Upload batch 228 (20 files, last=huggingface_dataset/Dataset_Card/Cropinky_rap_lyrics_english.md)
Browse files- huggingface_dataset/Dataset_Card/12ml_e-CARE.md +19 -0
- huggingface_dataset/Dataset_Card/Cohere_miracl-sw-queries-22-12.md +152 -0
- huggingface_dataset/Dataset_Card/Cropinky_rap_lyrics_english.md +4 -0
- huggingface_dataset/Dataset_Card/Kirili4ik_yandex_jobs.md +135 -0
- huggingface_dataset/Dataset_Card/RUCAIBox_Commonsense-Generation.md +16 -0
- huggingface_dataset/Dataset_Card/ZIZOU_Arabic_Squad.md +84 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampletx-toxic-7252ee-1708159804.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-quoref-9c01ff03-1305849901.md +35 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-6fbfec76-7855034.md +31 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-kmfoda__booksum-5034faac-10965473.md +33 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-samsum-0c672345-10275363.md +31 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad-4690f1f9-13895911.md +35 -0
- huggingface_dataset/Dataset_Card/biu-nlp_alsqa.md +187 -0
- huggingface_dataset/Dataset_Card/flexthink_ljspeech.md +166 -0
- huggingface_dataset/Dataset_Card/income_quora-top-20-gen-queries.md +510 -0
- huggingface_dataset/Dataset_Card/keremberke_garbage-object-detection.md +61 -0
- huggingface_dataset/Dataset_Card/philschmid_emotion.md +279 -0
- huggingface_dataset/Dataset_Card/qasc.md +225 -0
- huggingface_dataset/Dataset_Card/smartdata.md +223 -0
- huggingface_dataset/Dataset_Card/vector_test_demo.md +439 -0
huggingface_dataset/Dataset_Card/12ml_e-CARE.md
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---
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task_categories:
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- multiple-choice
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---
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# Dataset of (Du et al., 2022)
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## Abstract
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>Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal fact to facilitate the causal reasoning process. However, such explanation information still remains absent in existing causal reasoning resources. In this paper, we fill this gap by presenting a human-annotated explainable CAusal REasoning dataset (e-CARE), which contains over 20K causal reasoning questions, together with natural language formed explanations of the causal questions. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.
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## Notes
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Please note that the original dataset has been modified so that the variable names match with those in the COPA dataset (Roemmele et al., 2011). In addition, only the training and the development sets are [publicly available](https://github.com/waste-wood/e-care).
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## References
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Du, L., Ding, X., Xiong, K., Liu, T., & Qin, B. (2022). e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. arXiv preprint arXiv:2205.05849.
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Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.
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huggingface_dataset/Dataset_Card/Cohere_miracl-sw-queries-22-12.md
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---
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annotations_creators:
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- expert-generated
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language:
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- sw
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multilinguality:
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- multilingual
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size_categories: []
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source_datasets: []
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tags: []
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task_categories:
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- text-retrieval
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license:
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- apache-2.0
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task_ids:
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- document-retrieval
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---
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# MIRACL (sw) embedded with cohere.ai `multilingual-22-12` encoder
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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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The query embeddings can be found in [Cohere/miracl-sw-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-queries-22-12) and the corpus embeddings can be found in [Cohere/miracl-sw-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-corpus-22-12).
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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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Dataset info:
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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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> 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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## Embeddings
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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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## Loading the dataset
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In [miracl-sw-corpus-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-corpus-22-12) we provide the corpus embeddings. Note, depending on the selected split, the respective files can be quite large.
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You can either load the dataset like this:
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```python
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from datasets import load_dataset
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docs = load_dataset(f"Cohere/miracl-sw-corpus-22-12", split="train")
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```
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Or you can also stream it without downloading it before:
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```python
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from datasets import load_dataset
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docs = load_dataset(f"Cohere/miracl-sw-corpus-22-12", split="train", streaming=True)
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for doc in docs:
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docid = doc['docid']
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title = doc['title']
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text = doc['text']
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emb = doc['emb']
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```
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## Search
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Have a look at [miracl-sw-queries-22-12](https://huggingface.co/datasets/Cohere/miracl-sw-queries-22-12) where we provide the query embeddings for the MIRACL dataset.
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To search in the documents, you must use **dot-product**.
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And then compare this query embeddings either with a vector database (recommended) or directly computing the dot product.
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A full search example:
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```python
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# Attention! For large datasets, this requires a lot of memory to store
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# all document embeddings and to compute the dot product scores.
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# Only use this for smaller datasets. For large datasets, use a vector DB
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from datasets import load_dataset
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import torch
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#Load documents + embeddings
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docs = load_dataset(f"Cohere/miracl-sw-corpus-22-12", split="train")
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doc_embeddings = torch.tensor(docs['emb'])
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# Load queries
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queries = load_dataset(f"Cohere/miracl-sw-queries-22-12", split="dev")
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# Select the first query as example
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qid = 0
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query = queries[qid]
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query_embedding = torch.tensor(queries['emb'])
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# Compute dot score between query embedding and document embeddings
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dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
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top_k = torch.topk(dot_scores, k=3)
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# Print results
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print("Query:", query['query'])
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for doc_id in top_k.indices[0].tolist():
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print(docs[doc_id]['title'])
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print(docs[doc_id]['text'])
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```
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You can get embeddings for new queries using our API:
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```python
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#Run: pip install cohere
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import cohere
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co = cohere.Client(f"{api_key}") # You should add your cohere API Key here :))
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texts = ['my search query']
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response = co.embed(texts=texts, model='multilingual-22-12')
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query_embedding = response.embeddings[0] # Get the embedding for the first text
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```
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## Performance
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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.
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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.
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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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| 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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|---|---|---|---|---|
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| miracl-ar | 64.2 | 75.2 | 46.8 | 56.2 |
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| miracl-bn | 61.5 | 75.7 | 49.2 | 60.1 |
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| miracl-de | 44.4 | 60.7 | 19.6 | 29.8 |
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| miracl-en | 44.6 | 62.2 | 30.2 | 43.2 |
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| miracl-es | 47.0 | 74.1 | 27.0 | 47.2 |
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| miracl-fi | 63.7 | 76.2 | 51.4 | 61.6 |
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| miracl-fr | 46.8 | 57.1 | 17.0 | 21.6 |
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| miracl-hi | 50.7 | 62.9 | 41.0 | 48.9 |
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| miracl-id | 44.8 | 63.8 | 39.2 | 54.7 |
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| miracl-ru | 49.2 | 66.9 | 25.4 | 36.7 |
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| **Avg** | 51.7 | 67.5 | 34.7 | 46.0 |
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Further languages (not supported by Elasticsearch):
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| Model | cohere multilingual-22-12 nDCG@10 | cohere multilingual-22-12 hit@3 |
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|---|---|---|
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| miracl-fa | 44.8 | 53.6 |
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| miracl-ja | 49.0 | 61.0 |
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| miracl-ko | 50.9 | 64.8 |
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| miracl-sw | 61.4 | 74.5 |
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| miracl-te | 67.8 | 72.3 |
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| miracl-th | 60.2 | 71.9 |
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| miracl-yo | 56.4 | 62.2 |
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| miracl-zh | 43.8 | 56.5 |
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| **Avg** | 54.3 | 64.6 |
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huggingface_dataset/Dataset_Card/Cropinky_rap_lyrics_english.md
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## Rap lyrics dataset
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this is the repo containing the dataset we made for the hugging face community week, in order to download more songs you need to request and get(it's very simple and fast) your genius API key which ou put in the genius.py file<br/>
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#TODO: turn it into an actual huggingface dataset
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huggingface_dataset/Dataset_Card/Kirili4ik_yandex_jobs.md
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language:
|
| 5 |
+
- ru
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
license:
|
| 9 |
+
- unknown
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
paperswithcode_id: climate-fever
|
| 13 |
+
pretty_name: yandex_jobs
|
| 14 |
+
size_categories:
|
| 15 |
+
- n<1K
|
| 16 |
+
source_datasets:
|
| 17 |
+
- original
|
| 18 |
+
tags:
|
| 19 |
+
- vacancies
|
| 20 |
+
- jobs
|
| 21 |
+
- ru
|
| 22 |
+
- yandex
|
| 23 |
+
task_categories:
|
| 24 |
+
- text-generation
|
| 25 |
+
- summarization
|
| 26 |
+
- multiple-choice
|
| 27 |
+
task_ids:
|
| 28 |
+
- language-modeling
|
| 29 |
+
---
|
| 30 |
+
|
| 31 |
+
# Dataset Card for Yandex_Jobs
|
| 32 |
+
|
| 33 |
+
## Table of Contents
|
| 34 |
+
- [Dataset Description](#dataset-description)
|
| 35 |
+
- [Dataset Summary](#dataset-summary)
|
| 36 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 37 |
+
- [Languages](#languages)
|
| 38 |
+
- [Dataset Structure](#dataset-structure)
|
| 39 |
+
- [Data Instances](#data-instances)
|
| 40 |
+
- [Data Fields](#data-fields)
|
| 41 |
+
- [Data Splits](#data-splits)
|
| 42 |
+
- [Dataset Creation](#dataset-creation)
|
| 43 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 44 |
+
- [Contributions](#contributions)
|
| 45 |
+
|
| 46 |
+
## Dataset Description
|
| 47 |
+
|
| 48 |
+
### Dataset Summary
|
| 49 |
+
|
| 50 |
+
This is a dataset of more than 600 IT vacancies in Russian from parsing telegram channel https://t.me/ya_jobs. All the texts are perfectly structured, no missing values.
|
| 51 |
+
|
| 52 |
+
### Supported Tasks and Leaderboards
|
| 53 |
+
|
| 54 |
+
`text-generation` with the 'Raw text column'.
|
| 55 |
+
|
| 56 |
+
`summarization` as for getting from all the info the header.
|
| 57 |
+
|
| 58 |
+
`multiple-choice` as for the hashtags (to choose multiple from all available in the dataset)
|
| 59 |
+
|
| 60 |
+
### Languages
|
| 61 |
+
|
| 62 |
+
The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`.
|
| 63 |
+
## Dataset Structure
|
| 64 |
+
|
| 65 |
+
### Data Instances
|
| 66 |
+
|
| 67 |
+
The data is parsed from a vacancy of Russian IT company [Yandex](https://ya.ru/).
|
| 68 |
+
|
| 69 |
+
An example from the set looks as follows:
|
| 70 |
+
```
|
| 71 |
+
{'Header': 'Разработчик интерфейсов в группу разработки спецпроектов',
|
| 72 |
+
'Emoji': '🎳',
|
| 73 |
+
'Description': 'Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.\nМы ищем опытного и открытого новому фронтенд-разработчика.',
|
| 74 |
+
'Requirements': '• отлично знаете JavaScript
|
| 75 |
+
• разрабатывали на Node.js, применяли фреймворк Express
|
| 76 |
+
• умеете создавать веб-приложения на React + Redux
|
| 77 |
+
• знаете HTML и CSS, особенности их отображения в браузерах',
|
| 78 |
+
'Tasks': '• разрабатывать интерфейсы',
|
| 79 |
+
'Pluses': '• писали интеграционные, модульные, функциональные или браузерные тесты
|
| 80 |
+
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
|
| 81 |
+
• работали с реляционными БД PostgreSQL',
|
| 82 |
+
'Hashtags': '#фронтенд #турбо #JS',
|
| 83 |
+
'Link': 'https://ya.cc/t/t7E3UsmVSKs6L',
|
| 84 |
+
'Raw text': 'Разработчик интерфейсов в группу разработки спецпроектов🎳
|
| 85 |
+
|
| 86 |
+
Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.
|
| 87 |
+
Мы ищем опытного и открытого новому фронтенд-разработчика.
|
| 88 |
+
|
| 89 |
+
Мы ждем, что вы:
|
| 90 |
+
• отлично знаете JavaScript
|
| 91 |
+
• разрабатывали на Node.js, применяли фреймворк Express
|
| 92 |
+
• умеете создавать веб-приложения на React + Redux
|
| 93 |
+
• знаете HTML и CSS, особенности их отображения в браузерах
|
| 94 |
+
|
| 95 |
+
Что нужно делать:
|
| 96 |
+
• разрабатывать интерфейсы
|
| 97 |
+
|
| 98 |
+
Будет плюсом, если вы:
|
| 99 |
+
• писали интеграционные, модульные, функциональные или браузерные тесты
|
| 100 |
+
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
|
| 101 |
+
• работали с реляционными БД PostgreSQL
|
| 102 |
+
|
| 103 |
+
https://ya.cc/t/t7E3UsmVSKs6L
|
| 104 |
+
|
| 105 |
+
#фронтенд #турбо #JS'
|
| 106 |
+
}
|
| 107 |
+
```
|
| 108 |
+
|
| 109 |
+
### Data Fields
|
| 110 |
+
|
| 111 |
+
- `Header`: A string with a position title (str)
|
| 112 |
+
- `Emoji`: Emoji that is used at the end of the title position (usually asosiated with the position) (str)
|
| 113 |
+
- `Description`: Short description of the vacancy (str)
|
| 114 |
+
- `Requirements`: A couple of required technologies/programming languages/experience (str)
|
| 115 |
+
- `Tasks`: Examples of the tasks of the job position (str)
|
| 116 |
+
- `Pluses`: A couple of great points for the applicant to have (technologies/experience/etc)
|
| 117 |
+
- `Hashtags`: A list of hashtags assosiated with the job (usually programming languages) (str)
|
| 118 |
+
- `Link`: A link to a job description (there may be more information, but it is not checked) (str)
|
| 119 |
+
- `Raw text`: Raw text with all the formatiing from the channel. Created with other fields. (str)
|
| 120 |
+
|
| 121 |
+
### Data Splits
|
| 122 |
+
|
| 123 |
+
There is not enough examples yet to split it to train/test/val in my opinion.
|
| 124 |
+
|
| 125 |
+
## Dataset Creation
|
| 126 |
+
|
| 127 |
+
It downloaded and parsed from telegram channel https://t.me/ya_jobs 03.09.2022. All the unparsed examples and the ones missing any field are deleted (from 1600 vacancies to only 600 without any missing fields like emojis or links)
|
| 128 |
+
|
| 129 |
+
## Considerations for Using the Data
|
| 130 |
+
|
| 131 |
+
These vacancies are for only one IT company (yandex). This means they can be pretty specific and probably can not be generalized as any vacancies or even any IT vacancies.
|
| 132 |
+
|
| 133 |
+
## Contributions
|
| 134 |
+
|
| 135 |
+
- **Point of Contact and Author:** [Kirill Gelvan](telegram: @kirili4ik)
|
huggingface_dataset/Dataset_Card/RUCAIBox_Commonsense-Generation.md
ADDED
|
@@ -0,0 +1,16 @@
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|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
multilinguality:
|
| 5 |
+
- monolingual
|
| 6 |
+
task_categories:
|
| 7 |
+
- other
|
| 8 |
+
task_ids: []
|
| 9 |
+
tags:
|
| 10 |
+
- commonsense-generation
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
This is the commonsense generation datasets collected by TextBox, including:
|
| 14 |
+
- CommonGen (cg).
|
| 15 |
+
|
| 16 |
+
The detail and leaderboard of each dataset can be found in [TextBox page](https://github.com/RUCAIBox/TextBox#dataset).
|
huggingface_dataset/Dataset_Card/ZIZOU_Arabic_Squad.md
ADDED
|
@@ -0,0 +1,84 @@
|
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|
|
| 1 |
+
Hugging Face's logo
|
| 2 |
+
Hugging Face
|
| 3 |
+
Search models, datasets, users...
|
| 4 |
+
Models
|
| 5 |
+
Datasets
|
| 6 |
+
Spaces
|
| 7 |
+
Docs
|
| 8 |
+
Solutions
|
| 9 |
+
Pricing
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
Hugging Face is way more fun with friends and colleagues! 🤗 Join an organization
|
| 14 |
+
Datasets:
|
| 15 |
+
|
| 16 |
+
Mostafa3zazi
|
| 17 |
+
/
|
| 18 |
+
Arabic_SQuAD Copied
|
| 19 |
+
like
|
| 20 |
+
0
|
| 21 |
+
Dataset card
|
| 22 |
+
Files and versions
|
| 23 |
+
Community
|
| 24 |
+
Arabic_SQuAD
|
| 25 |
+
/
|
| 26 |
+
README.md
|
| 27 |
+
Mostafa3zazi's picture
|
| 28 |
+
Mostafa3zazi
|
| 29 |
+
Update README.md
|
| 30 |
+
17d5b9d
|
| 31 |
+
19 days ago
|
| 32 |
+
raw
|
| 33 |
+
history
|
| 34 |
+
blame
|
| 35 |
+
contribute
|
| 36 |
+
delete
|
| 37 |
+
Safe
|
| 38 |
+
2.18 kB
|
| 39 |
+
---
|
| 40 |
+
dataset_info:
|
| 41 |
+
features:
|
| 42 |
+
- name: index
|
| 43 |
+
dtype: string
|
| 44 |
+
- name: question
|
| 45 |
+
dtype: string
|
| 46 |
+
- name: context
|
| 47 |
+
dtype: string
|
| 48 |
+
- name: text
|
| 49 |
+
dtype: string
|
| 50 |
+
- name: answer_start
|
| 51 |
+
dtype: int64
|
| 52 |
+
- name: c_id
|
| 53 |
+
dtype: int64
|
| 54 |
+
splits:
|
| 55 |
+
- name: train
|
| 56 |
+
num_bytes: 61868003
|
| 57 |
+
num_examples: 48344
|
| 58 |
+
download_size: 10512179
|
| 59 |
+
dataset_size: 61868003
|
| 60 |
+
---
|
| 61 |
+
# Dataset Card for "Arabic_SQuAD"
|
| 62 |
+
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 63 |
+
|
| 64 |
+
---
|
| 65 |
+
# Citation
|
| 66 |
+
```
|
| 67 |
+
@inproceedings{mozannar-etal-2019-neural,
|
| 68 |
+
title = "Neural {A}rabic Question Answering",
|
| 69 |
+
author = "Mozannar, Hussein and
|
| 70 |
+
Maamary, Elie and
|
| 71 |
+
El Hajal, Karl and
|
| 72 |
+
Hajj, Hazem",
|
| 73 |
+
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
|
| 74 |
+
month = aug,
|
| 75 |
+
year = "2019",
|
| 76 |
+
address = "Florence, Italy",
|
| 77 |
+
publisher = "Association for Computational Linguistics",
|
| 78 |
+
url = "https://www.aclweb.org/anthology/W19-4612",
|
| 79 |
+
doi = "10.18653/v1/W19-4612",
|
| 80 |
+
pages = "108--118",
|
| 81 |
+
abstract = "This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA for Arabic entails three challenges: annotated QA datasets in Arabic, large scale efficient information retrieval and machine reading comprehension. To deal with the lack of Arabic QA datasets we present the Arabic Reading Comprehension Dataset (ARCD) composed of 1,395 questions posed by crowdworkers on Wikipedia articles, and a machine translation of the Stanford Question Answering Dataset (Arabic-SQuAD). Our system for open domain question answering in Arabic (SOQAL) is based on two components: (1) a document retriever using a hierarchical TF-IDF approach and (2) a neural reading comprehension model using the pre-trained bi-directional transformer BERT. Our experiments on ARCD indicate the effectiveness of our approach with our BERT-based reader achieving a 61.3 F1 score, and our open domain system SOQAL achieving a 27.6 F1 score.",
|
| 82 |
+
}
|
| 83 |
+
```
|
| 84 |
+
---
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampletx-toxic-7252ee-1708159804.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- phpthinh/exampletx
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: bigscience/bloom-1b7
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: phpthinh/exampletx
|
| 13 |
+
dataset_config: toxic
|
| 14 |
+
dataset_split: test
|
| 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: bigscience/bloom-1b7
|
| 26 |
+
* Dataset: phpthinh/exampletx
|
| 27 |
+
* Config: toxic
|
| 28 |
+
* Split: test
|
| 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 [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-project-quoref-9c01ff03-1305849901.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- quoref
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: nbroad/rob-base-superqa1
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: quoref
|
| 13 |
+
dataset_config: default
|
| 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-superqa1
|
| 27 |
+
* Dataset: quoref
|
| 28 |
+
* Config: default
|
| 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-6fbfec76-7855034.md
ADDED
|
@@ -0,0 +1,31 @@
|
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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: henryu-lin/t5-3b-samsum-deepspeed
|
| 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: henryu-lin/t5-3b-samsum-deepspeed
|
| 25 |
+
* Dataset: samsum
|
| 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-kmfoda__booksum-5034faac-10965473.md
ADDED
|
@@ -0,0 +1,33 @@
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- kmfoda/booksum
|
| 8 |
+
eval_info:
|
| 9 |
+
task: summarization
|
| 10 |
+
model: pszemraj/bigbird-pegasus-large-K-booksum
|
| 11 |
+
metrics: ['perplexity']
|
| 12 |
+
dataset_name: kmfoda/booksum
|
| 13 |
+
dataset_config: kmfoda--booksum
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: chapter
|
| 17 |
+
target: summary_text
|
| 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/bigbird-pegasus-large-K-booksum
|
| 25 |
+
* Dataset: kmfoda/booksum
|
| 26 |
+
* Config: kmfoda--booksum
|
| 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/autoevaluate_autoeval-staging-eval-project-samsum-0c672345-10275363.md
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
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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: google/pegasus-cnn_dailymail
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: samsum
|
| 13 |
+
dataset_config: samsum
|
| 14 |
+
dataset_split: train
|
| 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: google/pegasus-cnn_dailymail
|
| 25 |
+
* Dataset: samsum
|
| 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 [@ikadebi](https://huggingface.co/ikadebi) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-project-squad-4690f1f9-13895911.md
ADDED
|
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- squad
|
| 8 |
+
eval_info:
|
| 9 |
+
task: extractive_question_answering
|
| 10 |
+
model: nlpconnect/deberta-v3-xsmall-squad2
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: squad
|
| 13 |
+
dataset_config: plain_text
|
| 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: nlpconnect/deberta-v3-xsmall-squad2
|
| 27 |
+
* Dataset: squad
|
| 28 |
+
* Config: plain_text
|
| 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 [@ankur310794](https://huggingface.co/ankur310794) for evaluating this model.
|
huggingface_dataset/Dataset_Card/biu-nlp_alsqa.md
ADDED
|
@@ -0,0 +1,187 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
pretty_name: ALSQA
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- crowdsourced
|
| 5 |
+
language_creators:
|
| 6 |
+
- crowdsourced
|
| 7 |
+
language:
|
| 8 |
+
- en
|
| 9 |
+
license: apache-2.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- <1000
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- question-answering
|
| 18 |
+
- text-classification
|
| 19 |
+
task_ids:
|
| 20 |
+
- open-domain-qa
|
| 21 |
+
- extractive-qa
|
| 22 |
+
paperswithcode_id: alsqa
|
| 23 |
+
dataset_info:
|
| 24 |
+
features:
|
| 25 |
+
- name: id
|
| 26 |
+
dtype: string
|
| 27 |
+
- name: title
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: context
|
| 30 |
+
dtype: string
|
| 31 |
+
- name: question
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: answers
|
| 34 |
+
sequence:
|
| 35 |
+
- name: text
|
| 36 |
+
dtype: string
|
| 37 |
+
- name: answer_start
|
| 38 |
+
dtype: int32
|
| 39 |
+
config_name: alsqa
|
| 40 |
+
---
|
| 41 |
+
|
| 42 |
+
# Dataset Card for "alsqa"
|
| 43 |
+
|
| 44 |
+
## Table of Contents
|
| 45 |
+
- [Dataset Card for "alsqa"](#dataset-card-for-alsqa)
|
| 46 |
+
- [Table of Contents](#table-of-contents)
|
| 47 |
+
- [Dataset Description](#dataset-description)
|
| 48 |
+
- [Dataset Summary](#dataset-summary)
|
| 49 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 50 |
+
- [Languages](#languages)
|
| 51 |
+
- [Dataset Structure](#dataset-structure)
|
| 52 |
+
- [Data Instances](#data-instances)
|
| 53 |
+
- [squad_v2](#squad_v2)
|
| 54 |
+
- [Data Fields](#data-fields)
|
| 55 |
+
- [squad_v2](#squad_v2-1)
|
| 56 |
+
- [Data Splits](#data-splits)
|
| 57 |
+
- [Dataset Creation](#dataset-creation)
|
| 58 |
+
- [Curation Rationale](#curation-rationale)
|
| 59 |
+
- [Source Data](#source-data)
|
| 60 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 61 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 62 |
+
- [Annotations](#annotations)
|
| 63 |
+
- [Annotation process](#annotation-process)
|
| 64 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 65 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 66 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 67 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 68 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 69 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 70 |
+
- [Additional Information](#additional-information)
|
| 71 |
+
- [Dataset Curators](#dataset-curators)
|
| 72 |
+
- [Licensing Information](#licensing-information)
|
| 73 |
+
- [Citation Information](#citation-information)
|
| 74 |
+
- [Contributions](#contributions)
|
| 75 |
+
|
| 76 |
+
## Dataset Description
|
| 77 |
+
|
| 78 |
+
- **Homepage:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
|
| 79 |
+
- **Repository:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
|
| 80 |
+
- **Paper:** [Lexical Generalization Improves with Larger Models and Longer Training](https://arxiv.org/abs/2210.12673)
|
| 81 |
+
- **Point of Contact:** [https://github.com/elronbandel/lexical-generalization](https://github.com/elronbandel/lexical-generalization)
|
| 82 |
+
- **Size of downloaded dataset files:** 100 KB
|
| 83 |
+
- **Size of the generated dataset:** 1 MB
|
| 84 |
+
- **Total amount of disk used:** 1 MB
|
| 85 |
+
|
| 86 |
+
### Dataset Summary
|
| 87 |
+
|
| 88 |
+
To test the lexical overlap heuristic utilization in Reading Comprehension models, we create a new test set: Analyzing Lexically Similar QA (ALSQA).
|
| 89 |
+
We augment the SQuAD 2.0 dataset (Rajpurkar et al., 2018) by asking crowdworkers to generate questions with high context-overlap from questions with low overlap (These questions are paraphrases of the original questions).
|
| 90 |
+
In the case of un-answerable questions, annotators were asked to re-write the question without changing its meaning and maintain the unanswerability reason.3 ALSQA contains 365 questions pairs, 190 with an- swer and 174 without answer.
|
| 91 |
+
|
| 92 |
+
## Dataset Structure
|
| 93 |
+
|
| 94 |
+
Identical to squad v2
|
| 95 |
+
|
| 96 |
+
#
|
| 97 |
+
### Data Fields
|
| 98 |
+
|
| 99 |
+
The data fields are the same among all splits.
|
| 100 |
+
|
| 101 |
+
#### alsqa
|
| 102 |
+
- `id`: a `string` feature.
|
| 103 |
+
- `title`: a `string` feature.
|
| 104 |
+
- `context`: a `string` feature.
|
| 105 |
+
- `question`: a `string` feature.
|
| 106 |
+
- `answers`: a dictionary feature containing:
|
| 107 |
+
- `text`: a `string` feature.
|
| 108 |
+
- `answer_start`: a `int32` feature.
|
| 109 |
+
|
| 110 |
+
### Data Splits
|
| 111 |
+
|
| 112 |
+
| name | test |
|
| 113 |
+
| -------- | -----: |
|
| 114 |
+
| squad_v2 | 365 |
|
| 115 |
+
|
| 116 |
+
## Dataset Creation
|
| 117 |
+
|
| 118 |
+
### Curation Rationale
|
| 119 |
+
|
| 120 |
+
### Source Data
|
| 121 |
+
squad_v2
|
| 122 |
+
#### Initial Data Collection and Normalization
|
| 123 |
+
|
| 124 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 125 |
+
|
| 126 |
+
#### Who are the source language producers?
|
| 127 |
+
|
| 128 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 129 |
+
|
| 130 |
+
### Annotations
|
| 131 |
+
|
| 132 |
+
#### Annotation process
|
| 133 |
+
|
| 134 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 135 |
+
|
| 136 |
+
#### Who are the annotators?
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
### Personal and Sensitive Information
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
## Considerations for Using the Data
|
| 145 |
+
|
| 146 |
+
### Social Impact of Dataset
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
### Discussion of Biases
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Other Known Limitations
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Additional Information
|
| 159 |
+
|
| 160 |
+
### Dataset Curators
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Licensing Information
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Citation Information
|
| 169 |
+
|
| 170 |
+
```
|
| 171 |
+
@misc{https://doi.org/10.48550/arxiv.2210.12673,
|
| 172 |
+
doi = {10.48550/ARXIV.2210.12673},
|
| 173 |
+
url = {https://arxiv.org/abs/2210.12673},
|
| 174 |
+
author = {Bandel, Elron and Goldberg, Yoav and Elazar, Yanai},
|
| 175 |
+
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
|
| 176 |
+
title = {Lexical Generalization Improves with Larger Models and Longer Training},
|
| 177 |
+
publisher = {arXiv},
|
| 178 |
+
year = {2022},
|
| 179 |
+
copyright = {arXiv.org perpetual, non-exclusive license}
|
| 180 |
+
}
|
| 181 |
+
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
|
| 185 |
+
### Contributions
|
| 186 |
+
|
| 187 |
+
Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset.
|
huggingface_dataset/Dataset_Card/flexthink_ljspeech.md
ADDED
|
@@ -0,0 +1,166 @@
|
|
|
|
|
|
|
|
|
|
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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 |
+
# The LJ Speech Dataset
|
| 2 |
+
|
| 3 |
+
Version 1.0
|
| 4 |
+
July 5, 2017
|
| 5 |
+
https://keithito.com/LJ-Speech-Dataset
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
# Overview
|
| 9 |
+
|
| 10 |
+
This is a public domain speech dataset consisting of 13,100 short audio clips
|
| 11 |
+
of a single speaker reading passages from 7 non-fiction books. A transcription
|
| 12 |
+
is provided for each clip. Clips vary in length from 1 to 10 seconds and have
|
| 13 |
+
a total length of approximately 24 hours.
|
| 14 |
+
|
| 15 |
+
The texts were published between 1884 and 1964, and are in the public domain.
|
| 16 |
+
The audio was recorded in 2016-17 by the LibriVox project and is also in the
|
| 17 |
+
public domain.
|
| 18 |
+
|
| 19 |
+
The following files provide raw lavels for the train/validation/test split
|
| 20 |
+
* train.txt
|
| 21 |
+
* valid.txt
|
| 22 |
+
* test.txt
|
| 23 |
+
|
| 24 |
+
Friendly metadata with the split is provided in the following files:
|
| 25 |
+
* ljspeech_train.json
|
| 26 |
+
* ljspeech_test.json
|
| 27 |
+
* ljspeech_valid.json
|
| 28 |
+
|
| 29 |
+
The JSON files are formatted as follows:
|
| 30 |
+
|
| 31 |
+
```json
|
| 32 |
+
{
|
| 33 |
+
"<sample-id>": {
|
| 34 |
+
"char_raw": "<label text (raw)>",
|
| 35 |
+
"char": "<label text (preprocessed)",
|
| 36 |
+
"phn": "<experimental phoneme annotation obtained using a G2P model",
|
| 37 |
+
"wav": "<relative path to the file"
|
| 38 |
+
}
|
| 39 |
+
}
|
| 40 |
+
```
|
| 41 |
+
|
| 42 |
+
The dataset is also usable as a HuggingFace Arrow dataset:
|
| 43 |
+
https://huggingface.co/docs/datasets/
|
| 44 |
+
|
| 45 |
+
# FILE FORMAT
|
| 46 |
+
|
| 47 |
+
Original metadata is provided in metadata.csv. This file consists of one record per line, delimited by the pipe character (0x7c). The fields are:
|
| 48 |
+
|
| 49 |
+
1. ID: this is the name of the corresponding .wav file
|
| 50 |
+
2. Transcription: words spoken by the reader (UTF-8)
|
| 51 |
+
3. Normalized Transcription: transcription with numbers, ordinals, and
|
| 52 |
+
monetary units expanded into full words (UTF-8).
|
| 53 |
+
|
| 54 |
+
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of
|
| 55 |
+
22050 Hz.
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
## Statistics
|
| 60 |
+
|
| 61 |
+
Total Clips 13,100
|
| 62 |
+
Total Words 225,715
|
| 63 |
+
Total Characters 1,308,674
|
| 64 |
+
Total Duration 23:55:17
|
| 65 |
+
Mean Clip Duration 6.57 sec
|
| 66 |
+
Min Clip Duration 1.11 sec
|
| 67 |
+
Max Clip Duration 10.10 sec
|
| 68 |
+
Mean Words per Clip 17.23
|
| 69 |
+
Distinct Words 13,821
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
|
| 73 |
+
## Miscellaneous
|
| 74 |
+
|
| 75 |
+
The audio clips range in length from approximately 1 second to 10 seconds.
|
| 76 |
+
They were segmented automatically based on silences in the recording. Clip
|
| 77 |
+
boundaries generally align with sentence or clause boundaries, but not always.
|
| 78 |
+
|
| 79 |
+
The text was matched to the audio manually, and a QA pass was done to ensure
|
| 80 |
+
that the text accurately matched the words spoken in the audio.
|
| 81 |
+
|
| 82 |
+
The original LibriVox recordings were distributed as 128 kbps MP3 files. As a
|
| 83 |
+
result, they may contain artifacts introduced by the MP3 encoding.
|
| 84 |
+
|
| 85 |
+
The following abbreviations appear in the text. They may be expanded as
|
| 86 |
+
follows:
|
| 87 |
+
|
| 88 |
+
Abbreviation Expansion
|
| 89 |
+
--------------------------
|
| 90 |
+
Mr. Mister
|
| 91 |
+
Mrs. Misess (*)
|
| 92 |
+
Dr. Doctor
|
| 93 |
+
No. Number
|
| 94 |
+
St. Saint
|
| 95 |
+
Co. Company
|
| 96 |
+
Jr. Junior
|
| 97 |
+
Maj. Major
|
| 98 |
+
Gen. General
|
| 99 |
+
Drs. Doctors
|
| 100 |
+
Rev. Reverend
|
| 101 |
+
Lt. Lieutenant
|
| 102 |
+
Hon. Honorable
|
| 103 |
+
Sgt. Sergeant
|
| 104 |
+
Capt. Captain
|
| 105 |
+
Esq. Esquire
|
| 106 |
+
Ltd. Limited
|
| 107 |
+
Col. Colonel
|
| 108 |
+
Ft. Fort
|
| 109 |
+
|
| 110 |
+
* there's no standard expansion of "Mrs."
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257
|
| 114 |
+
contains "raison d'être").
|
| 115 |
+
|
| 116 |
+
For more information or to report errors, please email kito@kito.us.
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
LICENSE
|
| 121 |
+
|
| 122 |
+
This dataset is in the public domain in the USA (and likely other countries as
|
| 123 |
+
well). There are no restrictions on its use. For more information, please see:
|
| 124 |
+
https://librivox.org/pages/public-domain.
|
| 125 |
+
|
| 126 |
+
|
| 127 |
+
CHANGELOG
|
| 128 |
+
|
| 129 |
+
* 1.0 (July 8, 2017):
|
| 130 |
+
Initial release
|
| 131 |
+
|
| 132 |
+
* 1.1 (Feb 19, 2018):
|
| 133 |
+
Version 1.0 included 30 .wav files with no corresponding annotations in
|
| 134 |
+
metadata.csv. These have been removed in version 1.1. Thanks to Rafael Valle
|
| 135 |
+
for spotting this.
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
CREDITS
|
| 139 |
+
|
| 140 |
+
This dataset consists of excerpts from the following works:
|
| 141 |
+
|
| 142 |
+
* Morris, William, et al. Arts and Crafts Essays. 1893.
|
| 143 |
+
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
|
| 144 |
+
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt.
|
| 145 |
+
1933-42.
|
| 146 |
+
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
|
| 147 |
+
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5:
|
| 148 |
+
Biology. 1910.
|
| 149 |
+
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
|
| 150 |
+
* President's Commission on the Assassination of President Kennedy. Report
|
| 151 |
+
of the President's Commission on the Assassination of President Kennedy.
|
| 152 |
+
1964.
|
| 153 |
+
|
| 154 |
+
Recordings by Linda Johnson. Alignment and annotation by Keith Ito. All text,
|
| 155 |
+
audio, and annotations are in the public domain.
|
| 156 |
+
|
| 157 |
+
There's no requirement to cite this work, but if you'd like to do so, you can
|
| 158 |
+
link to: https://keithito.com/LJ-Speech-Dataset
|
| 159 |
+
|
| 160 |
+
or use the following:
|
| 161 |
+
@misc{ljspeech17,
|
| 162 |
+
author = {Keith Ito},
|
| 163 |
+
title = {The LJ Speech Dataset},
|
| 164 |
+
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
|
| 165 |
+
year = 2017
|
| 166 |
+
}
|
huggingface_dataset/Dataset_Card/income_quora-top-20-gen-queries.md
ADDED
|
@@ -0,0 +1,510 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
language_creators: []
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
license:
|
| 7 |
+
- cc-by-sa-4.0
|
| 8 |
+
multilinguality:
|
| 9 |
+
- monolingual
|
| 10 |
+
paperswithcode_id: beir
|
| 11 |
+
pretty_name: BEIR Benchmark
|
| 12 |
+
size_categories:
|
| 13 |
+
msmarco:
|
| 14 |
+
- 1M<n<10M
|
| 15 |
+
trec-covid:
|
| 16 |
+
- 100k<n<1M
|
| 17 |
+
nfcorpus:
|
| 18 |
+
- 1K<n<10K
|
| 19 |
+
nq:
|
| 20 |
+
- 1M<n<10M
|
| 21 |
+
hotpotqa:
|
| 22 |
+
- 1M<n<10M
|
| 23 |
+
fiqa:
|
| 24 |
+
- 10K<n<100K
|
| 25 |
+
arguana:
|
| 26 |
+
- 1K<n<10K
|
| 27 |
+
touche-2020:
|
| 28 |
+
- 100K<n<1M
|
| 29 |
+
cqadupstack:
|
| 30 |
+
- 100K<n<1M
|
| 31 |
+
quora:
|
| 32 |
+
- 100K<n<1M
|
| 33 |
+
dbpedia:
|
| 34 |
+
- 1M<n<10M
|
| 35 |
+
scidocs:
|
| 36 |
+
- 10K<n<100K
|
| 37 |
+
fever:
|
| 38 |
+
- 1M<n<10M
|
| 39 |
+
climate-fever:
|
| 40 |
+
- 1M<n<10M
|
| 41 |
+
scifact:
|
| 42 |
+
- 1K<n<10K
|
| 43 |
+
source_datasets: []
|
| 44 |
+
task_categories:
|
| 45 |
+
- text-retrieval
|
| 46 |
+
---
|
| 47 |
+
|
| 48 |
+
# NFCorpus: 20 generated queries (BEIR Benchmark)
|
| 49 |
+
|
| 50 |
+
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
|
| 51 |
+
|
| 52 |
+
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
|
| 53 |
+
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
|
| 54 |
+
- Questions generated: 20
|
| 55 |
+
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
Below contains the old dataset card for the BEIR benchmark.
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
# Dataset Card for BEIR Benchmark
|
| 62 |
+
|
| 63 |
+
## Table of Contents
|
| 64 |
+
- [Dataset Description](#dataset-description)
|
| 65 |
+
- [Dataset Summary](#dataset-summary)
|
| 66 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 67 |
+
- [Languages](#languages)
|
| 68 |
+
- [Dataset Structure](#dataset-structure)
|
| 69 |
+
- [Data Instances](#data-instances)
|
| 70 |
+
- [Data Fields](#data-fields)
|
| 71 |
+
- [Data Splits](#data-splits)
|
| 72 |
+
- [Dataset Creation](#dataset-creation)
|
| 73 |
+
- [Curation Rationale](#curation-rationale)
|
| 74 |
+
- [Source Data](#source-data)
|
| 75 |
+
- [Annotations](#annotations)
|
| 76 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 77 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 78 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 79 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 80 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 81 |
+
- [Additional Information](#additional-information)
|
| 82 |
+
- [Dataset Curators](#dataset-curators)
|
| 83 |
+
- [Licensing Information](#licensing-information)
|
| 84 |
+
- [Citation Information](#citation-information)
|
| 85 |
+
- [Contributions](#contributions)
|
| 86 |
+
|
| 87 |
+
## Dataset Description
|
| 88 |
+
|
| 89 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 90 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 91 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 92 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 93 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 94 |
+
|
| 95 |
+
### Dataset Summary
|
| 96 |
+
|
| 97 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 98 |
+
|
| 99 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 100 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 101 |
+
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
|
| 102 |
+
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 103 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 104 |
+
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
|
| 105 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 106 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 107 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 108 |
+
|
| 109 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
```python
|
| 113 |
+
|
| 114 |
+
```
|
| 115 |
+
|
| 116 |
+
### Supported Tasks and Leaderboards
|
| 117 |
+
|
| 118 |
+
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
|
| 119 |
+
|
| 120 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 121 |
+
|
| 122 |
+
### Languages
|
| 123 |
+
|
| 124 |
+
All tasks are in English (`en`).
|
| 125 |
+
|
| 126 |
+
## Dataset Structure
|
| 127 |
+
|
| 128 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 129 |
+
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
|
| 130 |
+
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
|
| 131 |
+
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
|
| 132 |
+
|
| 133 |
+
### Data Instances
|
| 134 |
+
|
| 135 |
+
A high level example of any beir dataset:
|
| 136 |
+
|
| 137 |
+
```python
|
| 138 |
+
corpus = {
|
| 139 |
+
"doc1" : {
|
| 140 |
+
"title": "Albert Einstein",
|
| 141 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 142 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 143 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 144 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 145 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 146 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 147 |
+
},
|
| 148 |
+
"doc2" : {
|
| 149 |
+
"title": "", # Keep title an empty string if not present
|
| 150 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 151 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 152 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 153 |
+
},
|
| 154 |
+
}
|
| 155 |
+
|
| 156 |
+
queries = {
|
| 157 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 158 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 159 |
+
}
|
| 160 |
+
|
| 161 |
+
qrels = {
|
| 162 |
+
"q1" : {"doc1": 1},
|
| 163 |
+
"q2" : {"doc2": 1},
|
| 164 |
+
}
|
| 165 |
+
```
|
| 166 |
+
|
| 167 |
+
### Data Fields
|
| 168 |
+
|
| 169 |
+
Examples from all configurations have the following features:
|
| 170 |
+
|
| 171 |
+
### Corpus
|
| 172 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 173 |
+
- `_id`: a `string` feature representing the unique document id
|
| 174 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 175 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 176 |
+
|
| 177 |
+
### Queries
|
| 178 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 179 |
+
- `_id`: a `string` feature representing the unique query id
|
| 180 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 181 |
+
|
| 182 |
+
### Qrels
|
| 183 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 184 |
+
- `_id`: a `string` feature representing the query id
|
| 185 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 186 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 187 |
+
|
| 188 |
+
|
| 189 |
+
### Data Splits
|
| 190 |
+
|
| 191 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 192 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 193 |
+
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
|
| 194 |
+
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
|
| 195 |
+
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
|
| 196 |
+
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
|
| 197 |
+
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
|
| 198 |
+
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
|
| 199 |
+
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
|
| 200 |
+
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
|
| 201 |
+
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
|
| 202 |
+
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
|
| 203 |
+
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
|
| 204 |
+
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
|
| 205 |
+
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
|
| 206 |
+
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
|
| 207 |
+
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
|
| 208 |
+
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
|
| 209 |
+
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
|
| 210 |
+
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
|
| 211 |
+
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
|
| 212 |
+
|
| 213 |
+
|
| 214 |
+
## Dataset Creation
|
| 215 |
+
|
| 216 |
+
### Curation Rationale
|
| 217 |
+
|
| 218 |
+
[Needs More Information]
|
| 219 |
+
|
| 220 |
+
### Source Data
|
| 221 |
+
|
| 222 |
+
#### Initial Data Collection and Normalization
|
| 223 |
+
|
| 224 |
+
[Needs More Information]
|
| 225 |
+
|
| 226 |
+
#### Who are the source language producers?
|
| 227 |
+
|
| 228 |
+
[Needs More Information]
|
| 229 |
+
|
| 230 |
+
### Annotations
|
| 231 |
+
|
| 232 |
+
#### Annotation process
|
| 233 |
+
|
| 234 |
+
[Needs More Information]
|
| 235 |
+
|
| 236 |
+
#### Who are the annotators?
|
| 237 |
+
|
| 238 |
+
[Needs More Information]
|
| 239 |
+
|
| 240 |
+
### Personal and Sensitive Information
|
| 241 |
+
|
| 242 |
+
[Needs More Information]
|
| 243 |
+
|
| 244 |
+
## Considerations for Using the Data
|
| 245 |
+
|
| 246 |
+
### Social Impact of Dataset
|
| 247 |
+
|
| 248 |
+
[Needs More Information]
|
| 249 |
+
|
| 250 |
+
### Discussion of Biases
|
| 251 |
+
|
| 252 |
+
[Needs More Information]
|
| 253 |
+
|
| 254 |
+
### Other Known Limitations
|
| 255 |
+
|
| 256 |
+
[Needs More Information]
|
| 257 |
+
|
| 258 |
+
## Additional Information
|
| 259 |
+
|
| 260 |
+
### Dataset Curators
|
| 261 |
+
|
| 262 |
+
[Needs More Information]
|
| 263 |
+
|
| 264 |
+
### Licensing Information
|
| 265 |
+
|
| 266 |
+
[Needs More Information]
|
| 267 |
+
|
| 268 |
+
### Citation Information
|
| 269 |
+
|
| 270 |
+
Cite as:
|
| 271 |
+
```
|
| 272 |
+
@inproceedings{
|
| 273 |
+
thakur2021beir,
|
| 274 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 275 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 276 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 277 |
+
year={2021},
|
| 278 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 279 |
+
}
|
| 280 |
+
```
|
| 281 |
+
|
| 282 |
+
### Contributions
|
| 283 |
+
|
| 284 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
|
| 285 |
+
|
| 286 |
+
|
| 287 |
+
# Dataset Card for BEIR Benchmark
|
| 288 |
+
|
| 289 |
+
## Table of Contents
|
| 290 |
+
- [Dataset Description](#dataset-description)
|
| 291 |
+
- [Dataset Summary](#dataset-summary)
|
| 292 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 293 |
+
- [Languages](#languages)
|
| 294 |
+
- [Dataset Structure](#dataset-structure)
|
| 295 |
+
- [Data Instances](#data-instances)
|
| 296 |
+
- [Data Fields](#data-fields)
|
| 297 |
+
- [Data Splits](#data-splits)
|
| 298 |
+
- [Dataset Creation](#dataset-creation)
|
| 299 |
+
- [Curation Rationale](#curation-rationale)
|
| 300 |
+
- [Source Data](#source-data)
|
| 301 |
+
- [Annotations](#annotations)
|
| 302 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 303 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 304 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 305 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 306 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 307 |
+
- [Additional Information](#additional-information)
|
| 308 |
+
- [Dataset Curators](#dataset-curators)
|
| 309 |
+
- [Licensing Information](#licensing-information)
|
| 310 |
+
- [Citation Information](#citation-information)
|
| 311 |
+
- [Contributions](#contributions)
|
| 312 |
+
|
| 313 |
+
## Dataset Description
|
| 314 |
+
|
| 315 |
+
- **Homepage:** https://github.com/UKPLab/beir
|
| 316 |
+
- **Repository:** https://github.com/UKPLab/beir
|
| 317 |
+
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
|
| 318 |
+
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
|
| 319 |
+
- **Point of Contact:** nandan.thakur@uwaterloo.ca
|
| 320 |
+
|
| 321 |
+
### Dataset Summary
|
| 322 |
+
|
| 323 |
+
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
|
| 324 |
+
|
| 325 |
+
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
|
| 326 |
+
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
|
| 327 |
+
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
|
| 328 |
+
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
|
| 329 |
+
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
|
| 330 |
+
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
|
| 331 |
+
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
|
| 332 |
+
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
|
| 333 |
+
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
|
| 334 |
+
|
| 335 |
+
All these datasets have been preprocessed and can be used for your experiments.
|
| 336 |
+
|
| 337 |
+
|
| 338 |
+
```python
|
| 339 |
+
|
| 340 |
+
```
|
| 341 |
+
|
| 342 |
+
### Supported Tasks and Leaderboards
|
| 343 |
+
|
| 344 |
+
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
|
| 345 |
+
|
| 346 |
+
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
|
| 347 |
+
|
| 348 |
+
### Languages
|
| 349 |
+
|
| 350 |
+
All tasks are in English (`en`).
|
| 351 |
+
|
| 352 |
+
## Dataset Structure
|
| 353 |
+
|
| 354 |
+
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
|
| 355 |
+
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
|
| 356 |
+
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
|
| 357 |
+
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
|
| 358 |
+
|
| 359 |
+
### Data Instances
|
| 360 |
+
|
| 361 |
+
A high level example of any beir dataset:
|
| 362 |
+
|
| 363 |
+
```python
|
| 364 |
+
corpus = {
|
| 365 |
+
"doc1" : {
|
| 366 |
+
"title": "Albert Einstein",
|
| 367 |
+
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
|
| 368 |
+
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
|
| 369 |
+
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
|
| 370 |
+
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
|
| 371 |
+
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
|
| 372 |
+
of the photoelectric effect', a pivotal step in the development of quantum theory."
|
| 373 |
+
},
|
| 374 |
+
"doc2" : {
|
| 375 |
+
"title": "", # Keep title an empty string if not present
|
| 376 |
+
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
|
| 377 |
+
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
|
| 378 |
+
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
|
| 379 |
+
},
|
| 380 |
+
}
|
| 381 |
+
|
| 382 |
+
queries = {
|
| 383 |
+
"q1" : "Who developed the mass-energy equivalence formula?",
|
| 384 |
+
"q2" : "Which beer is brewed with a large proportion of wheat?"
|
| 385 |
+
}
|
| 386 |
+
|
| 387 |
+
qrels = {
|
| 388 |
+
"q1" : {"doc1": 1},
|
| 389 |
+
"q2" : {"doc2": 1},
|
| 390 |
+
}
|
| 391 |
+
```
|
| 392 |
+
|
| 393 |
+
### Data Fields
|
| 394 |
+
|
| 395 |
+
Examples from all configurations have the following features:
|
| 396 |
+
|
| 397 |
+
### Corpus
|
| 398 |
+
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
|
| 399 |
+
- `_id`: a `string` feature representing the unique document id
|
| 400 |
+
- `title`: a `string` feature, denoting the title of the document.
|
| 401 |
+
- `text`: a `string` feature, denoting the text of the document.
|
| 402 |
+
|
| 403 |
+
### Queries
|
| 404 |
+
- `queries`: a `dict` feature representing the query, made up of:
|
| 405 |
+
- `_id`: a `string` feature representing the unique query id
|
| 406 |
+
- `text`: a `string` feature, denoting the text of the query.
|
| 407 |
+
|
| 408 |
+
### Qrels
|
| 409 |
+
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
|
| 410 |
+
- `_id`: a `string` feature representing the query id
|
| 411 |
+
- `_id`: a `string` feature, denoting the document id.
|
| 412 |
+
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
|
| 413 |
+
|
| 414 |
+
|
| 415 |
+
### Data Splits
|
| 416 |
+
|
| 417 |
+
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
|
| 418 |
+
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
|
| 419 |
+
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
|
| 420 |
+
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
|
| 421 |
+
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
|
| 422 |
+
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
|
| 423 |
+
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
|
| 424 |
+
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
|
| 425 |
+
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
|
| 426 |
+
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
|
| 427 |
+
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
|
| 428 |
+
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
|
| 429 |
+
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
|
| 430 |
+
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
|
| 431 |
+
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
|
| 432 |
+
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
|
| 433 |
+
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
|
| 434 |
+
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
|
| 435 |
+
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
|
| 436 |
+
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
|
| 437 |
+
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
|
| 438 |
+
|
| 439 |
+
|
| 440 |
+
## Dataset Creation
|
| 441 |
+
|
| 442 |
+
### Curation Rationale
|
| 443 |
+
|
| 444 |
+
[Needs More Information]
|
| 445 |
+
|
| 446 |
+
### Source Data
|
| 447 |
+
|
| 448 |
+
#### Initial Data Collection and Normalization
|
| 449 |
+
|
| 450 |
+
[Needs More Information]
|
| 451 |
+
|
| 452 |
+
#### Who are the source language producers?
|
| 453 |
+
|
| 454 |
+
[Needs More Information]
|
| 455 |
+
|
| 456 |
+
### Annotations
|
| 457 |
+
|
| 458 |
+
#### Annotation process
|
| 459 |
+
|
| 460 |
+
[Needs More Information]
|
| 461 |
+
|
| 462 |
+
#### Who are the annotators?
|
| 463 |
+
|
| 464 |
+
[Needs More Information]
|
| 465 |
+
|
| 466 |
+
### Personal and Sensitive Information
|
| 467 |
+
|
| 468 |
+
[Needs More Information]
|
| 469 |
+
|
| 470 |
+
## Considerations for Using the Data
|
| 471 |
+
|
| 472 |
+
### Social Impact of Dataset
|
| 473 |
+
|
| 474 |
+
[Needs More Information]
|
| 475 |
+
|
| 476 |
+
### Discussion of Biases
|
| 477 |
+
|
| 478 |
+
[Needs More Information]
|
| 479 |
+
|
| 480 |
+
### Other Known Limitations
|
| 481 |
+
|
| 482 |
+
[Needs More Information]
|
| 483 |
+
|
| 484 |
+
## Additional Information
|
| 485 |
+
|
| 486 |
+
### Dataset Curators
|
| 487 |
+
|
| 488 |
+
[Needs More Information]
|
| 489 |
+
|
| 490 |
+
### Licensing Information
|
| 491 |
+
|
| 492 |
+
[Needs More Information]
|
| 493 |
+
|
| 494 |
+
### Citation Information
|
| 495 |
+
|
| 496 |
+
Cite as:
|
| 497 |
+
```
|
| 498 |
+
@inproceedings{
|
| 499 |
+
thakur2021beir,
|
| 500 |
+
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
|
| 501 |
+
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
|
| 502 |
+
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
|
| 503 |
+
year={2021},
|
| 504 |
+
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
|
| 505 |
+
}
|
| 506 |
+
```
|
| 507 |
+
|
| 508 |
+
### Contributions
|
| 509 |
+
|
| 510 |
+
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
|
huggingface_dataset/Dataset_Card/keremberke_garbage-object-detection.md
ADDED
|
@@ -0,0 +1,61 @@
|
|
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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 |
+
task_categories:
|
| 3 |
+
- object-detection
|
| 4 |
+
tags:
|
| 5 |
+
- roboflow
|
| 6 |
+
---
|
| 7 |
+
|
| 8 |
+
### Roboflow Dataset Page
|
| 9 |
+
[https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2](https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2?ref=roboflow2huggingface)
|
| 10 |
+
|
| 11 |
+
### Dataset Labels
|
| 12 |
+
|
| 13 |
+
```
|
| 14 |
+
['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic']
|
| 15 |
+
```
|
| 16 |
+
|
| 17 |
+
### Citation
|
| 18 |
+
|
| 19 |
+
```
|
| 20 |
+
@misc{ garbage-classification-3_dataset,
|
| 21 |
+
title = { GARBAGE CLASSIFICATION 3 Dataset },
|
| 22 |
+
type = { Open Source Dataset },
|
| 23 |
+
author = { Material Identification },
|
| 24 |
+
howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } },
|
| 25 |
+
url = { https://universe.roboflow.com/material-identification/garbage-classification-3 },
|
| 26 |
+
journal = { Roboflow Universe },
|
| 27 |
+
publisher = { Roboflow },
|
| 28 |
+
year = { 2022 },
|
| 29 |
+
month = { mar },
|
| 30 |
+
note = { visited on 2023-01-02 },
|
| 31 |
+
}
|
| 32 |
+
```
|
| 33 |
+
|
| 34 |
+
### License
|
| 35 |
+
CC BY 4.0
|
| 36 |
+
|
| 37 |
+
### Dataset Summary
|
| 38 |
+
This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT
|
| 39 |
+
|
| 40 |
+
Roboflow is an end-to-end computer vision platform that helps you
|
| 41 |
+
* collaborate with your team on computer vision projects
|
| 42 |
+
* collect & organize images
|
| 43 |
+
* understand unstructured image data
|
| 44 |
+
* annotate, and create datasets
|
| 45 |
+
* export, train, and deploy computer vision models
|
| 46 |
+
* use active learning to improve your dataset over time
|
| 47 |
+
|
| 48 |
+
It includes 10464 images.
|
| 49 |
+
GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format.
|
| 50 |
+
|
| 51 |
+
The following pre-processing was applied to each image:
|
| 52 |
+
* Auto-orientation of pixel data (with EXIF-orientation stripping)
|
| 53 |
+
* Resize to 416x416 (Stretch)
|
| 54 |
+
|
| 55 |
+
The following augmentation was applied to create 1 versions of each source image:
|
| 56 |
+
* 50% probability of horizontal flip
|
| 57 |
+
* 50% probability of vertical flip
|
| 58 |
+
* Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
|
huggingface_dataset/Dataset_Card/philschmid_emotion.md
ADDED
|
@@ -0,0 +1,279 @@
|
|
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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 |
+
pretty_name: Emotion
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- machine-generated
|
| 5 |
+
language_creators:
|
| 6 |
+
- machine-generated
|
| 7 |
+
language:
|
| 8 |
+
- en
|
| 9 |
+
license:
|
| 10 |
+
- other
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
size_categories:
|
| 14 |
+
- 10K<n<100K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- text-classification
|
| 19 |
+
task_ids:
|
| 20 |
+
- multi-class-classification
|
| 21 |
+
paperswithcode_id: emotion
|
| 22 |
+
train-eval-index:
|
| 23 |
+
- config: default
|
| 24 |
+
task: text-classification
|
| 25 |
+
task_id: multi_class_classification
|
| 26 |
+
splits:
|
| 27 |
+
train_split: train
|
| 28 |
+
eval_split: test
|
| 29 |
+
col_mapping:
|
| 30 |
+
text: text
|
| 31 |
+
label: target
|
| 32 |
+
metrics:
|
| 33 |
+
- type: accuracy
|
| 34 |
+
name: Accuracy
|
| 35 |
+
- type: f1
|
| 36 |
+
name: F1 macro
|
| 37 |
+
args:
|
| 38 |
+
average: macro
|
| 39 |
+
- type: f1
|
| 40 |
+
name: F1 micro
|
| 41 |
+
args:
|
| 42 |
+
average: micro
|
| 43 |
+
- type: f1
|
| 44 |
+
name: F1 weighted
|
| 45 |
+
args:
|
| 46 |
+
average: weighted
|
| 47 |
+
- type: precision
|
| 48 |
+
name: Precision macro
|
| 49 |
+
args:
|
| 50 |
+
average: macro
|
| 51 |
+
- type: precision
|
| 52 |
+
name: Precision micro
|
| 53 |
+
args:
|
| 54 |
+
average: micro
|
| 55 |
+
- type: precision
|
| 56 |
+
name: Precision weighted
|
| 57 |
+
args:
|
| 58 |
+
average: weighted
|
| 59 |
+
- type: recall
|
| 60 |
+
name: Recall macro
|
| 61 |
+
args:
|
| 62 |
+
average: macro
|
| 63 |
+
- type: recall
|
| 64 |
+
name: Recall micro
|
| 65 |
+
args:
|
| 66 |
+
average: micro
|
| 67 |
+
- type: recall
|
| 68 |
+
name: Recall weighted
|
| 69 |
+
args:
|
| 70 |
+
average: weighted
|
| 71 |
+
tags:
|
| 72 |
+
- emotion-classification
|
| 73 |
+
dataset_info:
|
| 74 |
+
- config_name: split
|
| 75 |
+
features:
|
| 76 |
+
- name: text
|
| 77 |
+
dtype: string
|
| 78 |
+
- name: label
|
| 79 |
+
dtype:
|
| 80 |
+
class_label:
|
| 81 |
+
names:
|
| 82 |
+
'0': sadness
|
| 83 |
+
'1': joy
|
| 84 |
+
'2': love
|
| 85 |
+
'3': anger
|
| 86 |
+
'4': fear
|
| 87 |
+
'5': surprise
|
| 88 |
+
splits:
|
| 89 |
+
- name: train
|
| 90 |
+
num_bytes: 1741597
|
| 91 |
+
num_examples: 16000
|
| 92 |
+
- name: validation
|
| 93 |
+
num_bytes: 214703
|
| 94 |
+
num_examples: 2000
|
| 95 |
+
- name: test
|
| 96 |
+
num_bytes: 217181
|
| 97 |
+
num_examples: 2000
|
| 98 |
+
download_size: 740883
|
| 99 |
+
dataset_size: 2173481
|
| 100 |
+
- config_name: unsplit
|
| 101 |
+
features:
|
| 102 |
+
- name: text
|
| 103 |
+
dtype: string
|
| 104 |
+
- name: label
|
| 105 |
+
dtype:
|
| 106 |
+
class_label:
|
| 107 |
+
names:
|
| 108 |
+
'0': sadness
|
| 109 |
+
'1': joy
|
| 110 |
+
'2': love
|
| 111 |
+
'3': anger
|
| 112 |
+
'4': fear
|
| 113 |
+
'5': surprise
|
| 114 |
+
splits:
|
| 115 |
+
- name: train
|
| 116 |
+
num_bytes: 45445685
|
| 117 |
+
num_examples: 416809
|
| 118 |
+
download_size: 15388281
|
| 119 |
+
dataset_size: 45445685
|
| 120 |
+
duplicated_from: emotion
|
| 121 |
+
---
|
| 122 |
+
|
| 123 |
+
# Dataset Card for "emotion"
|
| 124 |
+
|
| 125 |
+
## Table of Contents
|
| 126 |
+
- [Dataset Description](#dataset-description)
|
| 127 |
+
- [Dataset Summary](#dataset-summary)
|
| 128 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 129 |
+
- [Languages](#languages)
|
| 130 |
+
- [Dataset Structure](#dataset-structure)
|
| 131 |
+
- [Data Instances](#data-instances)
|
| 132 |
+
- [Data Fields](#data-fields)
|
| 133 |
+
- [Data Splits](#data-splits)
|
| 134 |
+
- [Dataset Creation](#dataset-creation)
|
| 135 |
+
- [Curation Rationale](#curation-rationale)
|
| 136 |
+
- [Source Data](#source-data)
|
| 137 |
+
- [Annotations](#annotations)
|
| 138 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 139 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 140 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 141 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 142 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 143 |
+
- [Additional Information](#additional-information)
|
| 144 |
+
- [Dataset Curators](#dataset-curators)
|
| 145 |
+
- [Licensing Information](#licensing-information)
|
| 146 |
+
- [Citation Information](#citation-information)
|
| 147 |
+
- [Contributions](#contributions)
|
| 148 |
+
|
| 149 |
+
## Dataset Description
|
| 150 |
+
|
| 151 |
+
- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
|
| 152 |
+
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 154 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 155 |
+
- **Size of downloaded dataset files:** 3.95 MB
|
| 156 |
+
- **Size of the generated dataset:** 4.16 MB
|
| 157 |
+
- **Total amount of disk used:** 8.11 MB
|
| 158 |
+
|
| 159 |
+
### Dataset Summary
|
| 160 |
+
|
| 161 |
+
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
|
| 162 |
+
|
| 163 |
+
### Supported Tasks and Leaderboards
|
| 164 |
+
|
| 165 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 166 |
+
|
| 167 |
+
### Languages
|
| 168 |
+
|
| 169 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 170 |
+
|
| 171 |
+
## Dataset Structure
|
| 172 |
+
|
| 173 |
+
### Data Instances
|
| 174 |
+
|
| 175 |
+
An example looks as follows.
|
| 176 |
+
```
|
| 177 |
+
{
|
| 178 |
+
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
|
| 179 |
+
"label": 0
|
| 180 |
+
}
|
| 181 |
+
```
|
| 182 |
+
|
| 183 |
+
### Data Fields
|
| 184 |
+
|
| 185 |
+
The data fields are:
|
| 186 |
+
- `text`: a `string` feature.
|
| 187 |
+
- `label`: a classification label, with possible values including `sadness` (0), `joy` (1), `love` (2), `anger` (3), `fear` (4), `surprise` (5).
|
| 188 |
+
|
| 189 |
+
### Data Splits
|
| 190 |
+
|
| 191 |
+
The dataset has 2 configurations:
|
| 192 |
+
- split: with a total of 20_000 examples split into train, validation and split
|
| 193 |
+
- unsplit: with a total of 416_809 examples in a single train split
|
| 194 |
+
|
| 195 |
+
| name | train | validation | test |
|
| 196 |
+
|---------|-------:|-----------:|-----:|
|
| 197 |
+
| split | 16000 | 2000 | 2000 |
|
| 198 |
+
| unsplit | 416809 | n/a | n/a |
|
| 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 |
+
The dataset should be used for educational and research purposes only.
|
| 253 |
+
|
| 254 |
+
### Citation Information
|
| 255 |
+
|
| 256 |
+
If you use this dataset, please cite:
|
| 257 |
+
```
|
| 258 |
+
@inproceedings{saravia-etal-2018-carer,
|
| 259 |
+
title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
|
| 260 |
+
author = "Saravia, Elvis and
|
| 261 |
+
Liu, Hsien-Chi Toby and
|
| 262 |
+
Huang, Yen-Hao and
|
| 263 |
+
Wu, Junlin and
|
| 264 |
+
Chen, Yi-Shin",
|
| 265 |
+
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
|
| 266 |
+
month = oct # "-" # nov,
|
| 267 |
+
year = "2018",
|
| 268 |
+
address = "Brussels, Belgium",
|
| 269 |
+
publisher = "Association for Computational Linguistics",
|
| 270 |
+
url = "https://www.aclweb.org/anthology/D18-1404",
|
| 271 |
+
doi = "10.18653/v1/D18-1404",
|
| 272 |
+
pages = "3687--3697",
|
| 273 |
+
abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
|
| 274 |
+
}
|
| 275 |
+
```
|
| 276 |
+
|
| 277 |
+
### Contributions
|
| 278 |
+
|
| 279 |
+
Thanks to [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
|
huggingface_dataset/Dataset_Card/qasc.md
ADDED
|
@@ -0,0 +1,225 @@
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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 |
+
- crowdsourced
|
| 4 |
+
language:
|
| 5 |
+
- en
|
| 6 |
+
language_creators:
|
| 7 |
+
- found
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: Question Answering via Sentence Composition (QASC)
|
| 13 |
+
size_categories:
|
| 14 |
+
- 1K<n<10K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- question-answering
|
| 19 |
+
- multiple-choice
|
| 20 |
+
task_ids:
|
| 21 |
+
- extractive-qa
|
| 22 |
+
- multiple-choice-qa
|
| 23 |
+
paperswithcode_id: qasc
|
| 24 |
+
dataset_info:
|
| 25 |
+
features:
|
| 26 |
+
- name: id
|
| 27 |
+
dtype: string
|
| 28 |
+
- name: question
|
| 29 |
+
dtype: string
|
| 30 |
+
- name: choices
|
| 31 |
+
sequence:
|
| 32 |
+
- name: text
|
| 33 |
+
dtype: string
|
| 34 |
+
- name: label
|
| 35 |
+
dtype: string
|
| 36 |
+
- name: answerKey
|
| 37 |
+
dtype: string
|
| 38 |
+
- name: fact1
|
| 39 |
+
dtype: string
|
| 40 |
+
- name: fact2
|
| 41 |
+
dtype: string
|
| 42 |
+
- name: combinedfact
|
| 43 |
+
dtype: string
|
| 44 |
+
- name: formatted_question
|
| 45 |
+
dtype: string
|
| 46 |
+
splits:
|
| 47 |
+
- name: test
|
| 48 |
+
num_bytes: 393683
|
| 49 |
+
num_examples: 920
|
| 50 |
+
- name: train
|
| 51 |
+
num_bytes: 4919377
|
| 52 |
+
num_examples: 8134
|
| 53 |
+
- name: validation
|
| 54 |
+
num_bytes: 562352
|
| 55 |
+
num_examples: 926
|
| 56 |
+
download_size: 1616514
|
| 57 |
+
dataset_size: 5875412
|
| 58 |
+
---
|
| 59 |
+
|
| 60 |
+
# Dataset Card for "qasc"
|
| 61 |
+
|
| 62 |
+
## Table of Contents
|
| 63 |
+
- [Dataset Description](#dataset-description)
|
| 64 |
+
- [Dataset Summary](#dataset-summary)
|
| 65 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 66 |
+
- [Languages](#languages)
|
| 67 |
+
- [Dataset Structure](#dataset-structure)
|
| 68 |
+
- [Data Instances](#data-instances)
|
| 69 |
+
- [Data Fields](#data-fields)
|
| 70 |
+
- [Data Splits](#data-splits)
|
| 71 |
+
- [Dataset Creation](#dataset-creation)
|
| 72 |
+
- [Curation Rationale](#curation-rationale)
|
| 73 |
+
- [Source Data](#source-data)
|
| 74 |
+
- [Annotations](#annotations)
|
| 75 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 76 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 77 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 78 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 79 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 80 |
+
- [Additional Information](#additional-information)
|
| 81 |
+
- [Dataset Curators](#dataset-curators)
|
| 82 |
+
- [Licensing Information](#licensing-information)
|
| 83 |
+
- [Citation Information](#citation-information)
|
| 84 |
+
- [Contributions](#contributions)
|
| 85 |
+
|
| 86 |
+
## Dataset Description
|
| 87 |
+
|
| 88 |
+
- **Homepage:** [https://allenai.org/data/qasc](https://allenai.org/data/qasc)
|
| 89 |
+
- **Repository:** https://github.com/allenai/qasc/
|
| 90 |
+
- **Paper:** [QASC: A Dataset for Question Answering via Sentence Composition](https://arxiv.org/abs/1910.11473)
|
| 91 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 92 |
+
- **Size of downloaded dataset files:** 1.54 MB
|
| 93 |
+
- **Size of the generated dataset:** 5.60 MB
|
| 94 |
+
- **Total amount of disk used:** 7.14 MB
|
| 95 |
+
|
| 96 |
+
### Dataset Summary
|
| 97 |
+
|
| 98 |
+
QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
|
| 99 |
+
questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
|
| 100 |
+
|
| 101 |
+
### Supported Tasks and Leaderboards
|
| 102 |
+
|
| 103 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 104 |
+
|
| 105 |
+
### Languages
|
| 106 |
+
|
| 107 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 108 |
+
|
| 109 |
+
## Dataset Structure
|
| 110 |
+
|
| 111 |
+
### Data Instances
|
| 112 |
+
|
| 113 |
+
#### default
|
| 114 |
+
|
| 115 |
+
- **Size of downloaded dataset files:** 1.54 MB
|
| 116 |
+
- **Size of the generated dataset:** 5.60 MB
|
| 117 |
+
- **Total amount of disk used:** 7.14 MB
|
| 118 |
+
|
| 119 |
+
An example of 'validation' looks as follows.
|
| 120 |
+
```
|
| 121 |
+
{
|
| 122 |
+
"answerKey": "F",
|
| 123 |
+
"choices": {
|
| 124 |
+
"label": ["A", "B", "C", "D", "E", "F", "G", "H"],
|
| 125 |
+
"text": ["sand", "occurs over a wide range", "forests", "Global warming", "rapid changes occur", "local weather conditions", "measure of motion", "city life"]
|
| 126 |
+
},
|
| 127 |
+
"combinedfact": "Climate is generally described in terms of local weather conditions",
|
| 128 |
+
"fact1": "Climate is generally described in terms of temperature and moisture.",
|
| 129 |
+
"fact2": "Fire behavior is driven by local weather conditions such as winds, temperature and moisture.",
|
| 130 |
+
"formatted_question": "Climate is generally described in terms of what? (A) sand (B) occurs over a wide range (C) forests (D) Global warming (E) rapid changes occur (F) local weather conditions (G) measure of motion (H) city life",
|
| 131 |
+
"id": "3NGI5ARFTT4HNGVWXAMLNBMFA0U1PG",
|
| 132 |
+
"question": "Climate is generally described in terms of what?"
|
| 133 |
+
}
|
| 134 |
+
```
|
| 135 |
+
|
| 136 |
+
### Data Fields
|
| 137 |
+
|
| 138 |
+
The data fields are the same among all splits.
|
| 139 |
+
|
| 140 |
+
#### default
|
| 141 |
+
- `id`: a `string` feature.
|
| 142 |
+
- `question`: a `string` feature.
|
| 143 |
+
- `choices`: a dictionary feature containing:
|
| 144 |
+
- `text`: a `string` feature.
|
| 145 |
+
- `label`: a `string` feature.
|
| 146 |
+
- `answerKey`: a `string` feature.
|
| 147 |
+
- `fact1`: a `string` feature.
|
| 148 |
+
- `fact2`: a `string` feature.
|
| 149 |
+
- `combinedfact`: a `string` feature.
|
| 150 |
+
- `formatted_question`: a `string` feature.
|
| 151 |
+
|
| 152 |
+
### Data Splits
|
| 153 |
+
|
| 154 |
+
| name |train|validation|test|
|
| 155 |
+
|-------|----:|---------:|---:|
|
| 156 |
+
|default| 8134| 926| 920|
|
| 157 |
+
|
| 158 |
+
## Dataset Creation
|
| 159 |
+
|
| 160 |
+
### Curation Rationale
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Source Data
|
| 165 |
+
|
| 166 |
+
#### Initial Data Collection and Normalization
|
| 167 |
+
|
| 168 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 169 |
+
|
| 170 |
+
#### Who are the source language producers?
|
| 171 |
+
|
| 172 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 173 |
+
|
| 174 |
+
### Annotations
|
| 175 |
+
|
| 176 |
+
#### Annotation process
|
| 177 |
+
|
| 178 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 179 |
+
|
| 180 |
+
#### Who are the annotators?
|
| 181 |
+
|
| 182 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 183 |
+
|
| 184 |
+
### Personal and Sensitive Information
|
| 185 |
+
|
| 186 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 187 |
+
|
| 188 |
+
## Considerations for Using the Data
|
| 189 |
+
|
| 190 |
+
### Social Impact of Dataset
|
| 191 |
+
|
| 192 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 193 |
+
|
| 194 |
+
### Discussion of Biases
|
| 195 |
+
|
| 196 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 197 |
+
|
| 198 |
+
### Other Known Limitations
|
| 199 |
+
|
| 200 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 201 |
+
|
| 202 |
+
## Additional Information
|
| 203 |
+
|
| 204 |
+
### Dataset Curators
|
| 205 |
+
|
| 206 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 207 |
+
|
| 208 |
+
### Licensing Information
|
| 209 |
+
|
| 210 |
+
The dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
|
| 211 |
+
|
| 212 |
+
### Citation Information
|
| 213 |
+
|
| 214 |
+
```
|
| 215 |
+
@article{allenai:qasc,
|
| 216 |
+
author = {Tushar Khot and Peter Clark and Michal Guerquin and Peter Jansen and Ashish Sabharwal},
|
| 217 |
+
title = {QASC: A Dataset for Question Answering via Sentence Composition},
|
| 218 |
+
journal = {arXiv:1910.11473v2},
|
| 219 |
+
year = {2020},
|
| 220 |
+
}
|
| 221 |
+
```
|
| 222 |
+
|
| 223 |
+
### Contributions
|
| 224 |
+
|
| 225 |
+
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.
|
huggingface_dataset/Dataset_Card/smartdata.md
ADDED
|
@@ -0,0 +1,223 @@
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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 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- de
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 1K<n<10K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- token-classification
|
| 18 |
+
task_ids:
|
| 19 |
+
- named-entity-recognition
|
| 20 |
+
pretty_name: SmartData
|
| 21 |
+
dataset_info:
|
| 22 |
+
features:
|
| 23 |
+
- name: id
|
| 24 |
+
dtype: string
|
| 25 |
+
- name: tokens
|
| 26 |
+
sequence: string
|
| 27 |
+
- name: ner_tags
|
| 28 |
+
sequence:
|
| 29 |
+
class_label:
|
| 30 |
+
names:
|
| 31 |
+
'0': O
|
| 32 |
+
'1': B-DATE
|
| 33 |
+
'2': I-DATE
|
| 34 |
+
'3': B-DISASTER_TYPE
|
| 35 |
+
'4': I-DISASTER_TYPE
|
| 36 |
+
'5': B-DISTANCE
|
| 37 |
+
'6': I-DISTANCE
|
| 38 |
+
'7': B-DURATION
|
| 39 |
+
'8': I-DURATION
|
| 40 |
+
'9': B-LOCATION
|
| 41 |
+
'10': I-LOCATION
|
| 42 |
+
'11': B-LOCATION_CITY
|
| 43 |
+
'12': I-LOCATION_CITY
|
| 44 |
+
'13': B-LOCATION_ROUTE
|
| 45 |
+
'14': I-LOCATION_ROUTE
|
| 46 |
+
'15': B-LOCATION_STOP
|
| 47 |
+
'16': I-LOCATION_STOP
|
| 48 |
+
'17': B-LOCATION_STREET
|
| 49 |
+
'18': I-LOCATION_STREET
|
| 50 |
+
'19': B-NUMBER
|
| 51 |
+
'20': I-NUMBER
|
| 52 |
+
'21': B-ORGANIZATION
|
| 53 |
+
'22': I-ORGANIZATION
|
| 54 |
+
'23': B-ORGANIZATION_COMPANY
|
| 55 |
+
'24': I-ORGANIZATION_COMPANY
|
| 56 |
+
'25': B-ORG_POSITION
|
| 57 |
+
'26': I-ORG_POSITION
|
| 58 |
+
'27': B-PERSON
|
| 59 |
+
'28': I-PERSON
|
| 60 |
+
'29': B-TIME
|
| 61 |
+
'30': I-TIME
|
| 62 |
+
'31': B-TRIGGER
|
| 63 |
+
'32': I-TRIGGER
|
| 64 |
+
config_name: smartdata-v3_20200302
|
| 65 |
+
splits:
|
| 66 |
+
- name: train
|
| 67 |
+
num_bytes: 2124312
|
| 68 |
+
num_examples: 1861
|
| 69 |
+
- name: test
|
| 70 |
+
num_bytes: 266529
|
| 71 |
+
num_examples: 230
|
| 72 |
+
- name: validation
|
| 73 |
+
num_bytes: 258681
|
| 74 |
+
num_examples: 228
|
| 75 |
+
download_size: 18880782
|
| 76 |
+
dataset_size: 2649522
|
| 77 |
+
---
|
| 78 |
+
|
| 79 |
+
# Dataset Card for SmartData
|
| 80 |
+
|
| 81 |
+
## Table of Contents
|
| 82 |
+
- [Dataset Description](#dataset-description)
|
| 83 |
+
- [Dataset Summary](#dataset-summary)
|
| 84 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 85 |
+
- [Languages](#languages)
|
| 86 |
+
- [Dataset Structure](#dataset-structure)
|
| 87 |
+
- [Data Instances](#data-instances)
|
| 88 |
+
- [Data Fields](#data-fields)
|
| 89 |
+
- [Data Splits](#data-splits)
|
| 90 |
+
- [Dataset Creation](#dataset-creation)
|
| 91 |
+
- [Curation Rationale](#curation-rationale)
|
| 92 |
+
- [Source Data](#source-data)
|
| 93 |
+
- [Annotations](#annotations)
|
| 94 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 95 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 96 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 97 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 98 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 99 |
+
- [Additional Information](#additional-information)
|
| 100 |
+
- [Dataset Curators](#dataset-curators)
|
| 101 |
+
- [Licensing Information](#licensing-information)
|
| 102 |
+
- [Citation Information](#citation-information)
|
| 103 |
+
- [Contributions](#contributions)
|
| 104 |
+
|
| 105 |
+
## Dataset Description
|
| 106 |
+
|
| 107 |
+
- **Homepage:** https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/9427/
|
| 108 |
+
- **Repository:** https://github.com/DFKI-NLP/smartdata-corpus
|
| 109 |
+
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
|
| 110 |
+
- **Leaderboard:**
|
| 111 |
+
- **Point of Contact:**
|
| 112 |
+
|
| 113 |
+
### Dataset Summary
|
| 114 |
+
|
| 115 |
+
DFKI SmartData Corpus is a dataset of 2598 German-language documents
|
| 116 |
+
which has been annotated with fine-grained geo-entities, such as streets,
|
| 117 |
+
stops and routes, as well as standard named entity types. It has also
|
| 118 |
+
been annotated with a set of 15 traffic- and industry-related n-ary
|
| 119 |
+
relations and events, such as Accidents, Traffic jams, Acquisitions,
|
| 120 |
+
and Strikes. The corpus consists of newswire texts, Twitter messages,
|
| 121 |
+
and traffic reports from radio stations, police and railway companies.
|
| 122 |
+
It allows for training and evaluating both named entity recognition
|
| 123 |
+
algorithms that aim for fine-grained typing of geo-entities, as well
|
| 124 |
+
as n-ary relation extraction systems.
|
| 125 |
+
|
| 126 |
+
### Supported Tasks and Leaderboards
|
| 127 |
+
|
| 128 |
+
NER
|
| 129 |
+
|
| 130 |
+
### Languages
|
| 131 |
+
|
| 132 |
+
German
|
| 133 |
+
|
| 134 |
+
## Dataset Structure
|
| 135 |
+
|
| 136 |
+
### Data Instances
|
| 137 |
+
|
| 138 |
+
[More Information Needed]
|
| 139 |
+
|
| 140 |
+
### Data Fields
|
| 141 |
+
|
| 142 |
+
- id: an identifier for the article the text came from
|
| 143 |
+
- tokens: a list of string tokens for the text of the article
|
| 144 |
+
- ner_tags: a corresponding list of NER tags in the BIO format
|
| 145 |
+
|
| 146 |
+
### Data Splits
|
| 147 |
+
|
| 148 |
+
[More Information Needed]
|
| 149 |
+
|
| 150 |
+
## Dataset Creation
|
| 151 |
+
|
| 152 |
+
### Curation Rationale
|
| 153 |
+
|
| 154 |
+
[More Information Needed]
|
| 155 |
+
|
| 156 |
+
### Source Data
|
| 157 |
+
|
| 158 |
+
#### Initial Data Collection and Normalization
|
| 159 |
+
|
| 160 |
+
[More Information Needed]
|
| 161 |
+
|
| 162 |
+
#### Who are the source language producers?
|
| 163 |
+
|
| 164 |
+
[More Information Needed]
|
| 165 |
+
|
| 166 |
+
### Annotations
|
| 167 |
+
|
| 168 |
+
#### Annotation process
|
| 169 |
+
|
| 170 |
+
[More Information Needed]
|
| 171 |
+
|
| 172 |
+
#### Who are the annotators?
|
| 173 |
+
|
| 174 |
+
[More Information Needed]
|
| 175 |
+
|
| 176 |
+
### Personal and Sensitive Information
|
| 177 |
+
|
| 178 |
+
[More Information Needed]
|
| 179 |
+
|
| 180 |
+
## Considerations for Using the Data
|
| 181 |
+
|
| 182 |
+
### Social Impact of Dataset
|
| 183 |
+
|
| 184 |
+
[More Information Needed]
|
| 185 |
+
|
| 186 |
+
### Discussion of Biases
|
| 187 |
+
|
| 188 |
+
[More Information Needed]
|
| 189 |
+
|
| 190 |
+
### Other Known Limitations
|
| 191 |
+
|
| 192 |
+
[More Information Needed]
|
| 193 |
+
|
| 194 |
+
## Additional Information
|
| 195 |
+
|
| 196 |
+
### Dataset Curators
|
| 197 |
+
|
| 198 |
+
[More Information Needed]
|
| 199 |
+
|
| 200 |
+
### Licensing Information
|
| 201 |
+
|
| 202 |
+
CC-BY 4.0
|
| 203 |
+
|
| 204 |
+
### Citation Information
|
| 205 |
+
|
| 206 |
+
```
|
| 207 |
+
@InProceedings{SCHIERSCH18.85,
|
| 208 |
+
author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
|
| 209 |
+
title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
|
| 210 |
+
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
|
| 211 |
+
year = {2018},
|
| 212 |
+
month = {May 7-12, 2018},
|
| 213 |
+
address = {Miyazaki, Japan},
|
| 214 |
+
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
|
| 215 |
+
publisher = {European Language Resources Association (ELRA)},
|
| 216 |
+
isbn = {979-10-95546-00-9},
|
| 217 |
+
language = {english}
|
| 218 |
+
}
|
| 219 |
+
```
|
| 220 |
+
|
| 221 |
+
### Contributions
|
| 222 |
+
|
| 223 |
+
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.
|
huggingface_dataset/Dataset_Card/vector_test_demo.md
ADDED
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotaeators:
|
| 3 |
+
- found
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- cn
|
| 8 |
+
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
### Dataset Summary
|
| 12 |
+
Placeholder
|
| 13 |
+
You can load the dataset via:
|
| 14 |
+
```
|
| 15 |
+
import datasets
|
| 16 |
+
data = datasets.load_dataset('GEM/wiki_lingua')
|
| 17 |
+
```
|
| 18 |
+
The data loader can be found [here](https://huggingface.co/datasets/GEM/wiki_lingua).
|
| 19 |
+
#### website
|
| 20 |
+
None (See Repository)
|
| 21 |
+
#### paper
|
| 22 |
+
https://www.aclweb.org/anthology/2020.findings-emnlp.360/
|
| 23 |
+
#### authors
|
| 24 |
+
Faisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)
|
| 25 |
+
## Dataset Overview
|
| 26 |
+
### Where to find the Data and its Documentation
|
| 27 |
+
#### Webpage
|
| 28 |
+
<!-- info: What is the webpage for the dataset (if it exists)? -->
|
| 29 |
+
<!-- scope: telescope -->
|
| 30 |
+
None (See Repository)
|
| 31 |
+
#### Download
|
| 32 |
+
<!-- info: What is the link to where the original dataset is hosted? -->
|
| 33 |
+
<!-- scope: telescope -->
|
| 34 |
+
https://github.com/esdurmus/Wikilingua
|
| 35 |
+
#### Paper
|
| 36 |
+
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
|
| 37 |
+
<!-- scope: telescope -->
|
| 38 |
+
https://www.aclweb.org/anthology/2020.findings-emnlp.360/
|
| 39 |
+
#### BibTex
|
| 40 |
+
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
|
| 41 |
+
<!-- scope: microscope -->
|
| 42 |
+
@inproceedings{ladhak-etal-2020-wikilingua,
|
| 43 |
+
title = "{W}iki{L}ingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization",
|
| 44 |
+
author = "Ladhak, Faisal and
|
| 45 |
+
Durmus, Esin and
|
| 46 |
+
Cardie, Claire and
|
| 47 |
+
McKeown, Kathleen",
|
| 48 |
+
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
|
| 49 |
+
month = nov,
|
| 50 |
+
year = "2020",
|
| 51 |
+
address = "Online",
|
| 52 |
+
publisher = "Association for Computational Linguistics",
|
| 53 |
+
url = "https://aclanthology.org/2020.findings-emnlp.360",
|
| 54 |
+
doi = "10.18653/v1/2020.findings-emnlp.360",
|
| 55 |
+
pages = "4034--4048",
|
| 56 |
+
abstract = "We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of cross-lingual abstractive summarization systems. We extract article and summary pairs in 18 languages from WikiHow, a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors. We create gold-standard article-summary alignments across languages by aligning the images that are used to describe each how-to step in an article. As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset. We further propose a method for direct cross-lingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step. Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.",
|
| 57 |
+
}
|
| 58 |
+
#### Contact Name
|
| 59 |
+
<!-- quick -->
|
| 60 |
+
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
|
| 61 |
+
<!-- scope: periscope -->
|
| 62 |
+
Faisal Ladhak, Esin Durmus
|
| 63 |
+
#### Contact Email
|
| 64 |
+
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
|
| 65 |
+
<!-- scope: periscope -->
|
| 66 |
+
faisal@cs.columbia.edu, esdurmus@stanford.edu
|
| 67 |
+
#### Has a Leaderboard?
|
| 68 |
+
<!-- info: Does the dataset have an active leaderboard? -->
|
| 69 |
+
<!-- scope: telescope -->
|
| 70 |
+
no
|
| 71 |
+
### Languages and Intended Use
|
| 72 |
+
#### Multilingual?
|
| 73 |
+
<!-- quick -->
|
| 74 |
+
<!-- info: Is the dataset multilingual? -->
|
| 75 |
+
<!-- scope: telescope -->
|
| 76 |
+
yes
|
| 77 |
+
#### Covered Dialects
|
| 78 |
+
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
|
| 79 |
+
<!-- scope: periscope -->
|
| 80 |
+
Dataset does not have multiple dialects per language.
|
| 81 |
+
#### Covered Languages
|
| 82 |
+
<!-- quick -->
|
| 83 |
+
<!-- info: What languages/dialects are covered in the dataset? -->
|
| 84 |
+
<!-- scope: telescope -->
|
| 85 |
+
`English`, `Spanish, Castilian`, `Portuguese`, `French`, `German`, `Russian`, `Italian`, `Indonesian`, `Dutch, Flemish`, `Arabic`, `Chinese`, `Vietnamese`, `Thai`, `Japanese`, `Korean`, `Hindi`, `Czech`, `Turkish`
|
| 86 |
+
#### Whose Language?
|
| 87 |
+
<!-- info: Whose language is in the dataset? -->
|
| 88 |
+
<!-- scope: periscope -->
|
| 89 |
+
No information about the user demographic is available.
|
| 90 |
+
#### License
|
| 91 |
+
<!-- quick -->
|
| 92 |
+
<!-- info: What is the license of the dataset? -->
|
| 93 |
+
<!-- scope: telescope -->
|
| 94 |
+
cc-by-3.0: Creative Commons Attribution 3.0 Unported
|
| 95 |
+
#### Intended Use
|
| 96 |
+
<!-- info: What is the intended use of the dataset? -->
|
| 97 |
+
<!-- scope: microscope -->
|
| 98 |
+
The dataset was intended to serve as a large-scale, high-quality benchmark dataset for cross-lingual summarization.
|
| 99 |
+
#### Primary Task
|
| 100 |
+
<!-- info: What primary task does the dataset support? -->
|
| 101 |
+
<!-- scope: telescope -->
|
| 102 |
+
Summarization
|
| 103 |
+
#### Communicative Goal
|
| 104 |
+
<!-- quick -->
|
| 105 |
+
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
|
| 106 |
+
<!-- scope: periscope -->
|
| 107 |
+
Produce a high quality summary for the given input article.
|
| 108 |
+
### Credit
|
| 109 |
+
#### Curation Organization Type(s)
|
| 110 |
+
<!-- info: In what kind of organization did the dataset curation happen? -->
|
| 111 |
+
<!-- scope: telescope -->
|
| 112 |
+
`academic`
|
| 113 |
+
#### Curation Organization(s)
|
| 114 |
+
<!-- info: Name the organization(s). -->
|
| 115 |
+
<!-- scope: periscope -->
|
| 116 |
+
Columbia University
|
| 117 |
+
#### Dataset Creators
|
| 118 |
+
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
|
| 119 |
+
<!-- scope: microscope -->
|
| 120 |
+
Faisal Ladhak (Columbia University), Esin Durmus (Stanford University), Claire Cardie (Cornell University), Kathleen McKeown (Columbia University)
|
| 121 |
+
#### Who added the Dataset to GEM?
|
| 122 |
+
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
|
| 123 |
+
<!-- scope: microscope -->
|
| 124 |
+
Jenny Chim (Queen Mary University of London), Faisal Ladhak (Columbia University)
|
| 125 |
+
### Dataset Structure
|
| 126 |
+
#### Data Fields
|
| 127 |
+
<!-- info: List and describe the fields present in the dataset. -->
|
| 128 |
+
<!-- scope: telescope -->
|
| 129 |
+
gem_id -- The id for the data instance.
|
| 130 |
+
source_language -- The language of the source article.
|
| 131 |
+
target_language -- The language of the target summary.
|
| 132 |
+
source -- The source document.
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
#### Example Instance
|
| 136 |
+
|
| 137 |
+
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
|
| 138 |
+
<!-- scope: periscope -->
|
| 139 |
+
{
|
| 140 |
+
"gem_id": "wikilingua_crosslingual-train-12345",
|
| 141 |
+
"gem_parent_id": "wikilingua_crosslingual-train-12345",
|
| 142 |
+
"source_language": "fr",
|
| 143 |
+
"target_language": "de",
|
| 144 |
+
"source": "Document in fr",
|
| 145 |
+
"target": "Summary in de",
|
| 146 |
+
}
|
| 147 |
+
#### Data Splits
|
| 148 |
+
|
| 149 |
+
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
|
| 150 |
+
<!-- scope: periscope -->
|
| 151 |
+
The data is split into train/dev/test. In addition to the full test set, there's also a sampled version of the test set.
|
| 152 |
+
|
| 153 |
+
#### Splitting Criteria
|
| 154 |
+
|
| 155 |
+
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
|
| 156 |
+
<!-- scope: microscope -->
|
| 157 |
+
The data was split to ensure the same document would appear in the same split across languages so as to ensure there's no leakage into the test set.
|
| 158 |
+
|
| 159 |
+
|
| 160 |
+
|
| 161 |
+
## Dataset in GEM
|
| 162 |
+
|
| 163 |
+
### Rationale for Inclusion in GEM
|
| 164 |
+
|
| 165 |
+
#### Why is the Dataset in GEM?
|
| 166 |
+
|
| 167 |
+
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
|
| 168 |
+
<!-- scope: microscope -->
|
| 169 |
+
This dataset provides a large-scale, high-quality resource for cross-lingual summarization in 18 languages, increasing the coverage of languages for the GEM summarization task.
|
| 170 |
+
|
| 171 |
+
#### Similar Datasets
|
| 172 |
+
|
| 173 |
+
<!-- info: Do other datasets for the high level task exist? -->
|
| 174 |
+
<!-- scope: telescope -->
|
| 175 |
+
yes
|
| 176 |
+
|
| 177 |
+
#### Unique Language Coverage
|
| 178 |
+
|
| 179 |
+
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
|
| 180 |
+
<!-- scope: periscope -->
|
| 181 |
+
yes
|
| 182 |
+
|
| 183 |
+
#### Difference from other GEM datasets
|
| 184 |
+
|
| 185 |
+
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
|
| 186 |
+
<!-- scope: microscope -->
|
| 187 |
+
XSum covers English news articles, and MLSum covers news articles in German and Spanish.
|
| 188 |
+
In contrast, this dataset has how-to articles in 18 languages, substantially increasing the languages covered. Moreover, it also provides a a different domain than the other two datasets.
|
| 189 |
+
|
| 190 |
+
#### Ability that the Dataset measures
|
| 191 |
+
|
| 192 |
+
<!-- info: What aspect of model ability can be measured with this dataset? -->
|
| 193 |
+
<!-- scope: periscope -->
|
| 194 |
+
The ability to generate quality summaries across multiple languages.
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
### GEM-Specific Curation
|
| 198 |
+
|
| 199 |
+
#### Modificatied for GEM?
|
| 200 |
+
|
| 201 |
+
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
|
| 202 |
+
<!-- scope: telescope -->
|
| 203 |
+
yes
|
| 204 |
+
|
| 205 |
+
#### GEM Modifications
|
| 206 |
+
|
| 207 |
+
<!-- info: What changes have been made to he original dataset? -->
|
| 208 |
+
<!-- scope: periscope -->
|
| 209 |
+
`other`
|
| 210 |
+
|
| 211 |
+
#### Modification Details
|
| 212 |
+
|
| 213 |
+
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
|
| 214 |
+
<!-- scope: microscope -->
|
| 215 |
+
Previous version had separate data loaders for each language. In this version, we've created a single monolingual data loader, which contains monolingual data in each of the 18 languages. In addition, we've also created a single cross-lingual data loader across all the language pairs in the dataset.
|
| 216 |
+
|
| 217 |
+
#### Additional Splits?
|
| 218 |
+
|
| 219 |
+
<!-- info: Does GEM provide additional splits to the dataset? -->
|
| 220 |
+
<!-- scope: telescope -->
|
| 221 |
+
no
|
| 222 |
+
|
| 223 |
+
|
| 224 |
+
### Getting Started with the Task
|
| 225 |
+
|
| 226 |
+
|
| 227 |
+
|
| 228 |
+
|
| 229 |
+
## Previous Results
|
| 230 |
+
|
| 231 |
+
### Previous Results
|
| 232 |
+
|
| 233 |
+
#### Measured Model Abilities
|
| 234 |
+
|
| 235 |
+
<!-- info: What aspect of model ability can be measured with this dataset? -->
|
| 236 |
+
<!-- scope: telescope -->
|
| 237 |
+
Ability to summarize content across different languages.
|
| 238 |
+
|
| 239 |
+
#### Metrics
|
| 240 |
+
|
| 241 |
+
<!-- info: What metrics are typically used for this task? -->
|
| 242 |
+
<!-- scope: periscope -->
|
| 243 |
+
`ROUGE`
|
| 244 |
+
|
| 245 |
+
#### Proposed Evaluation
|
| 246 |
+
|
| 247 |
+
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
|
| 248 |
+
<!-- scope: microscope -->
|
| 249 |
+
ROUGE is used to measure content selection by comparing word overlap with reference summaries. In addition, the authors of the dataset also used human evaluation to evaluate content selection and fluency of the systems.
|
| 250 |
+
|
| 251 |
+
#### Previous results available?
|
| 252 |
+
|
| 253 |
+
<!-- info: Are previous results available? -->
|
| 254 |
+
<!-- scope: telescope -->
|
| 255 |
+
no
|
| 256 |
+
|
| 257 |
+
|
| 258 |
+
|
| 259 |
+
## Dataset Curation
|
| 260 |
+
|
| 261 |
+
### Original Curation
|
| 262 |
+
|
| 263 |
+
#### Original Curation Rationale
|
| 264 |
+
|
| 265 |
+
<!-- info: Original curation rationale -->
|
| 266 |
+
<!-- scope: telescope -->
|
| 267 |
+
The dataset was created in order to enable new approaches for cross-lingual and multilingual summarization, which are currently understudied as well as open up inetersting new directions for research in summarization. E.g., exploration of multi-source cross-lingual architectures, i.e. models that can summarize from multiple source languages into a target language, building models that can summarize articles from any language to any other language for a given set of languages.
|
| 268 |
+
|
| 269 |
+
#### Communicative Goal
|
| 270 |
+
|
| 271 |
+
<!-- info: What was the communicative goal? -->
|
| 272 |
+
<!-- scope: periscope -->
|
| 273 |
+
Given an input article, produce a high quality summary of the article in the target language.
|
| 274 |
+
|
| 275 |
+
#### Sourced from Different Sources
|
| 276 |
+
|
| 277 |
+
<!-- info: Is the dataset aggregated from different data sources? -->
|
| 278 |
+
<!-- scope: telescope -->
|
| 279 |
+
no
|
| 280 |
+
|
| 281 |
+
|
| 282 |
+
### Language Data
|
| 283 |
+
|
| 284 |
+
#### How was Language Data Obtained?
|
| 285 |
+
|
| 286 |
+
<!-- info: How was the language data obtained? -->
|
| 287 |
+
<!-- scope: telescope -->
|
| 288 |
+
`Found`
|
| 289 |
+
|
| 290 |
+
#### Where was it found?
|
| 291 |
+
|
| 292 |
+
<!-- info: If found, where from? -->
|
| 293 |
+
<!-- scope: telescope -->
|
| 294 |
+
`Single website`
|
| 295 |
+
|
| 296 |
+
#### Language Producers
|
| 297 |
+
|
| 298 |
+
<!-- info: What further information do we have on the language producers? -->
|
| 299 |
+
<!-- scope: microscope -->
|
| 300 |
+
WikiHow, which is an online resource of how-to guides (written and reviewed by human authors) is used as the data source.
|
| 301 |
+
|
| 302 |
+
#### Topics Covered
|
| 303 |
+
|
| 304 |
+
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
|
| 305 |
+
<!-- scope: periscope -->
|
| 306 |
+
The articles cover 19 broad categories including health, arts and entertainment, personal care and style, travel, education and communications, etc. The categories cover a broad set of genres and topics.
|
| 307 |
+
|
| 308 |
+
#### Data Validation
|
| 309 |
+
|
| 310 |
+
<!-- info: Was the text validated by a different worker or a data curator? -->
|
| 311 |
+
<!-- scope: telescope -->
|
| 312 |
+
not validated
|
| 313 |
+
|
| 314 |
+
#### Was Data Filtered?
|
| 315 |
+
|
| 316 |
+
<!-- info: Were text instances selected or filtered? -->
|
| 317 |
+
<!-- scope: telescope -->
|
| 318 |
+
not filtered
|
| 319 |
+
|
| 320 |
+
|
| 321 |
+
### Structured Annotations
|
| 322 |
+
|
| 323 |
+
#### Additional Annotations?
|
| 324 |
+
|
| 325 |
+
<!-- quick -->
|
| 326 |
+
<!-- info: Does the dataset have additional annotations for each instance? -->
|
| 327 |
+
<!-- scope: telescope -->
|
| 328 |
+
none
|
| 329 |
+
|
| 330 |
+
#### Annotation Service?
|
| 331 |
+
|
| 332 |
+
<!-- info: Was an annotation service used? -->
|
| 333 |
+
<!-- scope: telescope -->
|
| 334 |
+
no
|
| 335 |
+
|
| 336 |
+
|
| 337 |
+
### Consent
|
| 338 |
+
|
| 339 |
+
#### Any Consent Policy?
|
| 340 |
+
|
| 341 |
+
<!-- info: Was there a consent policy involved when gathering the data? -->
|
| 342 |
+
<!-- scope: telescope -->
|
| 343 |
+
yes
|
| 344 |
+
|
| 345 |
+
#### Consent Policy Details
|
| 346 |
+
|
| 347 |
+
<!-- info: What was the consent policy? -->
|
| 348 |
+
<!-- scope: microscope -->
|
| 349 |
+
(1) Text Content. All text posted by Users to the Service is sub-licensed by wikiHow to other Users under a Creative Commons license as provided herein. The Creative Commons license allows such text content be used freely for non-commercial purposes, so long as it is used and attributed to the original author as specified under the terms of the license. Allowing free republication of our articles helps wikiHow achieve its mission by providing instruction on solving the problems of everyday life to more people for free. In order to support this goal, wikiHow hereby grants each User of the Service a license to all text content that Users contribute to the Service under the terms and conditions of a Creative Commons CC BY-NC-SA 3.0 License. Please be sure to read the terms of the license carefully. You continue to own all right, title, and interest in and to your User Content, and you are free to distribute it as you wish, whether for commercial or non-commercial purposes.
|
| 350 |
+
|
| 351 |
+
#### Other Consented Downstream Use
|
| 352 |
+
|
| 353 |
+
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
|
| 354 |
+
<!-- scope: microscope -->
|
| 355 |
+
The data is made freely available under the Creative Commons license, therefore there are no restrictions about downstream uses as long is it's for non-commercial purposes.
|
| 356 |
+
|
| 357 |
+
|
| 358 |
+
### Private Identifying Information (PII)
|
| 359 |
+
|
| 360 |
+
#### Contains PII?
|
| 361 |
+
|
| 362 |
+
<!-- quick -->
|
| 363 |
+
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
|
| 364 |
+
<!-- scope: telescope -->
|
| 365 |
+
no PII
|
| 366 |
+
|
| 367 |
+
#### Justification for no PII
|
| 368 |
+
|
| 369 |
+
<!-- info: Provide a justification for selecting `no PII` above. -->
|
| 370 |
+
<!-- scope: periscope -->
|
| 371 |
+
Only the article text and summaries were collected. No user information was retained in the dataset.
|
| 372 |
+
|
| 373 |
+
|
| 374 |
+
### Maintenance
|
| 375 |
+
|
| 376 |
+
#### Any Maintenance Plan?
|
| 377 |
+
|
| 378 |
+
<!-- info: Does the original dataset have a maintenance plan? -->
|
| 379 |
+
<!-- scope: telescope -->
|
| 380 |
+
no
|
| 381 |
+
|
| 382 |
+
|
| 383 |
+
|
| 384 |
+
## Broader Social Context
|
| 385 |
+
|
| 386 |
+
### Previous Work on the Social Impact of the Dataset
|
| 387 |
+
|
| 388 |
+
#### Usage of Models based on the Data
|
| 389 |
+
|
| 390 |
+
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
|
| 391 |
+
<!-- scope: telescope -->
|
| 392 |
+
yes - other datasets featuring the same task
|
| 393 |
+
|
| 394 |
+
|
| 395 |
+
### Impact on Under-Served Communities
|
| 396 |
+
|
| 397 |
+
#### Addresses needs of underserved Communities?
|
| 398 |
+
|
| 399 |
+
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
|
| 400 |
+
<!-- scope: telescope -->
|
| 401 |
+
no
|
| 402 |
+
|
| 403 |
+
|
| 404 |
+
### Discussion of Biases
|
| 405 |
+
|
| 406 |
+
#### Any Documented Social Biases?
|
| 407 |
+
|
| 408 |
+
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
|
| 409 |
+
<!-- scope: telescope -->
|
| 410 |
+
yes
|
| 411 |
+
|
| 412 |
+
|
| 413 |
+
|
| 414 |
+
## Considerations for Using the Data
|
| 415 |
+
|
| 416 |
+
### PII Risks and Liability
|
| 417 |
+
|
| 418 |
+
|
| 419 |
+
|
| 420 |
+
### Licenses
|
| 421 |
+
|
| 422 |
+
#### Copyright Restrictions on the Dataset
|
| 423 |
+
|
| 424 |
+
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
|
| 425 |
+
<!-- scope: periscope -->
|
| 426 |
+
`non-commercial use only`
|
| 427 |
+
|
| 428 |
+
#### Copyright Restrictions on the Language Data
|
| 429 |
+
|
| 430 |
+
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
|
| 431 |
+
<!-- scope: periscope -->
|
| 432 |
+
`non-commercial use only`
|
| 433 |
+
|
| 434 |
+
|
| 435 |
+
### Known Technical Limitations
|
| 436 |
+
|
| 437 |
+
|
| 438 |
+
|
| 439 |
+
|