
### Citation Information
|
| 152 |
+
|
| 153 |
+
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
|
| 154 |
+
|
| 155 |
+
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
|
| 156 |
+
|
| 157 |
+
### Contributions
|
| 158 |
+
|
| 159 |
+
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset.
|
huggingface_dataset/Dataset_Card/Datatang_3D_Facial_Expressions_Recognition_Data.md
ADDED
|
@@ -0,0 +1,126 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
YAML tags:
|
| 3 |
+
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
|
| 4 |
+
---
|
| 5 |
+
|
| 6 |
+
# Dataset Card for Datatang/3D_Facial_Expressions_Recognition_Data
|
| 7 |
+
|
| 8 |
+
## Table of Contents
|
| 9 |
+
- [Table of Contents](#table-of-contents)
|
| 10 |
+
- [Dataset Description](#dataset-description)
|
| 11 |
+
- [Dataset Summary](#dataset-summary)
|
| 12 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 13 |
+
- [Languages](#languages)
|
| 14 |
+
- [Dataset Structure](#dataset-structure)
|
| 15 |
+
- [Data Instances](#data-instances)
|
| 16 |
+
- [Data Fields](#data-fields)
|
| 17 |
+
- [Data Splits](#data-splits)
|
| 18 |
+
- [Dataset Creation](#dataset-creation)
|
| 19 |
+
- [Curation Rationale](#curation-rationale)
|
| 20 |
+
- [Source Data](#source-data)
|
| 21 |
+
- [Annotations](#annotations)
|
| 22 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 23 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 24 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 25 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 26 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 27 |
+
- [Additional Information](#additional-information)
|
| 28 |
+
- [Dataset Curators](#dataset-curators)
|
| 29 |
+
- [Licensing Information](#licensing-information)
|
| 30 |
+
- [Citation Information](#citation-information)
|
| 31 |
+
- [Contributions](#contributions)
|
| 32 |
+
|
| 33 |
+
## Dataset Description
|
| 34 |
+
|
| 35 |
+
- **Homepage:** https://bit.ly/3xZlC5A
|
| 36 |
+
- **Repository:**
|
| 37 |
+
- **Paper:**
|
| 38 |
+
- **Leaderboard:**
|
| 39 |
+
- **Point of Contact:**
|
| 40 |
+
|
| 41 |
+
### Dataset Summary
|
| 42 |
+
|
| 43 |
+
4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.
|
| 44 |
+
|
| 45 |
+
For more details, please refer to the link: https://bit.ly/3xZlC5A
|
| 46 |
+
|
| 47 |
+
### Supported Tasks and Leaderboards
|
| 48 |
+
|
| 49 |
+
face-detection, computer-vision: The dataset can be used to train a model for face detection.
|
| 50 |
+
|
| 51 |
+
### Languages
|
| 52 |
+
English
|
| 53 |
+
|
| 54 |
+
## Dataset Structure
|
| 55 |
+
|
| 56 |
+
### Data Instances
|
| 57 |
+
|
| 58 |
+
[More Information Needed]
|
| 59 |
+
|
| 60 |
+
### Data Fields
|
| 61 |
+
|
| 62 |
+
[More Information Needed]
|
| 63 |
+
|
| 64 |
+
### Data Splits
|
| 65 |
+
|
| 66 |
+
[More Information Needed]
|
| 67 |
+
|
| 68 |
+
## Dataset Creation
|
| 69 |
+
|
| 70 |
+
### Curation Rationale
|
| 71 |
+
|
| 72 |
+
[More Information Needed]
|
| 73 |
+
|
| 74 |
+
### Source Data
|
| 75 |
+
|
| 76 |
+
#### Initial Data Collection and Normalization
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
#### Who are the source language producers?
|
| 81 |
+
|
| 82 |
+
[More Information Needed]
|
| 83 |
+
|
| 84 |
+
### Annotations
|
| 85 |
+
|
| 86 |
+
#### Annotation process
|
| 87 |
+
|
| 88 |
+
[More Information Needed]
|
| 89 |
+
|
| 90 |
+
#### Who are the annotators?
|
| 91 |
+
|
| 92 |
+
[More Information Needed]
|
| 93 |
+
|
| 94 |
+
### Personal and Sensitive Information
|
| 95 |
+
|
| 96 |
+
[More Information Needed]
|
| 97 |
+
|
| 98 |
+
## Considerations for Using the Data
|
| 99 |
+
|
| 100 |
+
### Social Impact of Dataset
|
| 101 |
+
|
| 102 |
+
[More Information Needed]
|
| 103 |
+
|
| 104 |
+
### Discussion of Biases
|
| 105 |
+
|
| 106 |
+
[More Information Needed]
|
| 107 |
+
|
| 108 |
+
### Other Known Limitations
|
| 109 |
+
|
| 110 |
+
[More Information Needed]
|
| 111 |
+
|
| 112 |
+
## Additional Information
|
| 113 |
+
|
| 114 |
+
### Dataset Curators
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
### Licensing Information
|
| 119 |
+
|
| 120 |
+
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
|
| 121 |
+
|
| 122 |
+
### Citation Information
|
| 123 |
+
|
| 124 |
+
[More Information Needed]
|
| 125 |
+
|
| 126 |
+
### Contributions
|
huggingface_dataset/Dataset_Card/MicPie_unpredictable_cluster22.md
ADDED
|
@@ -0,0 +1,250 @@
|
|
|
|
|
|
|
|
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|
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|
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- no-annotation
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- apache-2.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: UnpredicTable-cluster22
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100K<n<1M
|
| 15 |
+
source_datasets: []
|
| 16 |
+
task_categories:
|
| 17 |
+
- multiple-choice
|
| 18 |
+
- question-answering
|
| 19 |
+
- zero-shot-classification
|
| 20 |
+
- text2text-generation
|
| 21 |
+
- table-question-answering
|
| 22 |
+
- text-generation
|
| 23 |
+
- text-classification
|
| 24 |
+
- tabular-classification
|
| 25 |
+
task_ids:
|
| 26 |
+
- multiple-choice-qa
|
| 27 |
+
- extractive-qa
|
| 28 |
+
- open-domain-qa
|
| 29 |
+
- closed-domain-qa
|
| 30 |
+
- closed-book-qa
|
| 31 |
+
- open-book-qa
|
| 32 |
+
- language-modeling
|
| 33 |
+
- multi-class-classification
|
| 34 |
+
- natural-language-inference
|
| 35 |
+
- topic-classification
|
| 36 |
+
- multi-label-classification
|
| 37 |
+
- tabular-multi-class-classification
|
| 38 |
+
- tabular-multi-label-classification
|
| 39 |
+
---
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
# Dataset Card for "UnpredicTable-cluster22" - Dataset of Few-shot Tasks from Tables
|
| 43 |
+
|
| 44 |
+
## Table of Contents
|
| 45 |
+
- [Dataset Description](#dataset-description)
|
| 46 |
+
- [Dataset Summary](#dataset-summary)
|
| 47 |
+
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
| 48 |
+
- [Languages](#languages)
|
| 49 |
+
- [Dataset Structure](#dataset-structure)
|
| 50 |
+
- [Data Instances](#data-instances)
|
| 51 |
+
- [Data Fields](#data-instances)
|
| 52 |
+
- [Data Splits](#data-instances)
|
| 53 |
+
- [Dataset Creation](#dataset-creation)
|
| 54 |
+
- [Curation Rationale](#curation-rationale)
|
| 55 |
+
- [Source Data](#source-data)
|
| 56 |
+
- [Annotations](#annotations)
|
| 57 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 58 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 59 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 60 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 61 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 62 |
+
- [Additional Information](#additional-information)
|
| 63 |
+
- [Dataset Curators](#dataset-curators)
|
| 64 |
+
- [Licensing Information](#licensing-information)
|
| 65 |
+
- [Citation Information](#citation-information)
|
| 66 |
+
|
| 67 |
+
## Dataset Description
|
| 68 |
+
|
| 69 |
+
- **Homepage:** https://ethanperez.net/unpredictable
|
| 70 |
+
- **Repository:** https://github.com/JunShern/few-shot-adaptation
|
| 71 |
+
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
|
| 72 |
+
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
|
| 73 |
+
|
| 74 |
+
### Dataset Summary
|
| 75 |
+
|
| 76 |
+
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
|
| 77 |
+
|
| 78 |
+
There are several dataset versions available:
|
| 79 |
+
|
| 80 |
+
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
|
| 81 |
+
|
| 82 |
+
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
|
| 83 |
+
|
| 84 |
+
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
|
| 85 |
+
|
| 86 |
+
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
|
| 87 |
+
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
|
| 88 |
+
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
|
| 89 |
+
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
|
| 90 |
+
|
| 91 |
+
* UnpredicTable data subsets based on the website of origin:
|
| 92 |
+
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
|
| 93 |
+
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
|
| 94 |
+
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
|
| 95 |
+
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
|
| 96 |
+
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
|
| 97 |
+
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
|
| 98 |
+
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
|
| 99 |
+
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
|
| 100 |
+
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
|
| 101 |
+
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
|
| 102 |
+
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
|
| 103 |
+
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
|
| 104 |
+
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
|
| 105 |
+
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
|
| 106 |
+
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
|
| 107 |
+
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
|
| 108 |
+
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
|
| 109 |
+
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
|
| 110 |
+
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
|
| 111 |
+
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
|
| 115 |
+
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
|
| 116 |
+
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
|
| 117 |
+
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
|
| 118 |
+
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
|
| 119 |
+
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
|
| 120 |
+
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
|
| 121 |
+
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
|
| 122 |
+
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
|
| 123 |
+
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
|
| 124 |
+
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
|
| 125 |
+
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
|
| 126 |
+
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
|
| 127 |
+
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
|
| 128 |
+
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
|
| 129 |
+
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
|
| 130 |
+
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
|
| 131 |
+
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
|
| 132 |
+
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
|
| 133 |
+
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
|
| 134 |
+
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
|
| 135 |
+
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
|
| 136 |
+
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
|
| 137 |
+
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
|
| 138 |
+
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
|
| 139 |
+
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
|
| 140 |
+
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
|
| 141 |
+
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
|
| 142 |
+
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
|
| 143 |
+
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
|
| 144 |
+
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
|
| 145 |
+
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
|
| 146 |
+
|
| 147 |
+
### Supported Tasks and Leaderboards
|
| 148 |
+
|
| 149 |
+
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
|
| 150 |
+
|
| 151 |
+
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
|
| 152 |
+
|
| 153 |
+
### Languages
|
| 154 |
+
|
| 155 |
+
English
|
| 156 |
+
|
| 157 |
+
## Dataset Structure
|
| 158 |
+
|
| 159 |
+
### Data Instances
|
| 160 |
+
|
| 161 |
+
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
|
| 162 |
+
|
| 163 |
+
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
|
| 164 |
+
|
| 165 |
+
### Data Fields
|
| 166 |
+
|
| 167 |
+
'task': task identifier
|
| 168 |
+
|
| 169 |
+
'input': column elements of a specific row in the table.
|
| 170 |
+
|
| 171 |
+
'options': for multiple choice classification, it provides the options to choose from.
|
| 172 |
+
|
| 173 |
+
'output': target column element of the same row as input.
|
| 174 |
+
|
| 175 |
+
'pageTitle': the title of the page containing the table.
|
| 176 |
+
|
| 177 |
+
'outputColName': output column name
|
| 178 |
+
|
| 179 |
+
'url': url to the website containing the table
|
| 180 |
+
|
| 181 |
+
'wdcFile': WDC Web Table Corpus file
|
| 182 |
+
|
| 183 |
+
### Data Splits
|
| 184 |
+
|
| 185 |
+
The UnpredicTable datasets do not come with additional data splits.
|
| 186 |
+
|
| 187 |
+
## Dataset Creation
|
| 188 |
+
|
| 189 |
+
### Curation Rationale
|
| 190 |
+
|
| 191 |
+
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
|
| 192 |
+
|
| 193 |
+
### Source Data
|
| 194 |
+
|
| 195 |
+
#### Initial Data Collection and Normalization
|
| 196 |
+
|
| 197 |
+
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
|
| 198 |
+
|
| 199 |
+
#### Who are the source language producers?
|
| 200 |
+
|
| 201 |
+
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
|
| 202 |
+
|
| 203 |
+
### Annotations
|
| 204 |
+
|
| 205 |
+
#### Annotation process
|
| 206 |
+
|
| 207 |
+
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
|
| 208 |
+
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
|
| 209 |
+
|
| 210 |
+
#### Who are the annotators?
|
| 211 |
+
|
| 212 |
+
Annotations were carried out by a lab assistant.
|
| 213 |
+
|
| 214 |
+
### Personal and Sensitive Information
|
| 215 |
+
|
| 216 |
+
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
|
| 217 |
+
|
| 218 |
+
## Considerations for Using the Data
|
| 219 |
+
|
| 220 |
+
### Social Impact of Dataset
|
| 221 |
+
|
| 222 |
+
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
|
| 223 |
+
|
| 224 |
+
### Discussion of Biases
|
| 225 |
+
|
| 226 |
+
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
|
| 227 |
+
|
| 228 |
+
### Other Known Limitations
|
| 229 |
+
|
| 230 |
+
No additional known limitations.
|
| 231 |
+
|
| 232 |
+
## Additional Information
|
| 233 |
+
|
| 234 |
+
### Dataset Curators
|
| 235 |
+
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
|
| 236 |
+
|
| 237 |
+
### Licensing Information
|
| 238 |
+
Apache 2.0
|
| 239 |
+
|
| 240 |
+
### Citation Information
|
| 241 |
+
|
| 242 |
+
```
|
| 243 |
+
@misc{chan2022few,
|
| 244 |
+
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
|
| 245 |
+
title = {Few-shot Adaptation Works with UnpredicTable Data},
|
| 246 |
+
publisher={arXiv},
|
| 247 |
+
year = {2022},
|
| 248 |
+
url = {https://arxiv.org/abs/2208.01009}
|
| 249 |
+
}
|
| 250 |
+
```
|
huggingface_dataset/Dataset_Card/ai2_arc.md
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|
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|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
language_bcp47:
|
| 9 |
+
- en-US
|
| 10 |
+
license:
|
| 11 |
+
- cc-by-sa-4.0
|
| 12 |
+
multilinguality:
|
| 13 |
+
- monolingual
|
| 14 |
+
size_categories:
|
| 15 |
+
- 1K<n<10K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
task_categories:
|
| 19 |
+
- question-answering
|
| 20 |
+
task_ids:
|
| 21 |
+
- open-domain-qa
|
| 22 |
+
- multiple-choice-qa
|
| 23 |
+
paperswithcode_id: null
|
| 24 |
+
pretty_name: Ai2Arc
|
| 25 |
+
dataset_info:
|
| 26 |
+
- config_name: ARC-Challenge
|
| 27 |
+
features:
|
| 28 |
+
- name: id
|
| 29 |
+
dtype: string
|
| 30 |
+
- name: question
|
| 31 |
+
dtype: string
|
| 32 |
+
- name: choices
|
| 33 |
+
sequence:
|
| 34 |
+
- name: text
|
| 35 |
+
dtype: string
|
| 36 |
+
- name: label
|
| 37 |
+
dtype: string
|
| 38 |
+
- name: answerKey
|
| 39 |
+
dtype: string
|
| 40 |
+
splits:
|
| 41 |
+
- name: train
|
| 42 |
+
num_bytes: 351888
|
| 43 |
+
num_examples: 1119
|
| 44 |
+
- name: test
|
| 45 |
+
num_bytes: 377740
|
| 46 |
+
num_examples: 1172
|
| 47 |
+
- name: validation
|
| 48 |
+
num_bytes: 97254
|
| 49 |
+
num_examples: 299
|
| 50 |
+
download_size: 680841265
|
| 51 |
+
dataset_size: 826882
|
| 52 |
+
- config_name: ARC-Easy
|
| 53 |
+
features:
|
| 54 |
+
- name: id
|
| 55 |
+
dtype: string
|
| 56 |
+
- name: question
|
| 57 |
+
dtype: string
|
| 58 |
+
- name: choices
|
| 59 |
+
sequence:
|
| 60 |
+
- name: text
|
| 61 |
+
dtype: string
|
| 62 |
+
- name: label
|
| 63 |
+
dtype: string
|
| 64 |
+
- name: answerKey
|
| 65 |
+
dtype: string
|
| 66 |
+
splits:
|
| 67 |
+
- name: train
|
| 68 |
+
num_bytes: 623254
|
| 69 |
+
num_examples: 2251
|
| 70 |
+
- name: test
|
| 71 |
+
num_bytes: 661997
|
| 72 |
+
num_examples: 2376
|
| 73 |
+
- name: validation
|
| 74 |
+
num_bytes: 158498
|
| 75 |
+
num_examples: 570
|
| 76 |
+
download_size: 680841265
|
| 77 |
+
dataset_size: 1443749
|
| 78 |
+
---
|
| 79 |
+
|
| 80 |
+
# Dataset Card for "ai2_arc"
|
| 81 |
+
|
| 82 |
+
## Table of Contents
|
| 83 |
+
- [Dataset Description](#dataset-description)
|
| 84 |
+
- [Dataset Summary](#dataset-summary)
|
| 85 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 86 |
+
- [Languages](#languages)
|
| 87 |
+
- [Dataset Structure](#dataset-structure)
|
| 88 |
+
- [Data Instances](#data-instances)
|
| 89 |
+
- [Data Fields](#data-fields)
|
| 90 |
+
- [Data Splits](#data-splits)
|
| 91 |
+
- [Dataset Creation](#dataset-creation)
|
| 92 |
+
- [Curation Rationale](#curation-rationale)
|
| 93 |
+
- [Source Data](#source-data)
|
| 94 |
+
- [Annotations](#annotations)
|
| 95 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 96 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 97 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 98 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 99 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 100 |
+
- [Additional Information](#additional-information)
|
| 101 |
+
- [Dataset Curators](#dataset-curators)
|
| 102 |
+
- [Licensing Information](#licensing-information)
|
| 103 |
+
- [Citation Information](#citation-information)
|
| 104 |
+
- [Contributions](#contributions)
|
| 105 |
+
|
| 106 |
+
## Dataset Description
|
| 107 |
+
|
| 108 |
+
- **Homepage:** [https://allenai.org/data/arc](https://allenai.org/data/arc)
|
| 109 |
+
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 110 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 111 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 112 |
+
- **Size of downloaded dataset files:** 1298.60 MB
|
| 113 |
+
- **Size of the generated dataset:** 2.17 MB
|
| 114 |
+
- **Total amount of disk used:** 1300.77 MB
|
| 115 |
+
|
| 116 |
+
### Dataset Summary
|
| 117 |
+
|
| 118 |
+
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
|
| 119 |
+
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
|
| 120 |
+
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
|
| 121 |
+
including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
|
| 122 |
+
|
| 123 |
+
### Supported Tasks and Leaderboards
|
| 124 |
+
|
| 125 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 126 |
+
|
| 127 |
+
### Languages
|
| 128 |
+
|
| 129 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 130 |
+
|
| 131 |
+
## Dataset Structure
|
| 132 |
+
|
| 133 |
+
### Data Instances
|
| 134 |
+
|
| 135 |
+
#### ARC-Challenge
|
| 136 |
+
|
| 137 |
+
- **Size of downloaded dataset files:** 649.30 MB
|
| 138 |
+
- **Size of the generated dataset:** 0.79 MB
|
| 139 |
+
- **Total amount of disk used:** 650.09 MB
|
| 140 |
+
|
| 141 |
+
An example of 'train' looks as follows.
|
| 142 |
+
```
|
| 143 |
+
{
|
| 144 |
+
"answerKey": "B",
|
| 145 |
+
"choices": {
|
| 146 |
+
"label": ["A", "B", "C", "D"],
|
| 147 |
+
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
|
| 148 |
+
},
|
| 149 |
+
"id": "Mercury_SC_405487",
|
| 150 |
+
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
|
| 151 |
+
}
|
| 152 |
+
```
|
| 153 |
+
|
| 154 |
+
#### ARC-Easy
|
| 155 |
+
|
| 156 |
+
- **Size of downloaded dataset files:** 649.30 MB
|
| 157 |
+
- **Size of the generated dataset:** 1.38 MB
|
| 158 |
+
- **Total amount of disk used:** 650.68 MB
|
| 159 |
+
|
| 160 |
+
An example of 'train' looks as follows.
|
| 161 |
+
```
|
| 162 |
+
{
|
| 163 |
+
"answerKey": "B",
|
| 164 |
+
"choices": {
|
| 165 |
+
"label": ["A", "B", "C", "D"],
|
| 166 |
+
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
|
| 167 |
+
},
|
| 168 |
+
"id": "Mercury_SC_405487",
|
| 169 |
+
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
|
| 170 |
+
}
|
| 171 |
+
```
|
| 172 |
+
|
| 173 |
+
### Data Fields
|
| 174 |
+
|
| 175 |
+
The data fields are the same among all splits.
|
| 176 |
+
|
| 177 |
+
#### ARC-Challenge
|
| 178 |
+
- `id`: a `string` feature.
|
| 179 |
+
- `question`: a `string` feature.
|
| 180 |
+
- `choices`: a dictionary feature containing:
|
| 181 |
+
- `text`: a `string` feature.
|
| 182 |
+
- `label`: a `string` feature.
|
| 183 |
+
- `answerKey`: a `string` feature.
|
| 184 |
+
|
| 185 |
+
#### ARC-Easy
|
| 186 |
+
- `id`: a `string` feature.
|
| 187 |
+
- `question`: a `string` feature.
|
| 188 |
+
- `choices`: a dictionary feature containing:
|
| 189 |
+
- `text`: a `string` feature.
|
| 190 |
+
- `label`: a `string` feature.
|
| 191 |
+
- `answerKey`: a `string` feature.
|
| 192 |
+
|
| 193 |
+
### Data Splits
|
| 194 |
+
|
| 195 |
+
| name |train|validation|test|
|
| 196 |
+
|-------------|----:|---------:|---:|
|
| 197 |
+
|ARC-Challenge| 1119| 299|1172|
|
| 198 |
+
|ARC-Easy | 2251| 570|2376|
|
| 199 |
+
|
| 200 |
+
## Dataset Creation
|
| 201 |
+
|
| 202 |
+
### Curation Rationale
|
| 203 |
+
|
| 204 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 205 |
+
|
| 206 |
+
### Source Data
|
| 207 |
+
|
| 208 |
+
#### Initial Data Collection and Normalization
|
| 209 |
+
|
| 210 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 211 |
+
|
| 212 |
+
#### Who are the source language producers?
|
| 213 |
+
|
| 214 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 215 |
+
|
| 216 |
+
### Annotations
|
| 217 |
+
|
| 218 |
+
#### Annotation process
|
| 219 |
+
|
| 220 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 221 |
+
|
| 222 |
+
#### Who are the annotators?
|
| 223 |
+
|
| 224 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 225 |
+
|
| 226 |
+
### Personal and Sensitive Information
|
| 227 |
+
|
| 228 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 229 |
+
|
| 230 |
+
## Considerations for Using the Data
|
| 231 |
+
|
| 232 |
+
### Social Impact of Dataset
|
| 233 |
+
|
| 234 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 235 |
+
|
| 236 |
+
### Discussion of Biases
|
| 237 |
+
|
| 238 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 239 |
+
|
| 240 |
+
### Other Known Limitations
|
| 241 |
+
|
| 242 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 243 |
+
|
| 244 |
+
## Additional Information
|
| 245 |
+
|
| 246 |
+
### Dataset Curators
|
| 247 |
+
|
| 248 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 249 |
+
|
| 250 |
+
### Licensing Information
|
| 251 |
+
|
| 252 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 253 |
+
|
| 254 |
+
### Citation Information
|
| 255 |
+
|
| 256 |
+
```
|
| 257 |
+
@article{allenai:arc,
|
| 258 |
+
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
|
| 259 |
+
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
|
| 260 |
+
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
|
| 261 |
+
journal = {arXiv:1803.05457v1},
|
| 262 |
+
year = {2018},
|
| 263 |
+
}
|
| 264 |
+
|
| 265 |
+
```
|
| 266 |
+
|
| 267 |
+
|
| 268 |
+
### Contributions
|
| 269 |
+
|
| 270 |
+
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_dev-mathemakitte-f407ed-1527355152.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- mathemakitten/winobias_antistereotype_dev
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: autoevaluate/zero-shot-classification
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: mathemakitten/winobias_antistereotype_dev
|
| 13 |
+
dataset_config: mathemakitten--winobias_antistereotype_dev
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: autoevaluate/zero-shot-classification
|
| 26 |
+
* Dataset: mathemakitten/winobias_antistereotype_dev
|
| 27 |
+
* Config: mathemakitten--winobias_antistereotype_dev
|
| 28 |
+
* Split: validation
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-adversarial_qa-0243fffc-1303549871.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- adversarial_qa
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: nbroad/rob-base-superqa2
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: adversarial_qa
|
| 13 |
+
dataset_config: adversarialQA
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
context: context
|
| 17 |
+
question: question
|
| 18 |
+
answers-text: answers.text
|
| 19 |
+
answers-answer_start: answers.answer_start
|
| 20 |
+
---
|
| 21 |
+
# Dataset Card for AutoTrain Evaluator
|
| 22 |
+
|
| 23 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 24 |
+
|
| 25 |
+
* Task: Question Answering
|
| 26 |
+
* Model: nbroad/rob-base-superqa2
|
| 27 |
+
* Dataset: adversarial_qa
|
| 28 |
+
* Config: adversarialQA
|
| 29 |
+
* Split: validation
|
| 30 |
+
|
| 31 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 32 |
+
|
| 33 |
+
## Contributions
|
| 34 |
+
|
| 35 |
+
Thanks to [@nbroad](https://huggingface.co/nbroad) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-36bd0b51-8375120.md
ADDED
|
@@ -0,0 +1,31 @@
|
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|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- scientific_papers
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: google/bigbird-pegasus-large-pubmed
|
| 11 |
+
metrics: ['bertscore', 'meteor']
|
| 12 |
+
dataset_name: scientific_papers
|
| 13 |
+
dataset_config: pubmed
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: article
|
| 17 |
+
target: abstract
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: google/bigbird-pegasus-large-pubmed
|
| 25 |
+
* Dataset: scientific_papers
|
| 26 |
+
|
| 27 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 28 |
+
|
| 29 |
+
## Contributions
|
| 30 |
+
|
| 31 |
+
Thanks to [@Blaise_g](https://huggingface.co/Blaise_g) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-samsum-0c52930e-12115616.md
ADDED
|
@@ -0,0 +1,33 @@
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- samsum
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: samsum
|
| 13 |
+
dataset_config: samsum
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: dialogue
|
| 17 |
+
target: summary
|
| 18 |
+
---
|
| 19 |
+
# Dataset Card for AutoTrain Evaluator
|
| 20 |
+
|
| 21 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 22 |
+
|
| 23 |
+
* Task: Summarization
|
| 24 |
+
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP9
|
| 25 |
+
* Dataset: samsum
|
| 26 |
+
* Config: samsum
|
| 27 |
+
* Split: test
|
| 28 |
+
|
| 29 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 30 |
+
|
| 31 |
+
## Contributions
|
| 32 |
+
|
| 33 |
+
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
|
huggingface_dataset/Dataset_Card/babelbox_babelbox_voice.md
ADDED
|
@@ -0,0 +1,145 @@
|
|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language:
|
| 5 |
+
- sv
|
| 6 |
+
language_creators:
|
| 7 |
+
- crowdsourced
|
| 8 |
+
license:
|
| 9 |
+
- cc0-1.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Babelbox Voice
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100K<n<1M
|
| 15 |
+
source_datasets: []
|
| 16 |
+
tags:
|
| 17 |
+
- NST
|
| 18 |
+
task_categories:
|
| 19 |
+
- automatic-speech-recognition
|
| 20 |
+
task_ids: []
|
| 21 |
+
---
|
| 22 |
+
|
| 23 |
+
# Dataset Card for Babelbox Voice
|
| 24 |
+
|
| 25 |
+
## Table of Contents
|
| 26 |
+
- [Table of Contents](#table-of-contents)
|
| 27 |
+
- [Dataset Description](#dataset-description)
|
| 28 |
+
- [Dataset Summary](#dataset-summary)
|
| 29 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 30 |
+
- [Languages](#languages)
|
| 31 |
+
- [Dataset Structure](#dataset-structure)
|
| 32 |
+
- [Data Instances](#data-instances)
|
| 33 |
+
- [Data Fields](#data-fields)
|
| 34 |
+
- [Data Splits](#data-splits)
|
| 35 |
+
- [Dataset Creation](#dataset-creation)
|
| 36 |
+
- [Curation Rationale](#curation-rationale)
|
| 37 |
+
- [Source Data](#source-data)
|
| 38 |
+
- [Annotations](#annotations)
|
| 39 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 40 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 41 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 42 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 43 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 44 |
+
- [Additional Information](#additional-information)
|
| 45 |
+
- [Dataset Curators](#dataset-curators)
|
| 46 |
+
- [Licensing Information](#licensing-information)
|
| 47 |
+
- [Citation Information](#citation-information)
|
| 48 |
+
- [Contributions](#contributions)
|
| 49 |
+
|
| 50 |
+
## Dataset Description
|
| 51 |
+
|
| 52 |
+
- **Homepage:**
|
| 53 |
+
- **Repository:**
|
| 54 |
+
- **Paper:**
|
| 55 |
+
- **Leaderboard:**
|
| 56 |
+
- **Point of Contact:**
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish.
|
| 61 |
+
It is redistributed as a Hugging Face dataset for convienience.
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed]
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
Swedish
|
| 70 |
+
|
| 71 |
+
## Dataset Structure
|
| 72 |
+
|
| 73 |
+
### Data Instances
|
| 74 |
+
|
| 75 |
+
[More Information Needed]
|
| 76 |
+
|
| 77 |
+
### Data Fields
|
| 78 |
+
|
| 79 |
+
[More Information Needed]
|
| 80 |
+
|
| 81 |
+
### Data Splits
|
| 82 |
+
|
| 83 |
+
[More Information Needed]
|
| 84 |
+
|
| 85 |
+
## Dataset Creation
|
| 86 |
+
|
| 87 |
+
### Curation Rationale
|
| 88 |
+
|
| 89 |
+
[More Information Needed]
|
| 90 |
+
|
| 91 |
+
### Source Data
|
| 92 |
+
|
| 93 |
+
#### Initial Data Collection and Normalization
|
| 94 |
+
|
| 95 |
+
[More Information Needed]
|
| 96 |
+
|
| 97 |
+
#### Who are the source language producers?
|
| 98 |
+
|
| 99 |
+
[More Information Needed]
|
| 100 |
+
|
| 101 |
+
### Annotations
|
| 102 |
+
|
| 103 |
+
#### Annotation process
|
| 104 |
+
|
| 105 |
+
[More Information Needed]
|
| 106 |
+
|
| 107 |
+
#### Who are the annotators?
|
| 108 |
+
|
| 109 |
+
[More Information Needed]
|
| 110 |
+
|
| 111 |
+
### Personal and Sensitive Information
|
| 112 |
+
|
| 113 |
+
[More Information Needed]
|
| 114 |
+
|
| 115 |
+
## Considerations for Using the Data
|
| 116 |
+
|
| 117 |
+
### Social Impact of Dataset
|
| 118 |
+
|
| 119 |
+
[More Information Needed]
|
| 120 |
+
|
| 121 |
+
### Discussion of Biases
|
| 122 |
+
|
| 123 |
+
[More Information Needed]
|
| 124 |
+
|
| 125 |
+
### Other Known Limitations
|
| 126 |
+
|
| 127 |
+
[More Information Needed]
|
| 128 |
+
|
| 129 |
+
## Additional Information
|
| 130 |
+
|
| 131 |
+
### Dataset Curators
|
| 132 |
+
|
| 133 |
+
[More Information Needed]
|
| 134 |
+
|
| 135 |
+
### Licensing Information
|
| 136 |
+
|
| 137 |
+
[More Information Needed]
|
| 138 |
+
|
| 139 |
+
### Citation Information
|
| 140 |
+
|
| 141 |
+
[More Information Needed]
|
| 142 |
+
|
| 143 |
+
### Contributions
|
| 144 |
+
|
| 145 |
+
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
|
huggingface_dataset/Dataset_Card/code_x_glue_cc_cloze_testing_maxmin.md
ADDED
|
@@ -0,0 +1,358 @@
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- code
|
| 8 |
+
license:
|
| 9 |
+
- c-uda
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
- 1K<n<10K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-generation
|
| 19 |
+
- fill-mask
|
| 20 |
+
task_ids:
|
| 21 |
+
- slot-filling
|
| 22 |
+
pretty_name: CodeXGlueCcClozeTestingMaxmin
|
| 23 |
+
configs:
|
| 24 |
+
- go
|
| 25 |
+
- java
|
| 26 |
+
- javascript
|
| 27 |
+
- php
|
| 28 |
+
- python
|
| 29 |
+
- ruby
|
| 30 |
+
dataset_info:
|
| 31 |
+
- config_name: go
|
| 32 |
+
features:
|
| 33 |
+
- name: id
|
| 34 |
+
dtype: int32
|
| 35 |
+
- name: idx
|
| 36 |
+
dtype: string
|
| 37 |
+
- name: nl_tokens
|
| 38 |
+
sequence: string
|
| 39 |
+
- name: pl_tokens
|
| 40 |
+
sequence: string
|
| 41 |
+
splits:
|
| 42 |
+
- name: train
|
| 43 |
+
num_bytes: 204997
|
| 44 |
+
num_examples: 152
|
| 45 |
+
download_size: 298893
|
| 46 |
+
dataset_size: 204997
|
| 47 |
+
- config_name: java
|
| 48 |
+
features:
|
| 49 |
+
- name: id
|
| 50 |
+
dtype: int32
|
| 51 |
+
- name: idx
|
| 52 |
+
dtype: string
|
| 53 |
+
- name: nl_tokens
|
| 54 |
+
sequence: string
|
| 55 |
+
- name: pl_tokens
|
| 56 |
+
sequence: string
|
| 57 |
+
splits:
|
| 58 |
+
- name: train
|
| 59 |
+
num_bytes: 785754
|
| 60 |
+
num_examples: 482
|
| 61 |
+
download_size: 1097733
|
| 62 |
+
dataset_size: 785754
|
| 63 |
+
- config_name: javascript
|
| 64 |
+
features:
|
| 65 |
+
- name: id
|
| 66 |
+
dtype: int32
|
| 67 |
+
- name: idx
|
| 68 |
+
dtype: string
|
| 69 |
+
- name: nl_tokens
|
| 70 |
+
sequence: string
|
| 71 |
+
- name: pl_tokens
|
| 72 |
+
sequence: string
|
| 73 |
+
splits:
|
| 74 |
+
- name: train
|
| 75 |
+
num_bytes: 594347
|
| 76 |
+
num_examples: 272
|
| 77 |
+
download_size: 836112
|
| 78 |
+
dataset_size: 594347
|
| 79 |
+
- config_name: php
|
| 80 |
+
features:
|
| 81 |
+
- name: id
|
| 82 |
+
dtype: int32
|
| 83 |
+
- name: idx
|
| 84 |
+
dtype: string
|
| 85 |
+
- name: nl_tokens
|
| 86 |
+
sequence: string
|
| 87 |
+
- name: pl_tokens
|
| 88 |
+
sequence: string
|
| 89 |
+
splits:
|
| 90 |
+
- name: train
|
| 91 |
+
num_bytes: 705477
|
| 92 |
+
num_examples: 407
|
| 93 |
+
download_size: 1010305
|
| 94 |
+
dataset_size: 705477
|
| 95 |
+
- config_name: python
|
| 96 |
+
features:
|
| 97 |
+
- name: id
|
| 98 |
+
dtype: int32
|
| 99 |
+
- name: idx
|
| 100 |
+
dtype: string
|
| 101 |
+
- name: nl_tokens
|
| 102 |
+
sequence: string
|
| 103 |
+
- name: pl_tokens
|
| 104 |
+
sequence: string
|
| 105 |
+
splits:
|
| 106 |
+
- name: train
|
| 107 |
+
num_bytes: 2566353
|
| 108 |
+
num_examples: 1264
|
| 109 |
+
download_size: 3577929
|
| 110 |
+
dataset_size: 2566353
|
| 111 |
+
- config_name: ruby
|
| 112 |
+
features:
|
| 113 |
+
- name: id
|
| 114 |
+
dtype: int32
|
| 115 |
+
- name: idx
|
| 116 |
+
dtype: string
|
| 117 |
+
- name: nl_tokens
|
| 118 |
+
sequence: string
|
| 119 |
+
- name: pl_tokens
|
| 120 |
+
sequence: string
|
| 121 |
+
splits:
|
| 122 |
+
- name: train
|
| 123 |
+
num_bytes: 48946
|
| 124 |
+
num_examples: 38
|
| 125 |
+
download_size: 67675
|
| 126 |
+
dataset_size: 48946
|
| 127 |
+
---
|
| 128 |
+
# Dataset Card for "code_x_glue_cc_cloze_testing_maxmin"
|
| 129 |
+
|
| 130 |
+
## Table of Contents
|
| 131 |
+
- [Dataset Description](#dataset-description)
|
| 132 |
+
- [Dataset Summary](#dataset-summary)
|
| 133 |
+
- [Supported Tasks and Leaderboards](#supported-tasks)
|
| 134 |
+
- [Languages](#languages)
|
| 135 |
+
- [Dataset Structure](#dataset-structure)
|
| 136 |
+
- [Data Instances](#data-instances)
|
| 137 |
+
- [Data Fields](#data-fields)
|
| 138 |
+
- [Data Splits](#data-splits-sample-size)
|
| 139 |
+
- [Dataset Creation](#dataset-creation)
|
| 140 |
+
- [Curation Rationale](#curation-rationale)
|
| 141 |
+
- [Source Data](#source-data)
|
| 142 |
+
- [Annotations](#annotations)
|
| 143 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 144 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 145 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 146 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 147 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 148 |
+
- [Additional Information](#additional-information)
|
| 149 |
+
- [Dataset Curators](#dataset-curators)
|
| 150 |
+
- [Licensing Information](#licensing-information)
|
| 151 |
+
- [Citation Information](#citation-information)
|
| 152 |
+
- [Contributions](#contributions)
|
| 153 |
+
|
| 154 |
+
## Dataset Description
|
| 155 |
+
|
| 156 |
+
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
|
| 157 |
+
|
| 158 |
+
### Dataset Summary
|
| 159 |
+
|
| 160 |
+
CodeXGLUE ClozeTesting-maxmin dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/ClozeTesting-maxmin
|
| 161 |
+
|
| 162 |
+
Cloze tests are widely adopted in Natural Languages Processing to evaluate the performance of the trained language models. The task is aimed to predict the answers for the blank with the context of the blank, which can be formulated as a multi-choice classification problem.
|
| 163 |
+
Here we present the two cloze testing datasets in code domain with six different programming languages: ClozeTest-maxmin and ClozeTest-all. Each instance in the dataset contains a masked code function, its docstring and the target word.
|
| 164 |
+
The only difference between ClozeTest-maxmin and ClozeTest-all is their selected words sets, where ClozeTest-maxmin only contains two words while ClozeTest-all contains 930 words.
|
| 165 |
+
|
| 166 |
+
### Supported Tasks and Leaderboards
|
| 167 |
+
|
| 168 |
+
- `slot-filling`: The dataset can be used to train a model for predicting the missing token from a piece of code, similar to the Cloze test.
|
| 169 |
+
|
| 170 |
+
### Languages
|
| 171 |
+
|
| 172 |
+
- Go **programming** language
|
| 173 |
+
- Java **programming** language
|
| 174 |
+
- Javascript **programming** language
|
| 175 |
+
- PHP **programming** language
|
| 176 |
+
- Python **programming** language
|
| 177 |
+
- Ruby **programming** language
|
| 178 |
+
|
| 179 |
+
## Dataset Structure
|
| 180 |
+
|
| 181 |
+
### Data Instances
|
| 182 |
+
|
| 183 |
+
#### go
|
| 184 |
+
|
| 185 |
+
An example of 'train' looks as follows.
|
| 186 |
+
```
|
| 187 |
+
{
|
| 188 |
+
"id": 0,
|
| 189 |
+
"idx": "maxmin-1",
|
| 190 |
+
"nl_tokens": ["SetMaxStructPoolSize", "sets", "the", "struct", "pools", "max", "size", ".", "this", "may", "be", "usefull", "for", "fine", "grained", "performance", "tuning", "towards", "your", "application", "however", "the", "default", "should", "be", "fine", "for", "nearly", "all", "cases", ".", "only", "increase", "if", "you", "have", "a", "deeply", "nested", "struct", "structure", ".", "NOTE", ":", "this", "method", "is", "not", "thread", "-", "safe", "NOTE", ":", "this", "is", "only", "here", "to", "keep", "compatibility", "with", "v5", "in", "v6", "the", "method", "will", "be", "removed"],
|
| 191 |
+
"pl_tokens": ["func", "(", "v", "*", "Validate", ")", "SetMaxStructPoolSize", "(", "<mask>", "int", ")", "{", "structPool", "=", "&", "sync", ".", "Pool", "{", "New", ":", "newStructErrors", "}", "\n", "}"]
|
| 192 |
+
}
|
| 193 |
+
```
|
| 194 |
+
|
| 195 |
+
#### java
|
| 196 |
+
|
| 197 |
+
An example of 'train' looks as follows.
|
| 198 |
+
```
|
| 199 |
+
{
|
| 200 |
+
"id": 0,
|
| 201 |
+
"idx": "maxmin-1",
|
| 202 |
+
"nl_tokens": ["Test", "whether", "find", "can", "be", "found", "at", "position", "startPos", "in", "the", "string", "src", "."],
|
| 203 |
+
"pl_tokens": ["public", "static", "boolean", "startsWith", "(", "char", "[", "]", "src", ",", "char", "[", "]", "find", ",", "int", "startAt", ")", "{", "int", "startPos", "=", "startAt", ";", "boolean", "result", "=", "true", ";", "// Check ranges", "if", "(", "src", ".", "length", "<", "startPos", "+", "find", ".", "length", ")", "{", "result", "=", "false", ";", "}", "else", "{", "final", "int", "<mask>", "=", "find", ".", "length", ";", "for", "(", "int", "a", "=", "0", ";", "a", "<", "max", "&&", "result", ";", "a", "++", ")", "{", "if", "(", "src", "[", "startPos", "]", "!=", "find", "[", "a", "]", ")", "{", "result", "=", "false", ";", "}", "startPos", "++", ";", "}", "}", "return", "result", ";", "}"]
|
| 204 |
+
}
|
| 205 |
+
```
|
| 206 |
+
|
| 207 |
+
#### javascript
|
| 208 |
+
|
| 209 |
+
An example of 'train' looks as follows.
|
| 210 |
+
```
|
| 211 |
+
{
|
| 212 |
+
"id": 0,
|
| 213 |
+
"idx": "maxmin-1",
|
| 214 |
+
"nl_tokens": ["string", ".", "max", "Maximum", "length", "of", "the", "string"],
|
| 215 |
+
"pl_tokens": ["function", "(", "string", ")", "{", "// string.check check sting type and size", "return", "(", "(", "typeof", "string", "===", "'string'", "||", "string", "instanceof", "String", ")", "&&", "string", ".", "length", ">=", "this", ".", "<mask>", "&&", "string", ".", "length", "<=", "this", ".", "max", "&&", "(", "!", "this", ".", "match", "||", "string", ".", "match", "(", "this", ".", "match", ")", ")", ")", ";", "}"]
|
| 216 |
+
}
|
| 217 |
+
```
|
| 218 |
+
|
| 219 |
+
#### php
|
| 220 |
+
|
| 221 |
+
An example of 'train' looks as follows.
|
| 222 |
+
```
|
| 223 |
+
{
|
| 224 |
+
"id": 0,
|
| 225 |
+
"idx": "maxmin-1",
|
| 226 |
+
"nl_tokens": ["Read", "the", "next", "character", "from", "the", "supplied", "string", ".", "Return", "null", "when", "we", "have", "run", "out", "of", "characters", "."],
|
| 227 |
+
"pl_tokens": ["public", "function", "readOne", "(", ")", "{", "if", "(", "$", "this", "->", "pos", "<=", "$", "this", "->", "<mask>", ")", "{", "$", "value", "=", "$", "this", "->", "string", "[", "$", "this", "->", "pos", "]", ";", "$", "this", "->", "pos", "+=", "1", ";", "}", "else", "{", "$", "value", "=", "null", ";", "}", "return", "$", "value", ";", "}"]
|
| 228 |
+
}
|
| 229 |
+
```
|
| 230 |
+
|
| 231 |
+
#### python
|
| 232 |
+
|
| 233 |
+
An example of 'train' looks as follows.
|
| 234 |
+
```
|
| 235 |
+
{
|
| 236 |
+
"id": 0,
|
| 237 |
+
"idx": "maxmin-1",
|
| 238 |
+
"nl_tokens": ["Returns", "intermediary", "colors", "for", "given", "list", "of", "colors", "."],
|
| 239 |
+
"pl_tokens": ["def", "_interpolate", "(", "self", ",", "colors", ",", "n", "=", "100", ")", ":", "gradient", "=", "[", "]", "for", "i", "in", "_range", "(", "n", ")", ":", "l", "=", "len", "(", "colors", ")", "-", "1", "x", "=", "int", "(", "1.0", "*", "i", "/", "n", "*", "l", ")", "x", "=", "<mask>", "(", "x", "+", "0", ",", "l", ")", "y", "=", "min", "(", "x", "+", "1", ",", "l", ")", "base", "=", "1.0", "*", "n", "/", "l", "*", "x", "d", "=", "(", "i", "-", "base", ")", "/", "(", "1.0", "*", "n", "/", "l", ")", "r", "=", "colors", "[", "x", "]", ".", "r", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "r", "*", "d", "g", "=", "colors", "[", "x", "]", ".", "g", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "g", "*", "d", "b", "=", "colors", "[", "x", "]", ".", "b", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "b", "*", "d", "a", "=", "colors", "[", "x", "]", ".", "a", "*", "(", "1", "-", "d", ")", "+", "colors", "[", "y", "]", ".", "a", "*", "d", "gradient", ".", "append", "(", "color", "(", "r", ",", "g", ",", "b", ",", "a", ",", "mode", "=", "\"rgb\"", ")", ")", "gradient", ".", "append", "(", "colors", "[", "-", "1", "]", ")", "return", "gradient"]
|
| 240 |
+
}
|
| 241 |
+
```
|
| 242 |
+
|
| 243 |
+
#### ruby
|
| 244 |
+
|
| 245 |
+
An example of 'train' looks as follows.
|
| 246 |
+
```
|
| 247 |
+
{
|
| 248 |
+
"id": 0,
|
| 249 |
+
"idx": "maxmin-1",
|
| 250 |
+
"nl_tokens": ["Delete", "all", "copies", "that", "are", "older", "than", "the", "max", "age", "provided", "in", "seconds", "."],
|
| 251 |
+
"pl_tokens": ["def", "clean", "(", "<mask>", ":", "24", "*", "60", "*", "60", ")", "Futex", ".", "new", "(", "file", ",", "log", ":", "@log", ")", ".", "open", "do", "list", "=", "load", "list", ".", "reject!", "do", "|", "s", "|", "if", "s", "[", ":time", "]", ">=", "Time", ".", "now", "-", "max", "false", "else", "@log", ".", "debug", "(", "\"Copy ##{s[:name]}/#{s[:host]}:#{s[:port]} is too old, over #{Age.new(s[:time])}\"", ")", "true", "end", "end", "save", "(", "list", ")", "deleted", "=", "0", "files", ".", "each", "do", "|", "f", "|", "next", "unless", "list", ".", "find", "{", "|", "s", "|", "s", "[", ":name", "]", "==", "File", ".", "basename", "(", "f", ",", "Copies", "::", "EXT", ")", "}", ".", "nil?", "file", "=", "File", ".", "join", "(", "@dir", ",", "f", ")", "size", "=", "File", ".", "size", "(", "file", ")", "File", ".", "delete", "(", "file", ")", "@log", ".", "debug", "(", "\"Copy at #{f} deleted: #{Size.new(size)}\"", ")", "deleted", "+=", "1", "end", "list", ".", "select!", "do", "|", "s", "|", "cp", "=", "File", ".", "join", "(", "@dir", ",", "\"#{s[:name]}#{Copies::EXT}\"", ")", "wallet", "=", "Wallet", ".", "new", "(", "cp", ")", "begin", "wallet", ".", "refurbish", "raise", "\"Invalid protocol #{wallet.protocol} in #{cp}\"", "unless", "wallet", ".", "protocol", "==", "Zold", "::", "PROTOCOL", "true", "rescue", "StandardError", "=>", "e", "FileUtils", ".", "rm_rf", "(", "cp", ")", "@log", ".", "debug", "(", "\"Copy at #{cp} deleted: #{Backtrace.new(e)}\"", ")", "deleted", "+=", "1", "false", "end", "end", "save", "(", "list", ")", "deleted", "end", "end"]
|
| 252 |
+
}
|
| 253 |
+
```
|
| 254 |
+
|
| 255 |
+
### Data Fields
|
| 256 |
+
|
| 257 |
+
In the following each data field in go is explained for each config. The data fields are the same among all splits.
|
| 258 |
+
|
| 259 |
+
#### go, java, javascript, php, python, ruby
|
| 260 |
+
|
| 261 |
+
|field name| type | description |
|
| 262 |
+
|----------|----------------|------------------------------|
|
| 263 |
+
|id |int32 | Index of the sample |
|
| 264 |
+
|idx |string | Original index in the dataset|
|
| 265 |
+
|nl_tokens |Sequence[string]| Natural language tokens |
|
| 266 |
+
|pl_tokens |Sequence[string]| Programming language tokens |
|
| 267 |
+
|
| 268 |
+
### Data Splits
|
| 269 |
+
|
| 270 |
+
| name |train|
|
| 271 |
+
|----------|----:|
|
| 272 |
+
|go | 152|
|
| 273 |
+
|java | 482|
|
| 274 |
+
|javascript| 272|
|
| 275 |
+
|php | 407|
|
| 276 |
+
|python | 1264|
|
| 277 |
+
|ruby | 38|
|
| 278 |
+
|
| 279 |
+
## Dataset Creation
|
| 280 |
+
|
| 281 |
+
### Curation Rationale
|
| 282 |
+
|
| 283 |
+
[More Information Needed]
|
| 284 |
+
|
| 285 |
+
### Source Data
|
| 286 |
+
|
| 287 |
+
#### Initial Data Collection and Normalization
|
| 288 |
+
|
| 289 |
+
Data from CodeSearchNet Challenge dataset.
|
| 290 |
+
[More Information Needed]
|
| 291 |
+
|
| 292 |
+
#### Who are the source language producers?
|
| 293 |
+
|
| 294 |
+
Software Engineering developers.
|
| 295 |
+
|
| 296 |
+
### Annotations
|
| 297 |
+
|
| 298 |
+
#### Annotation process
|
| 299 |
+
|
| 300 |
+
[More Information Needed]
|
| 301 |
+
|
| 302 |
+
#### Who are the annotators?
|
| 303 |
+
|
| 304 |
+
[More Information Needed]
|
| 305 |
+
|
| 306 |
+
### Personal and Sensitive Information
|
| 307 |
+
|
| 308 |
+
[More Information Needed]
|
| 309 |
+
|
| 310 |
+
## Considerations for Using the Data
|
| 311 |
+
|
| 312 |
+
### Social Impact of Dataset
|
| 313 |
+
|
| 314 |
+
[More Information Needed]
|
| 315 |
+
|
| 316 |
+
### Discussion of Biases
|
| 317 |
+
|
| 318 |
+
[More Information Needed]
|
| 319 |
+
|
| 320 |
+
### Other Known Limitations
|
| 321 |
+
|
| 322 |
+
[More Information Needed]
|
| 323 |
+
|
| 324 |
+
## Additional Information
|
| 325 |
+
|
| 326 |
+
### Dataset Curators
|
| 327 |
+
|
| 328 |
+
https://github.com/microsoft, https://github.com/madlag
|
| 329 |
+
|
| 330 |
+
### Licensing Information
|
| 331 |
+
|
| 332 |
+
Computational Use of Data Agreement (C-UDA) License.
|
| 333 |
+
|
| 334 |
+
### Citation Information
|
| 335 |
+
|
| 336 |
+
```
|
| 337 |
+
@article{CodeXGLUE,
|
| 338 |
+
title={CodeXGLUE: An Open Challenge for Code Intelligence},
|
| 339 |
+
journal={arXiv},
|
| 340 |
+
year={2020},
|
| 341 |
+
}
|
| 342 |
+
@article{feng2020codebert,
|
| 343 |
+
title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages},
|
| 344 |
+
author={Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others},
|
| 345 |
+
journal={arXiv preprint arXiv:2002.08155},
|
| 346 |
+
year={2020}
|
| 347 |
+
}
|
| 348 |
+
@article{husain2019codesearchnet,
|
| 349 |
+
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
|
| 350 |
+
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
|
| 351 |
+
journal={arXiv preprint arXiv:1909.09436},
|
| 352 |
+
year={2019}
|
| 353 |
+
}
|
| 354 |
+
```
|
| 355 |
+
|
| 356 |
+
### Contributions
|
| 357 |
+
|
| 358 |
+
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.
|
huggingface_dataset/Dataset_Card/codeparrot_github-jupyter.md
ADDED
|
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators: []
|
| 3 |
+
language_creators:
|
| 4 |
+
- crowdsourced
|
| 5 |
+
- expert-generated
|
| 6 |
+
language:
|
| 7 |
+
- code
|
| 8 |
+
license:
|
| 9 |
+
- other
|
| 10 |
+
multilinguality:
|
| 11 |
+
- muonolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- unknown
|
| 14 |
+
source_datasets: []
|
| 15 |
+
task_categories:
|
| 16 |
+
- text-generation
|
| 17 |
+
task_ids:
|
| 18 |
+
- language-modeling
|
| 19 |
+
---
|
| 20 |
+
|
| 21 |
+
# GitHub Jupyter Dataset
|
| 22 |
+
|
| 23 |
+
## Dataset Description
|
| 24 |
+
The dataset was extracted from Jupyter Notebooks on BigQuery.
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
## Licenses
|
| 28 |
+
Each example has the license of its associated repository. There are in total 15 licenses:
|
| 29 |
+
```python
|
| 30 |
+
[
|
| 31 |
+
'mit',
|
| 32 |
+
'apache-2.0',
|
| 33 |
+
'gpl-3.0',
|
| 34 |
+
'gpl-2.0',
|
| 35 |
+
'bsd-3-clause',
|
| 36 |
+
'agpl-3.0',
|
| 37 |
+
'lgpl-3.0',
|
| 38 |
+
'lgpl-2.1',
|
| 39 |
+
'bsd-2-clause',
|
| 40 |
+
'cc0-1.0',
|
| 41 |
+
'epl-1.0',
|
| 42 |
+
'mpl-2.0',
|
| 43 |
+
'unlicense',
|
| 44 |
+
'isc',
|
| 45 |
+
'artistic-2.0'
|
| 46 |
+
]
|
| 47 |
+
```
|
huggingface_dataset/Dataset_Card/fewshot-goes-multilingual_cs_czech-court-decisions-ner.md
ADDED
|
@@ -0,0 +1,82 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language:
|
| 5 |
+
- cs
|
| 6 |
+
language_creators:
|
| 7 |
+
- other
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-nc-sa-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Czech Court Decisions NER
|
| 13 |
+
size_categories:
|
| 14 |
+
- n<1K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
tags:
|
| 18 |
+
- czech NER
|
| 19 |
+
- court decisions
|
| 20 |
+
task_categories:
|
| 21 |
+
- token-classification
|
| 22 |
+
task_ids:
|
| 23 |
+
- named-entity-recognition
|
| 24 |
+
---
|
| 25 |
+
|
| 26 |
+
# Dataset Card for Czech Court Decisions NER
|
| 27 |
+
|
| 28 |
+
|
| 29 |
+
## Dataset Description
|
| 30 |
+
Czech Court Decisions NER is a dataset of 300 court decisions published by The Supreme Court of the Czech Republic and the Constitutional Court of the Czech Republic.
|
| 31 |
+
In the documents, 4 types of named entities are selected.
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
## Dataset Features
|
| 35 |
+
|
| 36 |
+
Each sample contains:
|
| 37 |
+
- `filename`: file name in the original dataset
|
| 38 |
+
- `text`: court decision document in plain text
|
| 39 |
+
- `entities`: list of selected entities. Each entity contains:
|
| 40 |
+
- `category_id`: integer identifier of the entity category
|
| 41 |
+
- `category_str`: human-friendly category name in Czech (verbalizer)
|
| 42 |
+
- `start`: index on which the entity starts in the source text
|
| 43 |
+
- `end`: index on which the entity ends in the source text
|
| 44 |
+
- `content`: entity content, it was created as `text[start:end]`
|
| 45 |
+
- `entity_id`: unique entity string identifier
|
| 46 |
+
- `refers_to`: some entities (mostly of category 'Reference na rozhodnutí soudu') refer to a specific other entity. `refers_to` attribute contains the `entity_id` of the referred entity
|
| 47 |
+
|
| 48 |
+
The `entity_id` field was checked to be globally unique (across data samples and dataset splits.)
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
## Entity categories
|
| 52 |
+
|
| 53 |
+
The list of the recognized entities (`category_id`, `category_str` pairs):
|
| 54 |
+
```python3
|
| 55 |
+
{
|
| 56 |
+
0: 'Soudní instituce',
|
| 57 |
+
1: 'Reference na rozhodnutí soudu',
|
| 58 |
+
2: 'Účinnost',
|
| 59 |
+
3: 'Reference zákonu'
|
| 60 |
+
}
|
| 61 |
+
```
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
## Dataset Source
|
| 65 |
+
|
| 66 |
+
The dataset is a preprocessed adaptation of existing Czech Court Decisions Dataset [project info](https://ufal.mff.cuni.cz/ccdd), [link to data](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-2853). This adaptation contains (almost) same data, but converted to a convenient format and with stripped leaked xml-like tags in the texts.
|
| 67 |
+
The category names (verbalizers) were added by a Czech native speaker.
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
## Citation
|
| 71 |
+
|
| 72 |
+
Cite authors of the [original dataset](https://ufal.mff.cuni.cz/ccdd):
|
| 73 |
+
```bibtex
|
| 74 |
+
@misc{11234/1-2853,
|
| 75 |
+
title = {Czech Court Decisions Dataset},
|
| 76 |
+
author = {Kr{\'{\i}}{\v z}, Vincent and Hladk{\'a}, Barbora},
|
| 77 |
+
url = {http://hdl.handle.net/11234/1-2853},
|
| 78 |
+
note = {{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University},
|
| 79 |
+
copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)},
|
| 80 |
+
year = {2014}
|
| 81 |
+
}
|
| 82 |
+
```
|
huggingface_dataset/Dataset_Card/huggingface_semantic-segmentation-test-sample.md
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
This dataset contains 10 examples of the [segments/sidewalk-semantic](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset (i.e. 10 images with corresponding ground-truth segmentation maps).
|
huggingface_dataset/Dataset_Card/irds_clinicaltrials_2019.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`clinicaltrials/2019`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: []
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `clinicaltrials/2019`
|
| 10 |
+
|
| 11 |
+
The `clinicaltrials/2019` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/clinicaltrials#clinicaltrials/2019).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `docs` (documents, i.e., the corpus); count=306,238
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
This dataset is used by: [`clinicaltrials_2019_trec-pm-2019`](https://huggingface.co/datasets/irds/clinicaltrials_2019_trec-pm-2019)
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
## Usage
|
| 24 |
+
|
| 25 |
+
```python
|
| 26 |
+
from datasets import load_dataset
|
| 27 |
+
|
| 28 |
+
docs = load_dataset('irds/clinicaltrials_2019', 'docs')
|
| 29 |
+
for record in docs:
|
| 30 |
+
record # {'doc_id': ..., 'title': ..., 'condition': ..., 'summary': ..., 'detailed_description': ..., 'eligibility': ...}
|
| 31 |
+
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 35 |
+
data in 🤗 Dataset format.
|
huggingface_dataset/Dataset_Card/irds_medline_2017_trec-pm-2018.md
ADDED
|
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`medline/2017/trec-pm-2018`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: ['irds/medline_2017']
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `medline/2017/trec-pm-2018`
|
| 10 |
+
|
| 11 |
+
The `medline/2017/trec-pm-2018` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/medline#medline/2017/trec-pm-2018).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `queries` (i.e., topics); count=50
|
| 18 |
+
- `qrels`: (relevance assessments); count=22,429
|
| 19 |
+
|
| 20 |
+
- For `docs`, use [`irds/medline_2017`](https://huggingface.co/datasets/irds/medline_2017)
|
| 21 |
+
|
| 22 |
+
## Usage
|
| 23 |
+
|
| 24 |
+
```python
|
| 25 |
+
from datasets import load_dataset
|
| 26 |
+
|
| 27 |
+
queries = load_dataset('irds/medline_2017_trec-pm-2018', 'queries')
|
| 28 |
+
for record in queries:
|
| 29 |
+
record # {'query_id': ..., 'disease': ..., 'gene': ..., 'demographic': ...}
|
| 30 |
+
|
| 31 |
+
qrels = load_dataset('irds/medline_2017_trec-pm-2018', 'qrels')
|
| 32 |
+
for record in qrels:
|
| 33 |
+
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
|
| 34 |
+
|
| 35 |
+
```
|
| 36 |
+
|
| 37 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 38 |
+
data in 🤗 Dataset format.
|
| 39 |
+
|
| 40 |
+
## Citation Information
|
| 41 |
+
|
| 42 |
+
```
|
| 43 |
+
@inproceedings{Roberts2018TrecPm,
|
| 44 |
+
title={Overview of the TREC 2018 Precision Medicine Track},
|
| 45 |
+
author={Kirk Roberts and Dina Demner-Fushman and Ellen M. Voorhees and William R. Hersh and Steven Bedrick and Alexander J. Lazar},
|
| 46 |
+
booktitle={TREC},
|
| 47 |
+
year={2018}
|
| 48 |
+
}
|
| 49 |
+
```
|
huggingface_dataset/Dataset_Card/nchlt.md
ADDED
|
@@ -0,0 +1,399 @@
|
|
|
|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
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|
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|
|
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|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- expert-generated
|
| 6 |
+
language:
|
| 7 |
+
- af
|
| 8 |
+
- nr
|
| 9 |
+
- nso
|
| 10 |
+
- ss
|
| 11 |
+
- tn
|
| 12 |
+
- ts
|
| 13 |
+
- ve
|
| 14 |
+
- xh
|
| 15 |
+
- zu
|
| 16 |
+
license:
|
| 17 |
+
- cc-by-2.5
|
| 18 |
+
multilinguality:
|
| 19 |
+
- multilingual
|
| 20 |
+
size_categories:
|
| 21 |
+
- 1K<n<10K
|
| 22 |
+
source_datasets:
|
| 23 |
+
- original
|
| 24 |
+
task_categories:
|
| 25 |
+
- token-classification
|
| 26 |
+
task_ids:
|
| 27 |
+
- named-entity-recognition
|
| 28 |
+
pretty_name: NCHLT
|
| 29 |
+
dataset_info:
|
| 30 |
+
- config_name: af
|
| 31 |
+
features:
|
| 32 |
+
- name: tokens
|
| 33 |
+
sequence: string
|
| 34 |
+
- name: ner_tags
|
| 35 |
+
sequence:
|
| 36 |
+
class_label:
|
| 37 |
+
names:
|
| 38 |
+
'0': OUT
|
| 39 |
+
'1': B-PERS
|
| 40 |
+
'2': I-PERS
|
| 41 |
+
'3': B-ORG
|
| 42 |
+
'4': I-ORG
|
| 43 |
+
'5': B-LOC
|
| 44 |
+
'6': I-LOC
|
| 45 |
+
'7': B-MISC
|
| 46 |
+
'8': I-MISC
|
| 47 |
+
splits:
|
| 48 |
+
- name: train
|
| 49 |
+
num_bytes: 3955069
|
| 50 |
+
num_examples: 8961
|
| 51 |
+
download_size: 25748344
|
| 52 |
+
dataset_size: 3955069
|
| 53 |
+
- config_name: nr
|
| 54 |
+
features:
|
| 55 |
+
- name: tokens
|
| 56 |
+
sequence: string
|
| 57 |
+
- name: ner_tags
|
| 58 |
+
sequence:
|
| 59 |
+
class_label:
|
| 60 |
+
names:
|
| 61 |
+
'0': OUT
|
| 62 |
+
'1': B-PERS
|
| 63 |
+
'2': I-PERS
|
| 64 |
+
'3': B-ORG
|
| 65 |
+
'4': I-ORG
|
| 66 |
+
'5': B-LOC
|
| 67 |
+
'6': I-LOC
|
| 68 |
+
'7': B-MISC
|
| 69 |
+
'8': I-MISC
|
| 70 |
+
splits:
|
| 71 |
+
- name: train
|
| 72 |
+
num_bytes: 3188781
|
| 73 |
+
num_examples: 9334
|
| 74 |
+
download_size: 20040327
|
| 75 |
+
dataset_size: 3188781
|
| 76 |
+
- config_name: xh
|
| 77 |
+
features:
|
| 78 |
+
- name: tokens
|
| 79 |
+
sequence: string
|
| 80 |
+
- name: ner_tags
|
| 81 |
+
sequence:
|
| 82 |
+
class_label:
|
| 83 |
+
names:
|
| 84 |
+
'0': OUT
|
| 85 |
+
'1': B-PERS
|
| 86 |
+
'2': I-PERS
|
| 87 |
+
'3': B-ORG
|
| 88 |
+
'4': I-ORG
|
| 89 |
+
'5': B-LOC
|
| 90 |
+
'6': I-LOC
|
| 91 |
+
'7': B-MISC
|
| 92 |
+
'8': I-MISC
|
| 93 |
+
splits:
|
| 94 |
+
- name: train
|
| 95 |
+
num_bytes: 2365821
|
| 96 |
+
num_examples: 6283
|
| 97 |
+
download_size: 14513302
|
| 98 |
+
dataset_size: 2365821
|
| 99 |
+
- config_name: zu
|
| 100 |
+
features:
|
| 101 |
+
- name: tokens
|
| 102 |
+
sequence: string
|
| 103 |
+
- name: ner_tags
|
| 104 |
+
sequence:
|
| 105 |
+
class_label:
|
| 106 |
+
names:
|
| 107 |
+
'0': OUT
|
| 108 |
+
'1': B-PERS
|
| 109 |
+
'2': I-PERS
|
| 110 |
+
'3': B-ORG
|
| 111 |
+
'4': I-ORG
|
| 112 |
+
'5': B-LOC
|
| 113 |
+
'6': I-LOC
|
| 114 |
+
'7': B-MISC
|
| 115 |
+
'8': I-MISC
|
| 116 |
+
splits:
|
| 117 |
+
- name: train
|
| 118 |
+
num_bytes: 3951366
|
| 119 |
+
num_examples: 10955
|
| 120 |
+
download_size: 25097584
|
| 121 |
+
dataset_size: 3951366
|
| 122 |
+
- config_name: nso-sepedi
|
| 123 |
+
features:
|
| 124 |
+
- name: tokens
|
| 125 |
+
sequence: string
|
| 126 |
+
- name: ner_tags
|
| 127 |
+
sequence:
|
| 128 |
+
class_label:
|
| 129 |
+
names:
|
| 130 |
+
'0': OUT
|
| 131 |
+
'1': B-PERS
|
| 132 |
+
'2': I-PERS
|
| 133 |
+
'3': B-ORG
|
| 134 |
+
'4': I-ORG
|
| 135 |
+
'5': B-LOC
|
| 136 |
+
'6': I-LOC
|
| 137 |
+
'7': B-MISC
|
| 138 |
+
'8': I-MISC
|
| 139 |
+
splits:
|
| 140 |
+
- name: train
|
| 141 |
+
num_bytes: 3322296
|
| 142 |
+
num_examples: 7116
|
| 143 |
+
download_size: 22077376
|
| 144 |
+
dataset_size: 3322296
|
| 145 |
+
- config_name: nso-sesotho
|
| 146 |
+
features:
|
| 147 |
+
- name: tokens
|
| 148 |
+
sequence: string
|
| 149 |
+
- name: ner_tags
|
| 150 |
+
sequence:
|
| 151 |
+
class_label:
|
| 152 |
+
names:
|
| 153 |
+
'0': OUT
|
| 154 |
+
'1': B-PERS
|
| 155 |
+
'2': I-PERS
|
| 156 |
+
'3': B-ORG
|
| 157 |
+
'4': I-ORG
|
| 158 |
+
'5': B-LOC
|
| 159 |
+
'6': I-LOC
|
| 160 |
+
'7': B-MISC
|
| 161 |
+
'8': I-MISC
|
| 162 |
+
splits:
|
| 163 |
+
- name: train
|
| 164 |
+
num_bytes: 4427898
|
| 165 |
+
num_examples: 9471
|
| 166 |
+
download_size: 30421109
|
| 167 |
+
dataset_size: 4427898
|
| 168 |
+
- config_name: tn
|
| 169 |
+
features:
|
| 170 |
+
- name: tokens
|
| 171 |
+
sequence: string
|
| 172 |
+
- name: ner_tags
|
| 173 |
+
sequence:
|
| 174 |
+
class_label:
|
| 175 |
+
names:
|
| 176 |
+
'0': OUT
|
| 177 |
+
'1': B-PERS
|
| 178 |
+
'2': I-PERS
|
| 179 |
+
'3': B-ORG
|
| 180 |
+
'4': I-ORG
|
| 181 |
+
'5': B-LOC
|
| 182 |
+
'6': I-LOC
|
| 183 |
+
'7': B-MISC
|
| 184 |
+
'8': I-MISC
|
| 185 |
+
splits:
|
| 186 |
+
- name: train
|
| 187 |
+
num_bytes: 3812339
|
| 188 |
+
num_examples: 7943
|
| 189 |
+
download_size: 25905236
|
| 190 |
+
dataset_size: 3812339
|
| 191 |
+
- config_name: ss
|
| 192 |
+
features:
|
| 193 |
+
- name: tokens
|
| 194 |
+
sequence: string
|
| 195 |
+
- name: ner_tags
|
| 196 |
+
sequence:
|
| 197 |
+
class_label:
|
| 198 |
+
names:
|
| 199 |
+
'0': OUT
|
| 200 |
+
'1': B-PERS
|
| 201 |
+
'2': I-PERS
|
| 202 |
+
'3': B-ORG
|
| 203 |
+
'4': I-ORG
|
| 204 |
+
'5': B-LOC
|
| 205 |
+
'6': I-LOC
|
| 206 |
+
'7': B-MISC
|
| 207 |
+
'8': I-MISC
|
| 208 |
+
splits:
|
| 209 |
+
- name: train
|
| 210 |
+
num_bytes: 3431063
|
| 211 |
+
num_examples: 10797
|
| 212 |
+
download_size: 21882224
|
| 213 |
+
dataset_size: 3431063
|
| 214 |
+
- config_name: ve
|
| 215 |
+
features:
|
| 216 |
+
- name: tokens
|
| 217 |
+
sequence: string
|
| 218 |
+
- name: ner_tags
|
| 219 |
+
sequence:
|
| 220 |
+
class_label:
|
| 221 |
+
names:
|
| 222 |
+
'0': OUT
|
| 223 |
+
'1': B-PERS
|
| 224 |
+
'2': I-PERS
|
| 225 |
+
'3': B-ORG
|
| 226 |
+
'4': I-ORG
|
| 227 |
+
'5': B-LOC
|
| 228 |
+
'6': I-LOC
|
| 229 |
+
'7': B-MISC
|
| 230 |
+
'8': I-MISC
|
| 231 |
+
splits:
|
| 232 |
+
- name: train
|
| 233 |
+
num_bytes: 3941041
|
| 234 |
+
num_examples: 8477
|
| 235 |
+
download_size: 26382457
|
| 236 |
+
dataset_size: 3941041
|
| 237 |
+
- config_name: ts
|
| 238 |
+
features:
|
| 239 |
+
- name: tokens
|
| 240 |
+
sequence: string
|
| 241 |
+
- name: ner_tags
|
| 242 |
+
sequence:
|
| 243 |
+
class_label:
|
| 244 |
+
names:
|
| 245 |
+
'0': OUT
|
| 246 |
+
'1': B-PERS
|
| 247 |
+
'2': I-PERS
|
| 248 |
+
'3': B-ORG
|
| 249 |
+
'4': I-ORG
|
| 250 |
+
'5': B-LOC
|
| 251 |
+
'6': I-LOC
|
| 252 |
+
'7': B-MISC
|
| 253 |
+
'8': I-MISC
|
| 254 |
+
splits:
|
| 255 |
+
- name: train
|
| 256 |
+
num_bytes: 3941041
|
| 257 |
+
num_examples: 8477
|
| 258 |
+
download_size: 26382457
|
| 259 |
+
dataset_size: 3941041
|
| 260 |
+
---
|
| 261 |
+
# Dataset Card for NCHLT
|
| 262 |
+
|
| 263 |
+
## Table of Contents
|
| 264 |
+
- [Dataset Description](#dataset-description)
|
| 265 |
+
- [Dataset Summary](#dataset-summary)
|
| 266 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 267 |
+
- [Languages](#languages)
|
| 268 |
+
- [Dataset Structure](#dataset-structure)
|
| 269 |
+
- [Data Instances](#data-instances)
|
| 270 |
+
- [Data Fields](#data-fields)
|
| 271 |
+
- [Data Splits](#data-splits)
|
| 272 |
+
- [Dataset Creation](#dataset-creation)
|
| 273 |
+
- [Curation Rationale](#curation-rationale)
|
| 274 |
+
- [Source Data](#source-data)
|
| 275 |
+
- [Annotations](#annotations)
|
| 276 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 277 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 278 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 279 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 280 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 281 |
+
- [Additional Information](#additional-information)
|
| 282 |
+
- [Dataset Curators](#dataset-curators)
|
| 283 |
+
- [Licensing Information](#licensing-information)
|
| 284 |
+
- [Citation Information](#citation-information)
|
| 285 |
+
- [Contributions](#contributions)
|
| 286 |
+
|
| 287 |
+
## Dataset Description
|
| 288 |
+
|
| 289 |
+
- **Homepage:** [link](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II)
|
| 290 |
+
- **Repository:** []()
|
| 291 |
+
- **Paper:** []()
|
| 292 |
+
- **Leaderboard:** []()
|
| 293 |
+
- **Point of Contact:** []()
|
| 294 |
+
|
| 295 |
+
### Dataset Summary
|
| 296 |
+
|
| 297 |
+
The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context.
|
| 298 |
+
|
| 299 |
+
|
| 300 |
+
|
| 301 |
+
### Supported Tasks and Leaderboards
|
| 302 |
+
|
| 303 |
+
[More Information Needed]
|
| 304 |
+
|
| 305 |
+
### Languages
|
| 306 |
+
|
| 307 |
+
[More Information Needed]
|
| 308 |
+
|
| 309 |
+
## Dataset Structure
|
| 310 |
+
|
| 311 |
+
[More Information Needed]
|
| 312 |
+
|
| 313 |
+
### Data Instances
|
| 314 |
+
|
| 315 |
+
[More Information Needed]
|
| 316 |
+
|
| 317 |
+
### Data Fields
|
| 318 |
+
|
| 319 |
+
[More Information Needed]
|
| 320 |
+
|
| 321 |
+
### Data Splits
|
| 322 |
+
|
| 323 |
+
[More Information Needed]
|
| 324 |
+
|
| 325 |
+
## Dataset Creation
|
| 326 |
+
|
| 327 |
+
|
| 328 |
+
### Curation Rationale
|
| 329 |
+
|
| 330 |
+
[More Information Needed]
|
| 331 |
+
|
| 332 |
+
### Source Data
|
| 333 |
+
|
| 334 |
+
[More Information Needed]
|
| 335 |
+
|
| 336 |
+
#### Initial Data Collection and Normalization
|
| 337 |
+
|
| 338 |
+
[More Information Needed]
|
| 339 |
+
|
| 340 |
+
#### Who are the source language producers?
|
| 341 |
+
|
| 342 |
+
[More Information Needed]
|
| 343 |
+
|
| 344 |
+
### Annotations
|
| 345 |
+
|
| 346 |
+
[More Information Needed]
|
| 347 |
+
|
| 348 |
+
#### Annotation process
|
| 349 |
+
|
| 350 |
+
[More Information Needed]
|
| 351 |
+
|
| 352 |
+
#### Who are the annotators?
|
| 353 |
+
|
| 354 |
+
[More Information Needed]
|
| 355 |
+
|
| 356 |
+
### Personal and Sensitive Information
|
| 357 |
+
|
| 358 |
+
[More Information Needed]
|
| 359 |
+
|
| 360 |
+
## Considerations for Using the Data
|
| 361 |
+
|
| 362 |
+
### Social Impact of Dataset
|
| 363 |
+
|
| 364 |
+
[More Information Needed]
|
| 365 |
+
|
| 366 |
+
### Discussion of Biases
|
| 367 |
+
|
| 368 |
+
[More Information Needed]
|
| 369 |
+
|
| 370 |
+
### Other Known Limitations
|
| 371 |
+
|
| 372 |
+
[More Information Needed]
|
| 373 |
+
|
| 374 |
+
## Additional Information
|
| 375 |
+
|
| 376 |
+
### Dataset Curators
|
| 377 |
+
|
| 378 |
+
Martin.Puttkammer@nwu.ac.za
|
| 379 |
+
|
| 380 |
+
### Licensing Information
|
| 381 |
+
|
| 382 |
+
[More Information Needed]
|
| 383 |
+
|
| 384 |
+
### Citation Information
|
| 385 |
+
|
| 386 |
+
```
|
| 387 |
+
@inproceedings{eiselen2014developing,
|
| 388 |
+
title={Developing Text Resources for Ten South African Languages.},
|
| 389 |
+
author={Eiselen, Roald and Puttkammer, Martin J},
|
| 390 |
+
booktitle={LREC},
|
| 391 |
+
pages={3698--3703},
|
| 392 |
+
year={2014}
|
| 393 |
+
}
|
| 394 |
+
```
|
| 395 |
+
|
| 396 |
+
|
| 397 |
+
### Contributions
|
| 398 |
+
|
| 399 |
+
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
|
huggingface_dataset/Dataset_Card/neuralspace_citizen_nlu.md
ADDED
|
@@ -0,0 +1,166 @@
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- other
|
| 4 |
+
language_creators:
|
| 5 |
+
- other
|
| 6 |
+
language:
|
| 7 |
+
- as
|
| 8 |
+
- bn
|
| 9 |
+
- gu
|
| 10 |
+
- hi
|
| 11 |
+
- kn
|
| 12 |
+
- mr
|
| 13 |
+
- pa
|
| 14 |
+
- ta
|
| 15 |
+
- te
|
| 16 |
+
expert-generated license:
|
| 17 |
+
- cc-by-nc-sa-4.0
|
| 18 |
+
multilinguality:
|
| 19 |
+
- multilingual
|
| 20 |
+
size_categories:
|
| 21 |
+
- n>1K
|
| 22 |
+
source_datasets:
|
| 23 |
+
- original
|
| 24 |
+
task_categories:
|
| 25 |
+
- question-answering
|
| 26 |
+
- text-retrieval
|
| 27 |
+
- text2text-generation
|
| 28 |
+
- other
|
| 29 |
+
- translation
|
| 30 |
+
- conversational
|
| 31 |
+
task_ids:
|
| 32 |
+
- extractive-qa
|
| 33 |
+
- closed-domain-qa
|
| 34 |
+
- utterance-retrieval
|
| 35 |
+
- document-retrieval
|
| 36 |
+
- closed-domain-qa
|
| 37 |
+
- open-book-qa
|
| 38 |
+
- closed-book-qa
|
| 39 |
+
paperswithcode_id: acronym-identification
|
| 40 |
+
pretty_name: Citizen Services NLU Multilingual Dataset.
|
| 41 |
+
train-eval-index:
|
| 42 |
+
- config: citizen_nlu
|
| 43 |
+
task: token-classification
|
| 44 |
+
task_id: entity_extraction
|
| 45 |
+
splits:
|
| 46 |
+
train_split: train
|
| 47 |
+
eval_split: test
|
| 48 |
+
col_mapping:
|
| 49 |
+
sentence: text
|
| 50 |
+
label: target
|
| 51 |
+
metrics:
|
| 52 |
+
- type: citizen_nlu
|
| 53 |
+
name: citizen_nlu
|
| 54 |
+
config:
|
| 55 |
+
citizen_nlu
|
| 56 |
+
tags:
|
| 57 |
+
- chatbots
|
| 58 |
+
- citizen services
|
| 59 |
+
- help
|
| 60 |
+
- emergency services
|
| 61 |
+
- health
|
| 62 |
+
- reporting crime
|
| 63 |
+
configs:
|
| 64 |
+
- citizen_nlu
|
| 65 |
+
---
|
| 66 |
+
# Dataset Card for citizen_nlu
|
| 67 |
+
|
| 68 |
+
## Table of Contents
|
| 69 |
+
|
| 70 |
+
- [Dataset Description](#dataset-description)
|
| 71 |
+
- [Dataset Summary](#dataset-summary)
|
| 72 |
+
- [Supported Tasks](#supported-tasks)
|
| 73 |
+
- [Languages](#languages)
|
| 74 |
+
- [Dataset Structure](#dataset-structure)
|
| 75 |
+
- [Data Instances](#data-instances)
|
| 76 |
+
- [Data Fields](#data-fields)
|
| 77 |
+
- [Data Splits](#data-splits)
|
| 78 |
+
- [Dataset Creation](#dataset-creation)
|
| 79 |
+
- [Curation Rationale](#curation-rationale)
|
| 80 |
+
- [Source Data](#source-data)
|
| 81 |
+
- [Annotations](#annotations)
|
| 82 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 83 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 84 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 85 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 86 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 87 |
+
- [Additional Information](#additional-information)
|
| 88 |
+
- [Dataset Curators](#dataset-curators)
|
| 89 |
+
- [Licensing Information](#licensing-information)
|
| 90 |
+
- [Citation Information](#citation-information)
|
| 91 |
+
- [Contributions](#contributions)
|
| 92 |
+
|
| 93 |
+
### Dataset Description
|
| 94 |
+
|
| 95 |
+
- **Homepage**: [NeuralSpace Homepage](https://huggingface.co/neuralspace)
|
| 96 |
+
- **Repository:** [citizen_nlu Dataset](https://huggingface.co/datasets/neuralspace/citizen_nlu)
|
| 97 |
+
- **Point of Contact:** [Juhi Jain](mailto:juhi@neuralspace.ai)
|
| 98 |
+
- **Point of Contact:** [Ayushman Dash](mailto:ayushman@neuralspace.ai)
|
| 99 |
+
- **Size of downloaded dataset files:** 67.6 MB
|
| 100 |
+
|
| 101 |
+
### Dataset Summary
|
| 102 |
+
|
| 103 |
+
NeuralSpace strives to provide AutoNLP text and speech services, especially for low-resource languages. One of the major services provided by NeuralSpace on its platform is the “Language Understanding” service, where you can build, train and deploy your NLU model to recognize intents and entities with minimal code and just a few clicks.
|
| 104 |
+
|
| 105 |
+
The initiative of this challenge is created with the purpose of sparkling AI applications to address some of the pressing problems in India and find unique ways to address them. Starting with a focus on NLU, this challenge hopes to make progress towards multilingual modelling, as language diversity is significantly underserved on the web.
|
| 106 |
+
|
| 107 |
+
NeuralSpace aims at mastering the low-resource domain, and the citizen services use case is naturally a multilingual and essential domain for the general citizen.
|
| 108 |
+
|
| 109 |
+
Citizen services refer to the essential services provided by organizations to general citizens. In this case, we focus on important services like various FIR-based requests, Blood/Platelets Donation, and Coronavirus-related queries.
|
| 110 |
+
|
| 111 |
+
Such services may not be needed regularly by any particular city but when needed are of utmost importance, and in general, the needs for such services are prevalent every day.
|
| 112 |
+
|
| 113 |
+
Despite the importance of citizen services, linguistically rich countries like India are still far behind in delivering such essential needs to the citizens with absolute ease. The best services currently available do not exist in various low-resource languages that are native to different groups of people. This challenge aims to make government services more efficient, responsive, and customer-friendly.
|
| 114 |
+
|
| 115 |
+
As our computing resources and modelling capabilities grow, so does our potential to support our citizens by delivering a far superior customer experience. Equipping a Citizen services bot with the ability to converse in vernacular languages would make them accessible to a vast group of people for whom English is not a language of choice, but for who are increasingly turning to digital platforms and interfaces for a wide range of needs and wants.
|
| 116 |
+
|
| 117 |
+
### Supported Tasks
|
| 118 |
+
|
| 119 |
+
A key component of any chatbot system is the NLU pipeline for ‘Intent Classification’ and ‘Named Entity Recognition. This primarily enables any chatbot to perform various tasks at ease. A fully functional multilingual chatbot needs to be able to decipher the language and understand exactly what the user wants.
|
| 120 |
+
|
| 121 |
+
|
| 122 |
+
#### citizen_nlu
|
| 123 |
+
|
| 124 |
+
A manually-curated multilingual dataset by Data Engineers at [NeuralSpace](https://www.neuralspace.ai/) for citizen services in 9 Indian languages for a realistic information-seeking task with data samples written by native-speaking expert data annotators [here](https://www.neuralspace.ai/). The dataset files are available in CSV format.
|
| 125 |
+
|
| 126 |
+
### Languages
|
| 127 |
+
|
| 128 |
+
The citizen_nlu data is available in nine Indian languages i.e, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil, and Telugu
|
| 129 |
+
|
| 130 |
+
## Dataset Structure
|
| 131 |
+
|
| 132 |
+
### Data Instances
|
| 133 |
+
|
| 134 |
+
- **Size of downloaded dataset files:** 67.6 MB
|
| 135 |
+
|
| 136 |
+
An example of 'test' looks as follows.
|
| 137 |
+
|
| 138 |
+
``` text,intents
|
| 139 |
+
मेरे पिता की कार उनके कार्यालय की पार्किंग से कल से गायब है। वाहन संख्या केए-03-एचए-1985 । मैं एफआईआर कराना चाहता हूं।,ReportingMissingVehicle
|
| 140 |
+
```
|
| 141 |
+
|
| 142 |
+
An example of 'train' looks as follows.
|
| 143 |
+
|
| 144 |
+
```text,intents
|
| 145 |
+
என் தாத்தா எனக்கு பிறந்தநாள் பரிசு கொடுத்தார் மஞ்சள் நான் டாடனானோவை இழந்தேன். காணவில்லை என புகார் தெரிவிக்க விரும்புகிறேன்,ReportingMissingVehicle
|
| 146 |
+
```
|
| 147 |
+
|
| 148 |
+
### Data Fields
|
| 149 |
+
|
| 150 |
+
The data fields are the same among all splits.
|
| 151 |
+
|
| 152 |
+
#### citizen_nlu
|
| 153 |
+
|
| 154 |
+
- `text`: a `string` feature.
|
| 155 |
+
- `intent`: a `string` feature.
|
| 156 |
+
- `type`: a classification label, with possible values including `train` or `test`.
|
| 157 |
+
|
| 158 |
+
### Data Splits
|
| 159 |
+
|
| 160 |
+
#### citizen_nlu
|
| 161 |
+
| |train|test|
|
| 162 |
+
|----|----:|---:|
|
| 163 |
+
|citizen_nlu| 287832| 4752|
|
| 164 |
+
|
| 165 |
+
### Contributions
|
| 166 |
+
Mehar Bhatia (mehar@neuralspace.ai)
|
huggingface_dataset/Dataset_Card/pragmeval.md
ADDED
|
@@ -0,0 +1,812 @@
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| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 10K<n<100K
|
| 14 |
+
- 1K<n<10K
|
| 15 |
+
- n<1K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
task_categories:
|
| 19 |
+
- text-classification
|
| 20 |
+
task_ids:
|
| 21 |
+
- multi-class-classification
|
| 22 |
+
pretty_name: pragmeval
|
| 23 |
+
configs:
|
| 24 |
+
- emergent
|
| 25 |
+
- emobank-arousal
|
| 26 |
+
- emobank-dominance
|
| 27 |
+
- emobank-valence
|
| 28 |
+
- gum
|
| 29 |
+
- mrda
|
| 30 |
+
- pdtb
|
| 31 |
+
- persuasiveness-claimtype
|
| 32 |
+
- persuasiveness-eloquence
|
| 33 |
+
- persuasiveness-premisetype
|
| 34 |
+
- persuasiveness-relevance
|
| 35 |
+
- persuasiveness-specificity
|
| 36 |
+
- persuasiveness-strength
|
| 37 |
+
- sarcasm
|
| 38 |
+
- squinky-formality
|
| 39 |
+
- squinky-implicature
|
| 40 |
+
- squinky-informativeness
|
| 41 |
+
- stac
|
| 42 |
+
- switchboard
|
| 43 |
+
- verifiability
|
| 44 |
+
dataset_info:
|
| 45 |
+
- config_name: verifiability
|
| 46 |
+
features:
|
| 47 |
+
- name: sentence
|
| 48 |
+
dtype: string
|
| 49 |
+
- name: label
|
| 50 |
+
dtype:
|
| 51 |
+
class_label:
|
| 52 |
+
names:
|
| 53 |
+
'0': experiential
|
| 54 |
+
'1': unverifiable
|
| 55 |
+
'2': non-experiential
|
| 56 |
+
- name: idx
|
| 57 |
+
dtype: int32
|
| 58 |
+
splits:
|
| 59 |
+
- name: train
|
| 60 |
+
num_bytes: 592520
|
| 61 |
+
num_examples: 5712
|
| 62 |
+
- name: validation
|
| 63 |
+
num_bytes: 65215
|
| 64 |
+
num_examples: 634
|
| 65 |
+
- name: test
|
| 66 |
+
num_bytes: 251799
|
| 67 |
+
num_examples: 2424
|
| 68 |
+
download_size: 5330724
|
| 69 |
+
dataset_size: 909534
|
| 70 |
+
- config_name: emobank-arousal
|
| 71 |
+
features:
|
| 72 |
+
- name: sentence
|
| 73 |
+
dtype: string
|
| 74 |
+
- name: label
|
| 75 |
+
dtype:
|
| 76 |
+
class_label:
|
| 77 |
+
names:
|
| 78 |
+
'0': low
|
| 79 |
+
'1': high
|
| 80 |
+
- name: idx
|
| 81 |
+
dtype: int32
|
| 82 |
+
splits:
|
| 83 |
+
- name: train
|
| 84 |
+
num_bytes: 567660
|
| 85 |
+
num_examples: 5470
|
| 86 |
+
- name: validation
|
| 87 |
+
num_bytes: 71221
|
| 88 |
+
num_examples: 684
|
| 89 |
+
- name: test
|
| 90 |
+
num_bytes: 69276
|
| 91 |
+
num_examples: 683
|
| 92 |
+
download_size: 5330724
|
| 93 |
+
dataset_size: 708157
|
| 94 |
+
- config_name: switchboard
|
| 95 |
+
features:
|
| 96 |
+
- name: sentence
|
| 97 |
+
dtype: string
|
| 98 |
+
- name: label
|
| 99 |
+
dtype:
|
| 100 |
+
class_label:
|
| 101 |
+
names:
|
| 102 |
+
'0': Response Acknowledgement
|
| 103 |
+
'1': Uninterpretable
|
| 104 |
+
'2': Or-Clause
|
| 105 |
+
'3': Reject
|
| 106 |
+
'4': Statement-non-opinion
|
| 107 |
+
'5': 3rd-party-talk
|
| 108 |
+
'6': Repeat-phrase
|
| 109 |
+
'7': Hold Before Answer/Agreement
|
| 110 |
+
'8': Signal-non-understanding
|
| 111 |
+
'9': Offers, Options Commits
|
| 112 |
+
'10': Agree/Accept
|
| 113 |
+
'11': Dispreferred Answers
|
| 114 |
+
'12': Hedge
|
| 115 |
+
'13': Action-directive
|
| 116 |
+
'14': Tag-Question
|
| 117 |
+
'15': Self-talk
|
| 118 |
+
'16': Yes-No-Question
|
| 119 |
+
'17': Rhetorical-Question
|
| 120 |
+
'18': No Answers
|
| 121 |
+
'19': Open-Question
|
| 122 |
+
'20': Conventional-closing
|
| 123 |
+
'21': Other Answers
|
| 124 |
+
'22': Acknowledge (Backchannel)
|
| 125 |
+
'23': Wh-Question
|
| 126 |
+
'24': Declarative Wh-Question
|
| 127 |
+
'25': Thanking
|
| 128 |
+
'26': Yes Answers
|
| 129 |
+
'27': Affirmative Non-yes Answers
|
| 130 |
+
'28': Declarative Yes-No-Question
|
| 131 |
+
'29': Backchannel in Question Form
|
| 132 |
+
'30': Apology
|
| 133 |
+
'31': Downplayer
|
| 134 |
+
'32': Conventional-opening
|
| 135 |
+
'33': Collaborative Completion
|
| 136 |
+
'34': Summarize/Reformulate
|
| 137 |
+
'35': Negative Non-no Answers
|
| 138 |
+
'36': Statement-opinion
|
| 139 |
+
'37': Appreciation
|
| 140 |
+
'38': Other
|
| 141 |
+
'39': Quotation
|
| 142 |
+
'40': Maybe/Accept-part
|
| 143 |
+
- name: idx
|
| 144 |
+
dtype: int32
|
| 145 |
+
splits:
|
| 146 |
+
- name: train
|
| 147 |
+
num_bytes: 1021220
|
| 148 |
+
num_examples: 18930
|
| 149 |
+
- name: validation
|
| 150 |
+
num_bytes: 116058
|
| 151 |
+
num_examples: 2113
|
| 152 |
+
- name: test
|
| 153 |
+
num_bytes: 34013
|
| 154 |
+
num_examples: 649
|
| 155 |
+
download_size: 5330724
|
| 156 |
+
dataset_size: 1171291
|
| 157 |
+
- config_name: persuasiveness-eloquence
|
| 158 |
+
features:
|
| 159 |
+
- name: sentence1
|
| 160 |
+
dtype: string
|
| 161 |
+
- name: sentence2
|
| 162 |
+
dtype: string
|
| 163 |
+
- name: label
|
| 164 |
+
dtype:
|
| 165 |
+
class_label:
|
| 166 |
+
names:
|
| 167 |
+
'0': low
|
| 168 |
+
'1': high
|
| 169 |
+
- name: idx
|
| 170 |
+
dtype: int32
|
| 171 |
+
splits:
|
| 172 |
+
- name: train
|
| 173 |
+
num_bytes: 153946
|
| 174 |
+
num_examples: 725
|
| 175 |
+
- name: validation
|
| 176 |
+
num_bytes: 19376
|
| 177 |
+
num_examples: 91
|
| 178 |
+
- name: test
|
| 179 |
+
num_bytes: 18379
|
| 180 |
+
num_examples: 90
|
| 181 |
+
download_size: 5330724
|
| 182 |
+
dataset_size: 191701
|
| 183 |
+
- config_name: mrda
|
| 184 |
+
features:
|
| 185 |
+
- name: sentence
|
| 186 |
+
dtype: string
|
| 187 |
+
- name: label
|
| 188 |
+
dtype:
|
| 189 |
+
class_label:
|
| 190 |
+
names:
|
| 191 |
+
'0': Declarative-Question
|
| 192 |
+
'1': Statement
|
| 193 |
+
'2': Reject
|
| 194 |
+
'3': Or-Clause
|
| 195 |
+
'4': 3rd-party-talk
|
| 196 |
+
'5': Continuer
|
| 197 |
+
'6': Hold Before Answer/Agreement
|
| 198 |
+
'7': Assessment/Appreciation
|
| 199 |
+
'8': Signal-non-understanding
|
| 200 |
+
'9': Floor Holder
|
| 201 |
+
'10': Sympathy
|
| 202 |
+
'11': Dispreferred Answers
|
| 203 |
+
'12': Reformulate/Summarize
|
| 204 |
+
'13': Exclamation
|
| 205 |
+
'14': Interrupted/Abandoned/Uninterpretable
|
| 206 |
+
'15': Expansions of y/n Answers
|
| 207 |
+
'16': Action-directive
|
| 208 |
+
'17': Tag-Question
|
| 209 |
+
'18': Accept
|
| 210 |
+
'19': Rhetorical-question Continue
|
| 211 |
+
'20': Self-talk
|
| 212 |
+
'21': Rhetorical-Question
|
| 213 |
+
'22': Yes-No-question
|
| 214 |
+
'23': Open-Question
|
| 215 |
+
'24': Rising Tone
|
| 216 |
+
'25': Other Answers
|
| 217 |
+
'26': Commit
|
| 218 |
+
'27': Wh-Question
|
| 219 |
+
'28': Repeat
|
| 220 |
+
'29': Follow Me
|
| 221 |
+
'30': Thanking
|
| 222 |
+
'31': Offer
|
| 223 |
+
'32': About-task
|
| 224 |
+
'33': Reject-part
|
| 225 |
+
'34': Affirmative Non-yes Answers
|
| 226 |
+
'35': Apology
|
| 227 |
+
'36': Downplayer
|
| 228 |
+
'37': Humorous Material
|
| 229 |
+
'38': Accept-part
|
| 230 |
+
'39': Collaborative Completion
|
| 231 |
+
'40': Mimic Other
|
| 232 |
+
'41': Understanding Check
|
| 233 |
+
'42': Misspeak Self-Correction
|
| 234 |
+
'43': Or-Question
|
| 235 |
+
'44': Topic Change
|
| 236 |
+
'45': Negative Non-no Answers
|
| 237 |
+
'46': Floor Grabber
|
| 238 |
+
'47': Correct-misspeaking
|
| 239 |
+
'48': Maybe
|
| 240 |
+
'49': Acknowledge-answer
|
| 241 |
+
'50': Defending/Explanation
|
| 242 |
+
- name: idx
|
| 243 |
+
dtype: int32
|
| 244 |
+
splits:
|
| 245 |
+
- name: train
|
| 246 |
+
num_bytes: 963913
|
| 247 |
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num_examples: 14484
|
| 248 |
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- name: validation
|
| 249 |
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num_bytes: 111813
|
| 250 |
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num_examples: 1630
|
| 251 |
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- name: test
|
| 252 |
+
num_bytes: 419797
|
| 253 |
+
num_examples: 6459
|
| 254 |
+
download_size: 5330724
|
| 255 |
+
dataset_size: 1495523
|
| 256 |
+
- config_name: gum
|
| 257 |
+
features:
|
| 258 |
+
- name: sentence1
|
| 259 |
+
dtype: string
|
| 260 |
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|
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|
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|
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|
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|
| 265 |
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|
| 266 |
+
'0': preparation
|
| 267 |
+
'1': evaluation
|
| 268 |
+
'2': circumstance
|
| 269 |
+
'3': solutionhood
|
| 270 |
+
'4': justify
|
| 271 |
+
'5': result
|
| 272 |
+
'6': evidence
|
| 273 |
+
'7': purpose
|
| 274 |
+
'8': concession
|
| 275 |
+
'9': elaboration
|
| 276 |
+
'10': background
|
| 277 |
+
'11': condition
|
| 278 |
+
'12': cause
|
| 279 |
+
'13': restatement
|
| 280 |
+
'14': motivation
|
| 281 |
+
'15': antithesis
|
| 282 |
+
'16': no_relation
|
| 283 |
+
- name: idx
|
| 284 |
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dtype: int32
|
| 285 |
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splits:
|
| 286 |
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|
| 287 |
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num_bytes: 270401
|
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|
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|
| 292 |
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|
| 293 |
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|
| 294 |
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num_examples: 248
|
| 295 |
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download_size: 5330724
|
| 296 |
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dataset_size: 346140
|
| 297 |
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- config_name: emergent
|
| 298 |
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|
| 299 |
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|
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|
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|
| 306 |
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|
| 307 |
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|
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|
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|
| 310 |
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|
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|
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|
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|
| 322 |
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download_size: 5330724
|
| 323 |
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dataset_size: 391047
|
| 324 |
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- config_name: persuasiveness-relevance
|
| 325 |
+
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|
| 326 |
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|
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|
| 348 |
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download_size: 5330724
|
| 349 |
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|
| 350 |
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|
| 351 |
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|
| 352 |
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|
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'11': Correction
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'4': List
|
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'5': Condition
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'6': Pragmatic concession
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'8': Pragmatic cause
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'3': common_knowledge
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'5': analogy
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'7': real_example
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---
|
| 690 |
+
|
| 691 |
+
# Dataset Card for pragmeval
|
| 692 |
+
|
| 693 |
+
## Table of Contents
|
| 694 |
+
- [Table of Contents](#table-of-contents)
|
| 695 |
+
- [Dataset Description](#dataset-description)
|
| 696 |
+
- [Dataset Summary](#dataset-summary)
|
| 697 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 698 |
+
- [Languages](#languages)
|
| 699 |
+
- [Dataset Structure](#dataset-structure)
|
| 700 |
+
- [Data Instances](#data-instances)
|
| 701 |
+
- [Data Fields](#data-fields)
|
| 702 |
+
- [Data Splits](#data-splits)
|
| 703 |
+
- [Dataset Creation](#dataset-creation)
|
| 704 |
+
- [Curation Rationale](#curation-rationale)
|
| 705 |
+
- [Source Data](#source-data)
|
| 706 |
+
- [Annotations](#annotations)
|
| 707 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 708 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 709 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 710 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 711 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 712 |
+
- [Additional Information](#additional-information)
|
| 713 |
+
- [Dataset Curators](#dataset-curators)
|
| 714 |
+
- [Licensing Information](#licensing-information)
|
| 715 |
+
- [Citation Information](#citation-information)
|
| 716 |
+
- [Contributions](#contributions)
|
| 717 |
+
|
| 718 |
+
## Dataset Description
|
| 719 |
+
|
| 720 |
+
- **Homepage:**
|
| 721 |
+
- **Repository:**
|
| 722 |
+
- **Paper:**
|
| 723 |
+
- **Leaderboard:**
|
| 724 |
+
- **Point of Contact:**
|
| 725 |
+
|
| 726 |
+
### Dataset Summary
|
| 727 |
+
|
| 728 |
+
[More Information Needed]
|
| 729 |
+
|
| 730 |
+
### Supported Tasks and Leaderboards
|
| 731 |
+
|
| 732 |
+
[More Information Needed]
|
| 733 |
+
|
| 734 |
+
### Languages
|
| 735 |
+
|
| 736 |
+
[More Information Needed]
|
| 737 |
+
|
| 738 |
+
## Dataset Structure
|
| 739 |
+
|
| 740 |
+
### Data Instances
|
| 741 |
+
|
| 742 |
+
[More Information Needed]
|
| 743 |
+
|
| 744 |
+
### Data Fields
|
| 745 |
+
|
| 746 |
+
[More Information Needed]
|
| 747 |
+
|
| 748 |
+
### Data Splits
|
| 749 |
+
|
| 750 |
+
[More Information Needed]
|
| 751 |
+
|
| 752 |
+
## Dataset Creation
|
| 753 |
+
|
| 754 |
+
### Curation Rationale
|
| 755 |
+
|
| 756 |
+
[More Information Needed]
|
| 757 |
+
|
| 758 |
+
### Source Data
|
| 759 |
+
|
| 760 |
+
#### Initial Data Collection and Normalization
|
| 761 |
+
|
| 762 |
+
[More Information Needed]
|
| 763 |
+
|
| 764 |
+
#### Who are the source language producers?
|
| 765 |
+
|
| 766 |
+
[More Information Needed]
|
| 767 |
+
|
| 768 |
+
### Annotations
|
| 769 |
+
|
| 770 |
+
#### Annotation process
|
| 771 |
+
|
| 772 |
+
[More Information Needed]
|
| 773 |
+
|
| 774 |
+
#### Who are the annotators?
|
| 775 |
+
|
| 776 |
+
[More Information Needed]
|
| 777 |
+
|
| 778 |
+
### Personal and Sensitive Information
|
| 779 |
+
|
| 780 |
+
[More Information Needed]
|
| 781 |
+
|
| 782 |
+
## Considerations for Using the Data
|
| 783 |
+
|
| 784 |
+
### Social Impact of Dataset
|
| 785 |
+
|
| 786 |
+
[More Information Needed]
|
| 787 |
+
|
| 788 |
+
### Discussion of Biases
|
| 789 |
+
|
| 790 |
+
[More Information Needed]
|
| 791 |
+
|
| 792 |
+
### Other Known Limitations
|
| 793 |
+
|
| 794 |
+
[More Information Needed]
|
| 795 |
+
|
| 796 |
+
## Additional Information
|
| 797 |
+
|
| 798 |
+
### Dataset Curators
|
| 799 |
+
|
| 800 |
+
[More Information Needed]
|
| 801 |
+
|
| 802 |
+
### Licensing Information
|
| 803 |
+
|
| 804 |
+
[More Information Needed]
|
| 805 |
+
|
| 806 |
+
### Citation Information
|
| 807 |
+
|
| 808 |
+
[More Information Needed]
|
| 809 |
+
|
| 810 |
+
### Contributions
|
| 811 |
+
|
| 812 |
+
Thanks to [@sileod](https://github.com/sileod) for adding this dataset.
|
huggingface_dataset/Dataset_Card/qa4pc_QA4PC.md
ADDED
|
@@ -0,0 +1,25 @@
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
## QA4PC Dataset (paper: Cross-Policy Compliance Detection via Question Answering)
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
### Train Sets
|
| 5 |
+
To create training set or entailment and QA tasks, download and convert the ShARC data using the following commands:
|
| 6 |
+
```
|
| 7 |
+
wget https://sharc-data.github.io/data/sharc1-official.zip
|
| 8 |
+
unzip sharc1-official.zip
|
| 9 |
+
python create_train_from_sharc.py -sharc_dev_path sharc1-official/json/sharc_dev.json -sharc_train_path sharc1-official/json/sharc_train.json
|
| 10 |
+
```
|
| 11 |
+
|
| 12 |
+
### Evaluation Sets
|
| 13 |
+
|
| 14 |
+
#### Entailment Data
|
| 15 |
+
The following files contain the data for the entailment task. This includes the policy + questions, a scenario and an answer (_Yes, No, Maybe_). Each datapoint also contain the information from the ShARC dataset such as tree_id and source_url.
|
| 16 |
+
- __dev_entailment_qa4pc.json__
|
| 17 |
+
- __test_entailment_qa4pc.json__
|
| 18 |
+
|
| 19 |
+
#### QA Data
|
| 20 |
+
The following files contain the data for the QA task.
|
| 21 |
+
- __dev_sc_qa4pc.json__
|
| 22 |
+
- __test_sc_qa4pc.json__
|
| 23 |
+
|
| 24 |
+
The following file contains the expression tree data for the dev and test sets. Each tree includes a policy, a set of questions and a logical expression.
|
| 25 |
+
- __trees_dev_test_qa4pc.json__
|