id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
DanArnin/Hinglish | 2023-09-27T04:12:50.000Z | [
"region:us"
] | DanArnin | null | null | null | 0 | 9 | Entry not found |
nitinbhayana/review-prompt-v2 | 2023-09-27T08:47:12.000Z | [
"region:us"
] | nitinbhayana | null | null | null | 0 | 9 | Entry not found |
zuzannad1/preprocessed_xsum | 2023-09-29T16:05:47.000Z | [
"region:us"
] | zuzannad1 | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: pixel_values
sequence:
sequence:
sequence: float32
- name: attention_mask
sequence: float32
- name: label_ids
sequence: int64
splits:
- name: train
num_bytes: 332148531900
num_examples: 204045
- name: val
num_bytes: 18446456240
num_examples: 11332
- name: test
num_bytes: 18449711880
num_examples: 11334
download_size: 14879560476
dataset_size: 369044700020
---
# Dataset Card for "preprocessed_xsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
siddanshchawla/finqa | 2023-09-27T16:00:25.000Z | [
"region:us"
] | siddanshchawla | null | null | null | 0 | 9 | Entry not found |
alpayariyak/orca_mini | 2023-09-28T03:13:19.000Z | [
"region:us"
] | alpayariyak | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: system
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 62321431
num_examples: 56037
download_size: 30816818
dataset_size: 62321431
---
# Dataset Card for "orca_mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AfshanAhmed/training-data | 2023-09-28T06:00:55.000Z | [
"region:us"
] | AfshanAhmed | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 301571473.0
num_examples: 300
download_size: 301565751
dataset_size: 301571473.0
---
# Dataset Card for "training-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AnthonyRayo/AutomAssist3 | 2023-09-28T09:21:43.000Z | [
"region:us"
] | AnthonyRayo | null | null | null | 0 | 9 | Entry not found |
Sneka/valid | 2023-09-30T06:10:11.000Z | [
"region:us"
] | Sneka | null | null | null | 0 | 9 | Entry not found |
Nicolas-BZRD/JADE_opendata | 2023-09-29T14:55:39.000Z | [
"size_categories:100K<n<1M",
"language:fr",
"license:odc-by",
"legal",
"region:us"
] | Nicolas-BZRD | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5674266682
num_examples: 558649
download_size: 2253639724
dataset_size: 5674266682
license: odc-by
language:
- fr
tags:
- legal
size_categories:
- 100K<n<1M
---
# JADE
[Decisions of the Council of State, administrative courts of appeal, and the Court of Conflicts.](https://echanges.dila.gouv.fr/OPENDATA/JADE/)<br>
For the Council of State:
- the "landmark judgments" that established administrative law;
- decisions published in the Official Collection of Council of State Decisions (Lebon collection) since 1965;
- a limited selection of unpublished decisions in the collection between 1975 and 1986, with an expanded selection since 1986.
For the Administrative Courts of Appeal (CAA):
- a selection of judgments, varying for each of the 8 Courts, dating back to the establishment of the respective Court (1989 for the oldest CAAs).
For the administrative tribunals:
- A very limited selection starting in 1965, consisting of judgments chosen for publication or reference in the Lebon collection. |
Nicolas-BZRD/SARDE_opendata | 2023-09-29T14:40:36.000Z | [
"size_categories:100K<n<1M",
"language:fr",
"license:odc-by",
"legal",
"region:us"
] | Nicolas-BZRD | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 96924578
num_examples: 224476
download_size: 36650583
dataset_size: 96924578
license: odc-by
language:
- fr
tags:
- legal
size_categories:
- 100K<n<1M
---
# SARDE (Système d'Aide à la Recherche Documentaire Elaborée)
[SARDE](https://echanges.dila.gouv.fr/OPENDATA/SARDE/) is a repository designed to provide a thematic search mode for the majority of legislative and regulatory texts in force.
The texts referenced are those published in the "Laws and Decrees" edition of the Journal officiel and in the Bulletins officiels distributed by the DILA. |
hassankhan434/WyomingtestData | 2023-10-01T00:15:59.000Z | [
"region:us"
] | hassankhan434 | null | null | null | 0 | 9 | Entry not found |
vladisha3000/test | 2023-09-30T10:27:32.000Z | [
"region:us"
] | vladisha3000 | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13078551
num_examples: 5001
download_size: 13117596
dataset_size: 13078551
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pphuc25/vlsp2023-test | 2023-10-01T06:12:48.000Z | [
"region:us"
] | pphuc25 | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: path
dtype: string
splits:
- name: train
num_bytes: 5694963666.0
num_examples: 50000
download_size: 4334044163
dataset_size: 5694963666.0
---
# Dataset Card for "vlsp2023-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pphuc25/vlsp2023-test2 | 2023-10-01T07:50:29.000Z | [
"region:us"
] | pphuc25 | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: path
dtype: string
splits:
- name: train
num_bytes: 6248152210.804
num_examples: 54874
download_size: 6346575989
dataset_size: 6248152210.804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vlsp2023-test2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
VuongQuoc/60k_dataset_multichoice_512 | 2023-10-01T09:50:52.000Z | [
"region:us"
] | VuongQuoc | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: token_type_ids
sequence:
sequence: int8
- name: attention_mask
sequence:
sequence: int8
- name: label
dtype: int64
splits:
- name: train
num_bytes: 77100610
num_examples: 5000
- name: test
num_bytes: 3088000
num_examples: 200
download_size: 7918277
dataset_size: 80188610
---
# Dataset Card for "60k_dataset_multichoice_512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mxode/StackOverflow-QA-C-Language-5k | 2023-10-02T10:30:48.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"code",
"region:us"
] | Mxode | null | null | null | 1 | 9 | ---
license: apache-2.0
language:
- en
tags:
- code
task_categories:
- question-answering
size_categories:
- 1K<n<10K
---
This is a collection of ~5000 QA's in **C Language** from StackOverflow. The data has been initially cleaned, and each response is with **Accepted Answer**.
All data is **<500** in length.
The questions and answers were organized into a **one-line** format. A sample format is shown below:
```json
{
"question": "```\nFILE* file = fopen(some file)\n\npcap_t* pd = pcap_fopen_offline(file)\n\npcap_close(pd)\n\nfclose(file)\n```\n\nThis code occurs double free error.\n\nCould you explain about this happening?\n\nMy Guess is that pd and file pointers are sharing some datas.\n",
"answer": "As the documentation says, thepcap_closefunction closes the files associated with thepcap_tstructure passed to it. Closing the file again withfcloseis an error.\n"
}
``` |
manu/english-60b | 2023-10-02T19:17:18.000Z | [
"region:us"
] | manu | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
- name: dataset_id
dtype: string
splits:
- name: train
num_bytes: 259969046699
num_examples: 58986336
- name: test
num_bytes: 43278365
num_examples: 10000
download_size: 151705709032
dataset_size: 260012325064
---
# Dataset Card for "english_20b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davanstrien/fake-library-chats-with-sentiment | 2023-10-02T20:21:26.000Z | [
"region:us"
] | davanstrien | null | null | null | 0 | 9 | ---
dataset_info:
- config_name: default
features:
- name: message
dtype: string
- name: message sentiment
dtype:
class_label:
names:
'0': positive
'1': negative
'2': neutral
splits:
- name: train
num_bytes: 674584
num_examples: 10000
download_size: 0
dataset_size: 674584
- config_name: demo
features:
- name: message
dtype: string
- name: message sentiment
dtype:
class_label:
names:
'0': positive
'1': negative
'2': neutral
splits:
- name: train
num_bytes: 674584
num_examples: 10000
download_size: 28880
dataset_size: 674584
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: demo
data_files:
- split: train
path: demo/train-*
---
# Dataset Card for "fake-library-chats-with-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Dong237/empathetic_dialogues_instruction | 2023-10-03T18:30:50.000Z | [
"region:us"
] | Dong237 | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: instruction
dtype: string
- name: dialogue
dtype: string
splits:
- name: train
num_bytes: 6392746
num_examples: 17780
- name: validation
num_bytes: 1076044
num_examples: 2758
- name: test
num_bytes: 1037401
num_examples: 2540
download_size: 4612892
dataset_size: 8506191
---
# Dataset Card for "empathetic_dialogues_instruction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
klima7/pol-spider-dev | 2023-10-08T11:36:15.000Z | [
"region:us"
] | klima7 | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train_spider
path: data/train_spider-*
- split: train_others
path: data/train_others-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: query_toks_no_value
sequence: string
- name: query_en
dtype: string
- name: question_en
dtype: string
- name: db_id
dtype: string
- name: query_pl
dtype: string
- name: question_pl
dtype: string
splits:
- name: train_spider
num_bytes: 4055253
num_examples: 7000
- name: train_others
num_bytes: 1287869
num_examples: 1659
- name: dev
num_bytes: 582922
num_examples: 1034
download_size: 1152383
dataset_size: 5926044
---
# Dataset Card for "pol-spider-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cadaeic/2000-sample-synthetic-recipe-dataset | 2023-10-04T22:44:10.000Z | [
"language:en",
"region:us"
] | cadaeic | null | null | null | 0 | 9 | ---
language:
- en
---
Dataset pairing GPT-4 synthesized instructions with outputs from [RecipeNLG](https://www.kaggle.com/datasets/paultimothymooney/recipenlg) in Axolotl's "alpaca" jsonl format |
Trelis/openassistant-guanaco-EOS | 2023-10-04T16:17:59.000Z | [
"size_categories:1K<n<10k",
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",... | Trelis | null | null | null | 0 | 9 | ---
license: apache-2.0
language:
- en
- es
- ru
- de
- pl
- th
- vi
- sv
- bn
- da
- he
- it
- fa
- sk
- id
- nb
- el
- nl
- hu
- eu
- zh
- eo
- ja
- ca
- cs
- bg
- fi
- pt
- tr
- ro
- ar
- uk
- gl
- fr
- ko
tags:
- human-feedback
- llama-2
size_categories:
- 1K<n<10k
pretty_name: Filtered OpenAssistant Conversations
---
# Chat Fine-tuning Dataset - Guanaco Style
This dataset allows for fine-tuning chat models using "### Human:" AND "### Assistant" as the beginning and end of sequence tokens.
Preparation:
1. The dataset is cloned from [TimDettmers](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), which itself is a subset of the Open Assistant dataset, which you can find [here](https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main). This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
1. The dataset was then slightly adjusted to:
- if a row of data ends with an assistant response, then "### Human" was additionally added to the end of that row of data.
Details of the root dataset follow, copied from that repo:
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- **Homepage:** https://www.open-assistant.io/
- **Repository:** https://github.com/LAION-AI/Open-Assistant
- **Paper:** https://arxiv.org/abs/2304.07327
### Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
Please refer to our [paper](https://arxiv.org/abs/2304.07327) for further details.
### Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant".
This version of the dataset contains data collected on the [open-assistant.io](https://open-assistant.io/) website until April 12 2023.
### JSON Example: Message
For readability, the following JSON examples are shown formatted with indentation on multiple lines.
Objects are stored without indentation (on single lines) in the actual jsonl files.
```json
{
"message_id": "218440fd-5317-4355-91dc-d001416df62b",
"parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4",
"user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4",
"text": "It was the winter of 2035, and artificial intelligence (..)",
"role": "assistant",
"lang": "en",
"review_count": 3,
"review_result": true,
"deleted": false,
"rank": 0,
"synthetic": true,
"model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)",
"labels": {
"spam": { "value": 0.0, "count": 3 },
"lang_mismatch": { "value": 0.0, "count": 3 },
"pii": { "value": 0.0, "count": 3 },
"not_appropriate": { "value": 0.0, "count": 3 },
"hate_speech": { "value": 0.0, "count": 3 },
"sexual_content": { "value": 0.0, "count": 3 },
"quality": { "value": 0.416, "count": 3 },
"toxicity": { "value": 0.16, "count": 3 },
"humor": { "value": 0.0, "count": 3 },
"creativity": { "value": 0.33, "count": 3 },
"violence": { "value": 0.16, "count": 3 }
}
}
```
### JSON Example: Conversation Tree
For readability, only a subset of the message properties is shown here.
```json
{
"message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"tree_state": "ready_for_export",
"prompt": {
"message_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"text": "Why can't we divide by 0? (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8",
"text": "The reason we cannot divide by zero is because (..)",
"role": "assistant",
"lang": "en",
"replies": [
// ...
]
},
{
"message_id": "84d0913b-0fd9-4508-8ef5-205626a7039d",
"text": "The reason that the result of a division by zero is (..)",
"role": "assistant",
"lang": "en",
"replies": [
{
"message_id": "3352725e-f424-4e3b-a627-b6db831bdbaa",
"text": "Math is confusing. Like those weird Irrational (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "f46207ca-3149-46e9-a466-9163d4ce499c",
"text": "Irrational numbers are simply numbers (..)",
"role": "assistant",
"lang": "en",
"replies": []
},
// ...
]
}
]
}
]
}
}
```
Please refer to [oasst-data](https://github.com/LAION-AI/Open-Assistant/tree/main/oasst-data) for
details about the data structure and Python code to read and write jsonl files containing oasst data objects.
If you would like to explore the dataset yourself you can find a
[`getting-started`](https://github.com/LAION-AI/Open-Assistant/blob/main/notebooks/openassistant-oasst1/getting-started.ipynb)
notebook in the `notebooks/openassistant-oasst1` folder of the [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
github repository.
## Main Dataset Files
Conversation data is provided either as nested messages in trees (extension `.trees.jsonl.gz`)
or as a flat list (table) of messages (extension `.messages.jsonl.gz`).
### Ready For Export Trees
```
2023-04-12_oasst_ready.trees.jsonl.gz 10,364 trees with 88,838 total messages
2023-04-12_oasst_ready.messages.jsonl.gz 88,838 messages
```
Trees in `ready_for_export` state without spam and deleted messages including message labels.
The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.
### All Trees
```
2023-04-12_oasst_all.trees.jsonl.gz 66,497 trees with 161,443 total messages
2023-04-12_oasst_all.messages.jsonl.gz 161,443 messages
```
All trees, including those in states `prompt_lottery_waiting` (trees that consist of only one message, namely the initial prompt),
`aborted_low_grade` (trees that stopped growing because the messages had low quality), and `halted_by_moderator`.
### Supplemental Exports: Spam & Prompts
```
2023-04-12_oasst_spam.messages.jsonl.gz
```
These are messages which were deleted or have a negative review result (`"review_result": false`).
Besides low quality, a frequent reason for message deletion is a wrong language tag.
```
2023-04-12_oasst_prompts.messages.jsonl.gz
```
These are all the kept initial prompt messages with positive review result (no spam) of trees in `ready_for_export` or `prompt_lottery_waiting` state.
### Using the Huggingface Datasets
While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.
Nevertheless, we make all messages which can also be found in the file `2023-04-12_oasst_ready.trees.jsonl.gz` available in parquet as train/validation splits.
These are directly loadable by [Huggingface Datasets](https://pypi.org/project/datasets/).
To load the oasst1 train & validation splits use:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/oasst1")
train = ds['train'] # len(train)=84437 (95%)
val = ds['validation'] # len(val)=4401 (5%)
```
The messages appear in depth-first order of the message trees.
Full conversation trees can be reconstructed from the flat messages table by using the `parent_id`
and `message_id` properties to identify the parent-child relationship of messages. The `message_tree_id`
and `tree_state` properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.
### Languages
OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:
**Languages with over 1000 messages**
- English: 71956
- Spanish: 43061
- Russian: 9089
- German: 5279
- Chinese: 4962
- French: 4251
- Thai: 3042
- Portuguese (Brazil): 2969
- Catalan: 2260
- Korean: 1553
- Ukrainian: 1352
- Italian: 1320
- Japanese: 1018
<details>
<summary><b>Languages with under 1000 messages</b></summary>
<ul>
<li>Vietnamese: 952</li>
<li>Basque: 947</li>
<li>Polish: 886</li>
<li>Hungarian: 811</li>
<li>Arabic: 666</li>
<li>Dutch: 628</li>
<li>Swedish: 512</li>
<li>Turkish: 454</li>
<li>Finnish: 386</li>
<li>Czech: 372</li>
<li>Danish: 358</li>
<li>Galician: 339</li>
<li>Hebrew: 255</li>
<li>Romanian: 200</li>
<li>Norwegian Bokmål: 133</li>
<li>Indonesian: 115</li>
<li>Bulgarian: 95</li>
<li>Bengali: 82</li>
<li>Persian: 72</li>
<li>Greek: 66</li>
<li>Esperanto: 59</li>
<li>Slovak: 19</li>
</ul>
</details>
## Contact
- Discord [Open Assistant Discord Server](https://ykilcher.com/open-assistant-discord)
- GitHub: [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
- E-Mail: [open-assistant@laion.ai](mailto:open-assistant@laion.ai) |
Sharka/CIVQA_easyocr_simple_train_half | 2023-10-04T15:48:19.000Z | [
"region:us"
] | Sharka | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: id
dtype: string
- name: words
sequence: string
- name: answers
dtype: string
- name: bboxes
sequence:
sequence: float32
- name: answers_bboxes
sequence:
sequence: float32
- name: questions
dtype: string
- name: image
dtype: string
splits:
- name: train
num_bytes: 963207990
num_examples: 143765
download_size: 41076905
dataset_size: 963207990
---
# Dataset Card for "CIVQA_easyocr_simple_train_half"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Intuit-GenSRF/hate-speech18 | 2023-10-05T01:09:57.000Z | [
"region:us"
] | Intuit-GenSRF | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: text
dtype: string
- name: user_id
dtype: int64
- name: subforum_id
dtype: int64
- name: num_contexts
dtype: int64
- name: labels
sequence: string
splits:
- name: train
num_bytes: 1343052
num_examples: 10944
download_size: 772056
dataset_size: 1343052
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hate_speech18"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Intuit-GenSRF/hate-speech-offensive | 2023-10-05T01:24:37.000Z | [
"region:us"
] | Intuit-GenSRF | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 2576536
num_examples: 24783
download_size: 1560109
dataset_size: 2576536
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hate_speech_offensive"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Intuit-GenSRF/tweets-hate-speech-detection | 2023-10-05T01:27:16.000Z | [
"region:us"
] | Intuit-GenSRF | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 3081848
num_examples: 31962
download_size: 2067751
dataset_size: 3081848
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tweets_hate_speech_detection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AustinMcMike/steve_jobs_quotes_3 | 2023-10-05T03:08:40.000Z | [
"region:us"
] | AustinMcMike | null | null | null | 0 | 9 | Entry not found |
Sathvik-24/chacha300 | 2023-10-05T14:56:36.000Z | [
"region:us"
] | Sathvik-24 | null | null | null | 0 | 9 | Entry not found |
vsarathy/nl-robotics-translation-simple_english-12k-no-context-TEST | 2023-10-05T19:54:36.000Z | [
"region:us"
] | vsarathy | null | null | null | 0 | 9 | Entry not found |
dmrau/trec_dl19-qrels | 2023-10-09T13:07:40.000Z | [
"region:us"
] | dmrau | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: string
splits:
- name: test
num_bytes: 242652
num_examples: 9260
download_size: 0
dataset_size: 242652
---
# Dataset Card for "trec_dl19-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BBuf/chid | 2023-10-07T06:33:11.000Z | [
"region:us"
] | BBuf | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: candidates
sequence: string
- name: content
dtype: string
- name: answer
dtype: int64
splits:
- name: train
num_bytes: 88466
num_examples: 202
- name: validation
num_bytes: 87327
num_examples: 202
download_size: 140651
dataset_size: 175793
---
# Dataset Card for "chid"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Zaid/iAshaar | 2023-10-07T08:01:46.000Z | [
"region:us"
] | Zaid | null | null | null | 0 | 9 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: poem title
dtype: string
- name: poem meter
dtype: string
- name: poem verses
sequence: string
- name: poem theme
dtype: string
- name: poem url
dtype: string
- name: poet name
dtype: string
- name: poet description
dtype: string
- name: poet url
dtype: string
- name: poet era
dtype: string
- name: poet location
dtype: string
- name: poem description
list:
- name: attributes
struct:
- name: class
dtype: string
- name: color
dtype: string
- name: dir
dtype: string
- name: face
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: children
list:
- name: attributes
struct:
- name: color
dtype: string
- name: dir
dtype: string
- name: face
dtype: string
- name: href
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: title
dtype: string
- name: value
dtype: string
- name: children
list:
- name: attributes
struct:
- name: class
dtype: string
- name: color
dtype: string
- name: dir
dtype: string
- name: face
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: children
list:
- name: attributes
struct:
- name: align
dtype: string
- name: face
dtype: string
- name: nowrap
dtype: string
- name: name
dtype: string
- name: parentAttributes
struct:
- name: lang
dtype: string
- name: style
dtype: string
- name: size
dtype: int64
- name: text
dtype: string
- name: truncated
dtype: bool
- name: type
dtype: string
- name: name
dtype: string
- name: parentAttributes
struct:
- name: dir
dtype: string
- name: face
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: partA
dtype: string
- name: size
dtype: int64
- name: text
dtype: string
- name: truncated
dtype: bool
- name: type
dtype: string
- name: name
dtype: string
- name: parentAttributes
struct:
- name: class
dtype: string
- name: color
dtype: string
- name: dir
dtype: string
- name: id
dtype: string
- name: lang
dtype: string
- name: style
dtype: string
- name: partA
dtype: string
- name: partB
dtype: string
- name: size
dtype: int64
- name: text
dtype: string
- name: truncated
dtype: bool
- name: type
dtype: string
- name: name
dtype: string
- name: parentAttributes
struct:
- name: dir
dtype: string
- name: style
dtype: string
- name: partA
dtype: string
- name: partB
dtype: string
- name: size
dtype: int64
- name: text
dtype: string
- name: truncated
dtype: bool
- name: type
dtype: string
- name: poem language type
dtype: string
- name: text
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 846365314
num_examples: 136422
download_size: 370389390
dataset_size: 846365314
---
# Dataset Card for "iAshaar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
H4438/tri-edu-date | 2023-10-08T18:14:26.000Z | [
"region:us"
] | H4438 | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: title
dtype: string
- name: url
dtype: string
- name: dates
sequence: string
- name: body
dtype: string
- name: head
dtype: string
- name: est_date
dtype: string
- name: ext_dates
sequence: string
- name: flt_dates
sequence: string
splits:
- name: train
num_bytes: 214580613
num_examples: 37239
download_size: 0
dataset_size: 214580613
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tri-edu-date"
Left: 3429 rows - 0.09%
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MattPiscopo/solvayllmtest | 2023-10-10T19:12:47.000Z | [
"region:us"
] | MattPiscopo | null | null | null | 0 | 9 | Entry not found |
Ckail/Needy_Girl_Overdose_P | 2023-10-08T11:21:07.000Z | [
"license:apache-2.0",
"region:us"
] | Ckail | null | null | null | 0 | 9 | ---
license: apache-2.0
---
|
benayas/snips | 2023-10-09T01:10:57.000Z | [
"license:apache-2.0",
"region:us"
] | benayas | null | null | null | 0 | 9 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 883074
num_examples: 13084
- name: test
num_bytes: 95870
num_examples: 1400
download_size: 414632
dataset_size: 978944
---
|
nguyenthanhdo/patent-en-v2-unfiltered | 2023-10-09T11:44:55.000Z | [
"region:us"
] | nguyenthanhdo | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: lang
dtype: string
- name: prompt
dtype: string
- name: prompt_len
dtype: int64
splits:
- name: train
num_bytes: 517272081.8613077
num_examples: 151166
download_size: 214906286
dataset_size: 517272081.8613077
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "patent-en-v2-unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vmalitskyi/images_rotation_dataset | 2023-10-09T14:02:02.000Z | [
"region:us"
] | vmalitskyi | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: uint8
- name: name
dtype: string
- name: sender_id
dtype: int64
- name: label
dtype: int64
- name: kids
dtype: int64
- name: class
dtype: int64
- name: fold
dtype: int64
splits:
- name: train
num_bytes: 32685477988
num_examples: 21612
download_size: 10754548456
dataset_size: 32685477988
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "images_rotation_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nguyenthanhdo/patent-vi-v2-unfiltered | 2023-10-09T13:53:24.000Z | [
"region:us"
] | nguyenthanhdo | null | null | null | 0 | 9 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: source
dtype: string
- name: lang
dtype: string
- name: prompt
dtype: string
- name: prompt_len
dtype: int64
- name: translated
dtype: bool
splits:
- name: train
num_bytes: 650384875.9860047
num_examples: 152108
download_size: 243829225
dataset_size: 650384875.9860047
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "patent-vi-v2-unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bianet | 2023-06-01T14:59:50.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:ku",
"language:tr",
"license:unknown",
"region:us"
] | null | A parallel news corpus in Turkish, Kurdish and English.
Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
3 languages, 3 bitexts
total number of files: 6
total number of tokens: 2.25M
total number of sentence fragments: 0.14M | @InProceedings{ATAMAN18.6,
author = {Duygu Ataman},
title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyazaki, Japan},
editor = {Jinhua Du and Mihael Arcan and Qun Liu and Hitoshi Isahara},
publisher = {European Language Resources Association (ELRA)},
address = {Paris, France},
isbn = {979-10-95546-15-3},
language = {english}
} | null | 0 | 8 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- ku
- tr
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bianet
pretty_name: Bianet
dataset_info:
- config_name: en_to_ku
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- ku
splits:
- name: train
num_bytes: 1800806
num_examples: 6402
download_size: 622420
dataset_size: 1800806
- config_name: en_to_tr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- tr
splits:
- name: train
num_bytes: 10231043
num_examples: 34770
download_size: 3544116
dataset_size: 10231043
- config_name: ku_to_tr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- ku
- tr
splits:
- name: train
num_bytes: 2086550
num_examples: 7325
download_size: 725227
dataset_size: 2086550
config_names:
- en-to-ku
- en-to-tr
- en_to_ku
- en_to_tr
- ku-to-tr
- ku_to_tr
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Bianet](http://opus.nlpl.eu/Bianet.php)
- **Repository:**
- **Paper:** [Ataman, D. (2018) Bianet: A Parallel News Corpus in Turkish, Kurdish and English. In Proceedings of the LREC 2018 Workshop MLP-Moment. pp. 14-17. pdf](http://lrec-conf.org/workshops/lrec2018/W19/pdf/6_W19.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A parallel news corpus in Turkish, Kurdish and English;
Bianet collects 3,214 Turkish articles with their sentence-aligned Kurdish or English translations from the Bianet online newspaper.
3 languages, 3 bitexts
total number of files: 6
total number of tokens: 2.25M
total number of sentence fragments: 0.14M
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC-BY-SA-4.0
### Citation Information
@InProceedings{ATAMAN18.6,
author = {Duygu Ataman},
title = {Bianet: A Parallel News Corpus in Turkish, Kurdish and English},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {may},
date = {7-12},
location = {Miyazaki, Japan},
editor = {Jinhua Du and Mihael Arcan and Qun Liu and Hitoshi Isahara},
publisher = {European Language Resources Association (ELRA)},
address = {Paris, France},
isbn = {979-10-95546-15-3},
language = {english}
}
### Contributions
Thanks to [@param087](https://github.com/param087) for adding this dataset. |
cdt | 2023-01-25T14:27:46.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"region:us"
] | null | The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content. | @article{ptaszynski2019results,
title={Results of the PolEval 2019 Shared Task 6: First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter},
author={Ptaszynski, Michal and Pieciukiewicz, Agata and Dybala, Pawel},
journal={Proceedings of the PolEval 2019 Workshop},
publisher={Institute of Computer Science, Polish Academy of Sciences},
pages={89},
year={2019}
} | null | 0 | 8 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: cdt
dataset_info:
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 1104322
num_examples: 10041
- name: test
num_bytes: 109681
num_examples: 1000
download_size: 375476
dataset_size: 1214003
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://2019.poleval.pl/index.php/tasks/
- **Repository:**
https://github.com/ptaszynski/cyberbullying-Polish
- **Paper:**
- **Leaderboard:**
https://klejbenchmark.com/leaderboard/
- **Point of Contact:**
### Dataset Summary
The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: an anonymized tweet in polish
- target: 1 if tweet is described as bullying, 0 otherwise. The test set doesn't have labels so -1 is used instead.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
BSD 3-Clause
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. |
hate_speech_portuguese | 2023-01-25T14:31:44.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:unknown",
"hate-speech-detection",
"region:us"
] | null | Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate'). | @inproceedings{fortuna-etal-2019-hierarchically,
title = "A Hierarchically-Labeled {P}ortuguese Hate Speech Dataset",
author = "Fortuna, Paula and
Rocha da Silva, Jo{\\~a}o and
Soler-Company, Juan and
Wanner, Leo and
Nunes, S{\'e}rgio",
booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-3510",
doi = "10.18653/v1/W19-3510",
pages = "94--104",
abstract = "Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels ({`}hate{'} vs. {`}no-hate{'}). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.",
} | null | 2 | 8 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HateSpeechPortuguese
tags:
- hate-speech-detection
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': no-hate
'1': hate
- name: hatespeech_G1
dtype: string
- name: annotator_G1
dtype: string
- name: hatespeech_G2
dtype: string
- name: annotator_G2
dtype: string
- name: hatespeech_G3
dtype: string
- name: annotator_G3
dtype: string
splits:
- name: train
num_bytes: 826130
num_examples: 5670
download_size: 763846
dataset_size: 826130
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset
- **Repository:** https://github.com/paulafortuna/Portuguese-Hate-Speech-Dataset
- **Paper:** https://www.aclweb.org/anthology/W19-3510/
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese dataset for hate speech detection composed of 5,668 tweets with binary annotations (i.e. 'hate' vs. 'no-hate').
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. |
id_liputan6 | 2022-11-18T20:08:31.000Z | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:id",
"license:unknown",
"extractive-summarization",
"arxiv:2... | null | In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models. | @inproceedings{id_liputan6,
author = {Fajri Koto, Jey Han Lau, Timothy Baldwin},
title = {Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
year = {2020},
url = {https://arxiv.org/abs/2011.00679},
} | null | 5 | 8 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- id
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Large-scale Indonesian Summarization
tags:
- extractive-summarization
dataset_info:
- config_name: canonical
features:
- name: id
dtype: string
- name: url
dtype: string
- name: clean_article
dtype: string
- name: clean_summary
dtype: string
- name: extractive_summary
dtype: string
splits:
- name: validation
num_bytes: 20944658
num_examples: 10972
- name: test
num_bytes: 20526768
num_examples: 10972
- name: train
num_bytes: 382245586
num_examples: 193883
download_size: 0
dataset_size: 423717012
- config_name: xtreme
features:
- name: id
dtype: string
- name: url
dtype: string
- name: clean_article
dtype: string
- name: clean_summary
dtype: string
- name: extractive_summary
dtype: string
splits:
- name: validation
num_bytes: 9652946
num_examples: 4948
- name: test
num_bytes: 7574550
num_examples: 3862
download_size: 0
dataset_size: 17227496
---
# Dataset Card for Large-scale Indonesian Summarization
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [IndoLEM (Indonesian Language Evaluation Montage)](https://indolem.github.io/)
- **Repository:** [Liputan6: Summarization Corpus for Indonesian](https://github.com/fajri91/sum_liputan6/)
- **Paper:** https://arxiv.org/abs/2011.00679
- **Leaderboard:**
- **Point of Contact:** [Fajri Koto](mailto:feryandi.n@gmail.com),
[Jey Han Lau](mailto:jeyhan.lau@gmail.com), [Timothy Baldwin](mailto:tbaldwin@unimelb.edu.au),
### Dataset Summary
In this paper, we introduce a large-scale Indonesian summarization dataset. We harvest articles from this http URL,
an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop
benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual
BERT-based models. We include a thorough error analysis by examining machine-generated summaries that have
low ROUGE scores, and expose both issues with ROUGE it-self, as well as with extractive and abstractive
summarization models.
The dataset has two variants: "canonical" and "xtreme". The "xtreme" variant discards development and test
document–summary pairs where the summary has fewer than 90% novel 4-grams (the training data remains the same
as the canonical variant).
You need to manually request the liputan6 dataset using the form in https://github.com/fajri91/sum_liputan6/
and uncompress it. The liputan6 dataset can then be loaded using the following command
`datasets.load_dataset("id_liputan6", 'canonical', data_dir="<path/to/uncompressed_folder>")` or
`datasets.load_dataset("id_liputan6", 'xtreme', data_dir="<path/to/uncompressed_folder>")`.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
```
{
'id': 'string',
'url': 'string',
'clean_article': 'string',
'clean_article': 'string',
'extractive_summary': 'string'
}
```
### Data Instances
An example of the dataset:
```
{
'clean_article': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Syaratnya, penanganan penyelesaian konflik Maluku harus dimulai dari awal kerusuhan, yakni 19 Januari 1999. Demikian hasil Musyawarah Wilayah I PBB Maluku yang dimulai Sabtu pekan silam dan berakhir Senin (31/12) di Ambon. Menurut seorang fungsionaris PBB Ridwan Hasan, persoalan di Maluku bisa selesai asalkan pemerintah dan aparat keamanan serius menangani setiap persoalan di Maluku secara komprehensif dan bijaksana. Itulah sebabnya, PBB wilayah Maluku akan menjadikan penyelesaian konflik sebagai agenda utama partai. PBB Maluku juga akan mendukung penegakan hukum secara terpadu dan tanpa pandang bulu. Siapa saja yang melanggar hukum harus ditindak. Ridwan berharap, Ketua PBB Maluku yang baru, Ali Fauzi, dapat menindak lanjuti agenda politik partai yang telah diamanatkan dan mau mendukung penegakan hukum di Maluku. (ULF/Sahlan Heluth).',
'clean_summary': 'Konflik Ambon telah berlangsung selama tiga tahun. Partai Bulan Bintang wilayah Maluku siap membantu pemerintah menyelesaikan kasus di provinsi tersebut.',
'extractive_summary': 'Liputan6.com, Ambon: Partai Bulan Bintang wilayah Maluku bertekad membantu pemerintah menyelesaikan konflik di provinsi tersebut. Siapa saja yang melanggar hukum harus ditindak.',
'id': '26408',
'url': 'https://www.liputan6.com/news/read/26408/pbb-siap-membantu-penyelesaian-konflik-ambon'
}
```
### Data Fields
- `id`: id of the sample
- `url`: the url to the original article
- `clean_article`: the original article
- `clean_article`: the abstractive summarization
- `extractive_summary`: the extractive summarization
### Data Splits
The dataset is splitted in to train, validation and test sets.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{Koto2020Liputan6AL,
title={Liputan6: A Large-scale Indonesian Dataset for Text Summarization},
author={Fajri Koto and Jey Han Lau and Timothy Baldwin},
booktitle={AACL/IJCNLP},
year={2020}
}
```
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset. |
kor_sarcasm | 2023-03-21T14:49:40.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:mit",
"sarcasm-detection",
"region:us"
] | null | This is a dataset designed to detect sarcasm in Korean because it distorts the literal meaning of a sentence
and is highly related to sentiment classification. | null | null | 2 | 8 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ko
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Korean Sarcasm Detection
tags:
- sarcasm-detection
dataset_info:
features:
- name: tokens
dtype: string
- name: label
dtype:
class_label:
names:
'0': no_sarcasm
'1': sarcasm
splits:
- name: train
num_bytes: 1012030
num_examples: 9000
- name: test
num_bytes: 32480
num_examples: 301
download_size: 1008955
dataset_size: 1044510
---
# Dataset Card for Korean Sarcasm Detection
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm)
- **Repository:** [Korean Sarcasm Detection](https://github.com/SpellOnYou/korean-sarcasm)
- **Point of Contact:** [Dionne Kim](jiwon.kim.096@gmail.com)
### Dataset Summary
The Korean Sarcasm Dataset was created to detect sarcasm in text, which can significantly alter the original meaning of a sentence. 9319 tweets were collected from Twitter and labeled for `sarcasm` or `not_sarcasm`. These tweets were gathered by querying for: `역설, 아무말, 운수좋은날, 笑, 뭐래 아닙니다, 그럴리없다, 어그로, irony sarcastic, and sarcasm`. The dataset was pre-processed by removing the keyword hashtag, urls and mentions of the user to maintain anonymity.
### Supported Tasks and Leaderboards
* `sarcasm_detection`: The dataset can be used to train a model to detect sarcastic tweets. A [BERT](https://huggingface.co/bert-base-uncased) model can be presented with a tweet in Korean and be asked to determine whether it is sarcastic or not.
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a Korean tweet and a label whether it is sarcastic or not. `1` maps to sarcasm and `0` maps to no sarcasm.
```
{
"tokens": "[ 수도권 노선 아이템 ] 17 . 신분당선의 #딸기 : 그의 이미지 컬러 혹은 머리 색에서 유래한 아이템이다 . #메트로라이프"
"label": 0
}
```
### Data Fields
* `tokens`: contains the text of the tweet
* `label`: determines whether the text is sarcastic (`1`: sarcasm, `0`: no sarcasm)
### Data Splits
The data is split into a training set comrpised of 9018 tweets and a test set of 301 tweets.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by gathering HTML data from Twitter. Queries for hashtags that include sarcasm and variants of it were used to return tweets. It was preprocessed by removing the keyword hashtag, urls and mentions of the user to preserve anonymity.
#### Who are the source language producers?
The source language producers are Korean Twitter users.
### Annotations
#### Annotation process
Tweets were labeled `1` for sarcasm and `0` for no sarcasm.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Mentions of the user in a tweet were removed to keep them anonymous.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was curated by Dionne Kim.
### Licensing Information
This dataset is licensed under the MIT License.
### Citation Information
```
@misc{kim2019kocasm,
author = {Kim, Jiwon and Cho, Won Ik},
title = {Kocasm: Korean Automatic Sarcasm Detection},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SpellOnYou/korean-sarcasm}}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. |
m_lama | 2022-11-03T16:15:15.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_ids:open-domain-qa",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creator... | null | mLAMA: a multilingual version of the LAMA benchmark (T-REx and GoogleRE) covering 53 languages. | @article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year = {2021},
url = {https://arxiv.org/abs/2102.00894},
archivePrefix = {arXiv},
eprint = {2102.00894},
timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = {to appear in EACL2021}
} | null | 4 | 8 | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- ga
- gl
- he
- hi
- hr
- hu
- hy
- id
- it
- ja
- ka
- ko
- la
- lt
- lv
- ms
- nl
- pl
- pt
- ro
- ru
- sk
- sl
- sq
- sr
- sv
- ta
- th
- tr
- uk
- ur
- vi
- zh
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- extended|lama
task_categories:
- question-answering
- text-classification
task_ids:
- open-domain-qa
- text-scoring
paperswithcode_id: null
pretty_name: MLama
tags:
- probing
dataset_info:
features:
- name: uuid
dtype: string
- name: lineid
dtype: uint32
- name: obj_uri
dtype: string
- name: obj_label
dtype: string
- name: sub_uri
dtype: string
- name: sub_label
dtype: string
- name: template
dtype: string
- name: language
dtype: string
- name: predicate_id
dtype: string
config_name: all
splits:
- name: test
num_bytes: 125919995
num_examples: 843143
download_size: 40772287
dataset_size: 125919995
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Multilingual LAMA](http://cistern.cis.lmu.de/mlama/)
- **Repository:** [Github](https://github.com/norakassner/mlama)
- **Paper:** [Arxiv](https://arxiv.org/abs/2102.00894)
- **Point of Contact:** [Contact section](http://cistern.cis.lmu.de/mlama/)
### Dataset Summary
This dataset provides the data for mLAMA, a multilingual version of LAMA.
Regarding LAMA see https://github.com/facebookresearch/LAMA. For mLAMA
the TREx and GoogleRE part of LAMA was considered and machine translated using
Google Translate, and the Wikidata and Google Knowledge Graph API. The machine
translated templates were checked for validity, i.e., whether they contain
exactly one '[X]' and one '[Y]'.
This data can be used for creating fill-in-the-blank queries like
"Paris is the capital of [MASK]" across 53 languages. For more details see
the website http://cistern.cis.lmu.de/mlama/ or the github repo https://github.com/norakassner/mlama.
### Supported Tasks and Leaderboards
Language model knowledge probing.
### Languages
This dataset contains data in 53 languages:
af,ar,az,be,bg,bn,ca,ceb,cs,cy,da,de,el,en,es,et,eu,fa,fi,fr,ga,gl,he,hi,hr,hu,hy,id,it,ja,ka,ko,la,lt,lv,ms,nl,pl,pt,ro,ru,sk,sl,sq,sr,sv,ta,th,tr,uk,ur,vi,zh
## Dataset Structure
For each of the 53 languages and each of the 43 relations/predicates there is a set of triples.
### Data Instances
For each language and relation there are triples, that consists of an object, a predicate and a subject. For each predicate there is a template available. An example for `dataset["test"][0]` is given here:
```python
{
'language': 'af',
'lineid': 0,
'obj_label': 'Frankryk',
'obj_uri': 'Q142',
'predicate_id': 'P1001',
'sub_label': 'President van Frankryk',
'sub_uri': 'Q191954',
'template': "[X] is 'n wettige term in [Y].",
'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'
}
```
### Data Fields
Each instance has the following fields
* "uuid": a unique identifier
* "lineid": a identifier unique to mlama
* "obj_id": knowledge graph id of the object
* "obj_label": surface form of the object
* "sub_id": knowledge graph id of the subject
* "sub_label": surface form of the subject
* "template": template
* "language": language code
* "predicate_id": relation id
### Data Splits
There is only one partition that is labelled as 'test data'.
## Dataset Creation
### Curation Rationale
The dataset was translated into 53 languages to investigate knowledge in pretrained language models
multilingually.
### Source Data
#### Initial Data Collection and Normalization
The data has several sources:
LAMA (https://github.com/facebookresearch/LAMA) licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
T-REx (https://hadyelsahar.github.io/t-rex/) licensed under Creative Commons Attribution-ShareAlike 4.0 International License
Google-RE (https://github.com/google-research-datasets/relation-extraction-corpus)
Wikidata (https://www.wikidata.org/) licensed under Creative Commons CC0 License and Creative Commons Attribution-ShareAlike License
#### Who are the source language producers?
See links above.
### Annotations
#### Annotation process
Crowdsourced (wikidata) and machine translated.
#### Who are the annotators?
Unknown.
### Personal and Sensitive Information
Names of (most likely) famous people who have entries in Google Knowledge Graph or Wikidata.
## Considerations for Using the Data
Data was created through machine translation and automatic processes.
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Not all triples are available in all languages.
## Additional Information
### Dataset Curators
The authors of the mLAMA paper and the authors of the original datasets.
### Licensing Information
The Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). https://creativecommons.org/licenses/by-nc-sa/4.0/
### Citation Information
```
@article{kassner2021multilingual,
author = {Nora Kassner and
Philipp Dufter and
Hinrich Sch{\"{u}}tze},
title = {Multilingual {LAMA:} Investigating Knowledge in Multilingual Pretrained
Language Models},
journal = {CoRR},
volume = {abs/2102.00894},
year = {2021},
url = {https://arxiv.org/abs/2102.00894},
archivePrefix = {arXiv},
eprint = {2102.00894},
timestamp = {Tue, 09 Feb 2021 13:35:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2102-00894.bib},
bibsource = {dblp computer science bibliography, https://dblp.org},
note = {to appear in EACL2021}
}
```
### Contributions
Thanks to [@pdufter](https://github.com/pdufter) for adding this dataset. |
matinf | 2023-04-05T10:09:38.000Z | [
"region:us"
] | null | MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification,
question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to
inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the
merits held by MATINF. | @inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and
Pei, Jiaxin and
Wu, Hongtao and
Liu, Yiyu and
Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
} | null | 3 | 8 | ---
paperswithcode_id: matinf
pretty_name: Maternal and Infant Dataset
dataset_info:
- config_name: age_classification
features:
- name: question
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
names:
'0': 0-1岁
'1': 1-2岁
'2': 2-3岁
- name: id
dtype: int32
splits:
- name: train
num_bytes: 33901977
num_examples: 134852
- name: test
num_bytes: 9616194
num_examples: 38318
- name: validation
num_bytes: 4869685
num_examples: 19323
download_size: 0
dataset_size: 48387856
- config_name: topic_classification
features:
- name: question
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
names:
'0': 产褥期保健
'1': 儿童过敏
'2': 动作发育
'3': 婴幼保健
'4': 婴幼心理
'5': 婴幼早教
'6': 婴幼期喂养
'7': 婴幼营养
'8': 孕期保健
'9': 家庭教育
'10': 幼儿园
'11': 未准父母
'12': 流产和不孕
'13': 疫苗接种
'14': 皮肤护理
'15': 宝宝上火
'16': 腹泻
'17': 婴幼常见病
- name: id
dtype: int32
splits:
- name: train
num_bytes: 153326538
num_examples: 613036
- name: test
num_bytes: 43877443
num_examples: 175363
- name: validation
num_bytes: 21834951
num_examples: 87519
download_size: 0
dataset_size: 219038932
- config_name: summarization
features:
- name: description
dtype: string
- name: question
dtype: string
- name: id
dtype: int32
splits:
- name: train
num_bytes: 181245403
num_examples: 747888
- name: test
num_bytes: 51784189
num_examples: 213681
- name: validation
num_bytes: 25849900
num_examples: 106842
download_size: 0
dataset_size: 258879492
- config_name: qa
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: id
dtype: int32
splits:
- name: train
num_bytes: 188047511
num_examples: 747888
- name: test
num_bytes: 53708532
num_examples: 213681
- name: validation
num_bytes: 26931809
num_examples: 106842
download_size: 0
dataset_size: 268687852
---
# Dataset Card for "matinf"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/WHUIR/MATINF](https://github.com/WHUIR/MATINF)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 795.00 MB
- **Total amount of disk used:** 795.00 MB
### Dataset Summary
MATINF is the first jointly labeled large-scale dataset for classification, question answering and summarization.
MATINF contains 1.07 million question-answer pairs with human-labeled categories and user-generated question
descriptions. Based on such rich information, MATINF is applicable for three major NLP tasks, including classification,
question answering, and summarization. We benchmark existing methods and a novel multi-task baseline over MATINF to
inspire further research. Our comprehensive comparison and experiments over MATINF and other datasets demonstrate the
merits held by MATINF.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### age_classification
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 48.39 MB
- **Total amount of disk used:** 48.39 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"description": "\"6个月的时候去儿宝检查，医生说宝宝的分胯动作做的不好，说最好去儿童医院看看，但我家宝宝很好，感觉没有什么不正常啊，请教一下，分胯做的不好，有什么不好吗？\"...",
"id": 88016,
"label": 0,
"question": "医生说宝宝的分胯动作不好"
}
```
#### qa
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 268.69 MB
- **Total amount of disk used:** 268.69 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answer": "\"我一个同学的孩子就是发现了肾积水，治疗了一段时间，结果还是越来越多，没办法就打掉了。虽然舍不得，但是还是要忍痛割爱，不然以后孩子真的有问题，大人和孩子都受罪。不过，这个最后的决定还要你自己做，毕竟是你的宝宝。，、、、、\"...",
"id": 536714,
"question": "孕5个月检查右侧肾积水孩子能要吗？"
}
```
#### summarization
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 258.88 MB
- **Total amount of disk used:** 258.88 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"description": "\"宝宝有中度HIE，但原因未查明，这是他出生后脸上红的几道，嘴唇深红近紫，请问这是像缺氧的表现吗？\"...",
"id": 173649,
"question": "宝宝脸上红的几道嘴唇深红近紫是像缺氧的表现吗？"
}
```
#### topic_classification
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 219.04 MB
- **Total amount of disk used:** 219.04 MB
An example of 'train' looks as follows.
```
{
"description": "媳妇怀孕五个月了经检查右侧肾积水、过了半月左侧也出现肾积水、她要拿掉孩子、怎么办？",
"id": 536714,
"label": 8,
"question": "孕5个月检查右侧肾积水孩子能要吗？"
}
```
### Data Fields
The data fields are the same among all splits.
#### age_classification
- `question`: a `string` feature.
- `description`: a `string` feature.
- `label`: a classification label, with possible values including `0-1岁` (0), `1-2岁` (1), `2-3岁` (2).
- `id`: a `int32` feature.
#### qa
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `id`: a `int32` feature.
#### summarization
- `description`: a `string` feature.
- `question`: a `string` feature.
- `id`: a `int32` feature.
#### topic_classification
- `question`: a `string` feature.
- `description`: a `string` feature.
- `label`: a classification label, with possible values including `产褥期保健` (0), `儿童过敏` (1), `动作发育` (2), `婴幼保健` (3), `婴幼心理` (4).
- `id`: a `int32` feature.
### Data Splits
| name |train |validation| test |
|--------------------|-----:|---------:|-----:|
|age_classification |134852| 19323| 38318|
|qa |747888| 106842|213681|
|summarization |747888| 106842|213681|
|topic_classification|613036| 87519|175363|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{xu-etal-2020-matinf,
title = "{MATINF}: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization",
author = "Xu, Canwen and
Pei, Jiaxin and
Wu, Hongtao and
Liu, Yiyu and
Li, Chenliang",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.330",
pages = "3586--3596",
}
```
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. |
multi_nli_mismatch | 2023-04-05T10:10:18.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:ori... | null | The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen. | @InProceedings{N18-1101,
author = {Williams, Adina
and Nangia, Nikita
and Bowman, Samuel},
title = {A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference},
booktitle = {Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)},
year = {2018},
publisher = {Association for Computational Linguistics},
pages = {1112--1122},
location = {New Orleans, Louisiana},
url = {http://aclweb.org/anthology/N18-1101}
} | null | 1 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-3.0
- cc-by-sa-3.0
- mit
- other
license_details: Open Portion of the American National Corpus
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: multinli
pretty_name: Multi-Genre Natural Language Inference
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 75601459
num_examples: 392702
- name: validation
num_bytes: 2009444
num_examples: 10000
download_size: 226850426
dataset_size: 77610903
---
# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
### Dataset Summary
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
An example of 'train' looks as follows.
```
{
"hypothesis": "independence",
"label": "contradiction",
"premise": "correlation"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train |validation|
|----------|-----:|---------:|
|plain_text|392702| 10000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
multilingual_librispeech | 2022-11-18T21:31:47.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"so... | null | Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | null | 7 | 8 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- es
- fr
- it
- nl
- pl
- pt
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- speaker-identification
dataset_info:
- config_name: polish
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 16136430
num_examples: 25043
- name: train.9h
num_bytes: 1383232
num_examples: 2173
- name: train.1h
num_bytes: 145411
num_examples: 238
- name: validation
num_bytes: 318964
num_examples: 512
- name: test
num_bytes: 332317
num_examples: 520
download_size: 6609569551
dataset_size: 18316354
- config_name: german
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 277089334
num_examples: 469942
- name: train.9h
num_bytes: 1325460
num_examples: 2194
- name: train.1h
num_bytes: 145998
num_examples: 241
- name: validation
num_bytes: 2160779
num_examples: 3469
- name: test
num_bytes: 2131177
num_examples: 3394
download_size: 122944886305
dataset_size: 282852748
- config_name: dutch
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 218648573
num_examples: 374287
- name: train.9h
num_bytes: 1281951
num_examples: 2153
- name: train.1h
num_bytes: 141672
num_examples: 234
- name: validation
num_bytes: 1984165
num_examples: 3095
- name: test
num_bytes: 1945428
num_examples: 3075
download_size: 92158429530
dataset_size: 224001789
- config_name: french
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 162009691
num_examples: 258213
- name: train.9h
num_bytes: 1347707
num_examples: 2167
- name: train.1h
num_bytes: 146699
num_examples: 241
- name: validation
num_bytes: 1482961
num_examples: 2416
- name: test
num_bytes: 1539152
num_examples: 2426
download_size: 64474642518
dataset_size: 166526210
- config_name: spanish
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 136743162
num_examples: 220701
- name: train.9h
num_bytes: 1288180
num_examples: 2110
- name: train.1h
num_bytes: 138734
num_examples: 233
- name: validation
num_bytes: 1463115
num_examples: 2408
- name: test
num_bytes: 1464565
num_examples: 2385
download_size: 53296894035
dataset_size: 141097756
- config_name: italian
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 36008104
num_examples: 59623
- name: train.9h
num_bytes: 1325927
num_examples: 2173
- name: train.1h
num_bytes: 145006
num_examples: 240
- name: validation
num_bytes: 732210
num_examples: 1248
- name: test
num_bytes: 746977
num_examples: 1262
download_size: 15395281399
dataset_size: 38958224
- config_name: portuguese
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: train
num_bytes: 23036487
num_examples: 37533
- name: train.9h
num_bytes: 1305698
num_examples: 2116
- name: train.1h
num_bytes: 143781
num_examples: 236
- name: validation
num_bytes: 512463
num_examples: 826
- name: test
num_bytes: 549893
num_examples: 871
download_size: 9982803818
dataset_size: 25548322
---
# Dataset Card for MultiLingual LibriSpeech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.</p>
</div>
Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
myanmar_news | 2023-01-25T14:41:11.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:my",
"license:gpl-3.0",
"region:us"
] | null | The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine | null | null | 0 | 8 | ---
annotations_creators:
- found
language_creators:
- found
language:
- my
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: MyanmarNews
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype:
class_label:
names:
'0': Sport
'1': Politic
'2': Business
'3': Entertainment
splits:
- name: train
num_bytes: 3797368
num_examples: 8116
download_size: 610592
dataset_size: 3797368
---
# Dataset Card for Myanmar_News
## Dataset Description
- **Repository:** https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Dataset Summary
The Myanmar news dataset contains article snippets in four categories:
Business, Entertainment, Politics, and Sport.
These were collected in October 2017 by Aye Hninn Khine
### Languages
Myanmar/Burmese language
## Dataset Structure
### Data Fields
- text - text from article
- category - a topic: Business, Entertainment, **Politic**, or **Sport** (note spellings)
### Data Splits
One training set (8,116 total rows)
### Source Data
#### Initial Data Collection and Normalization
Data was collected by Aye Hninn Khine
and shared on GitHub with a GPL-3.0 license.
Multiple text files were consolidated into one labeled CSV file by Nick Doiron.
## Additional Information
### Dataset Curators
Contributors to original GitHub repo:
- https://github.com/ayehninnkhine
### Licensing Information
GPL-3.0
### Citation Information
See https://github.com/ayehninnkhine/MyanmarNewsClassificationSystem
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. |
refresd | 2023-01-25T14:43:11.000Z | [
"task_categories:text-classification",
"task_categories:translation",
"task_ids:semantic-similarity-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"lang... | null | The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039
English-French sentence-pairs annotated with sentence-level divergence judgments and token-level
rationales. For any questions, write to ebriakou@cs.umd.edu. | @inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
pages = "1563--1580",
} | null | 0 | 8 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- machine-generated
language:
- en
- fr
license:
- mit
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-wikimatrix
task_categories:
- text-classification
- translation
task_ids:
- semantic-similarity-classification
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: refresd
pretty_name: Rationalized English-French Semantic Divergences
dataset_info:
features:
- name: sentence_en
dtype: string
- name: sentence_fr
dtype: string
- name: label
dtype:
class_label:
names:
'0': divergent
'1': equivalent
- name: all_labels
dtype:
class_label:
names:
'0': unrelated
'1': some_meaning_difference
'2': no_meaning_difference
- name: rationale_en
dtype: string
- name: rationale_fr
dtype: string
splits:
- name: train
num_bytes: 501562
num_examples: 1039
download_size: 503977
dataset_size: 501562
---
# Dataset Card for REFreSD Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/Elbria/xling-SemDiv/tree/master/REFreSD)
- **Repository:** [Github](https://github.com/Elbria/xling-SemDiv/)
- **Paper:** [Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank](https://www.aclweb.org/anthology/2020.emnlp-main.121)
- **Leaderboard:**
- **Point of Contact:** [Eleftheria Briakou](mailto:ebriakou@cs.umd.edu)
- **Additional Documentation:** [Annotation workflow, data statement, DataSheet, and IRB documentation](https://elbria.github.io/post/refresd/)
### Dataset Summary
The Rationalized English-French Semantic Divergences (REFreSD) dataset consists of 1,039 English-French sentence-pairs annotated with sentence-level divergence judgments and token-level rationales. The project under which REFreSD was collected aims to advance our fundamental understanding of computational representations and methods for comparing and contrasting text meaning across languages.
### Supported Tasks and Leaderboards
`semantic-similarity-classification` and `semantic-similarity-scoring`: This dataset can by used to assess the ability of computational methods to detect meaning mismatches between languages. The model performance is measured in terms of accuracy by comparing the model predictions with the human judgments in REFreSD. Details about the results of a BERT-based model, Divergent mBERT, over this dataset can be found in the [paper](https://www.aclweb.org/anthology/2020.emnlp-main.121).
### Languages
The text is in English and French as found on Wikipedia. The associated BCP-47 codes are `en` and `fr`.
## Dataset Structure
### Data Instances
Each data point looks like this:
```python
{
'sentence_pair': {'en': 'The invention of farming some 10,000 years ago led to the development of agrarian societies , whether nomadic or peasant , the latter in particular almost always dominated by a strong sense of traditionalism .',
'fr': "En quelques décennies , l' activité économique de la vallée est passée d' une mono-activité agricole essentiellement vivrière , à une quasi mono-activité touristique , si l' on excepte un artisanat du bâtiment traditionnel important , en partie saisonnier ."}
'label': 0,
'all_labels': 0,
'rationale_en': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'rationale_fr': [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3],
}
```
### Data Fields
- `sentence_pair`: Dictionary of sentences containing the following field.
- `en`: The English sentence.
- `fr`: The corresponding (or not) French sentence.
- `label`: Binary. Whether both sentences correspond. `{0:divergent, 1:equivalent}`
- `all_labels`: 3-class label `{0: "unrelated", 1: "some_meaning_difference", 2:"no_meaning_difference"}`. The first two are sub-classes of the `divergent` label.
- `rationale_en`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the English sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from English.
- `rationale_fr`: A list of integers from 0-3 indicating the number of annotators who highlighted the token of the text in the French sentence during annotation. Word-aligned rationale for the divergent/equivalent label, from French.
### Data Splits
The dataset contains 1039 sentence pairs in a single `"train"` split. Of these pairs, 64% are annotated as divergent, and 40% contain fine-grained meaning divergences.
| Label | Number of Instances |
| ----------------------- | ------------------- |
| Unrelated | 252 |
| Some meaning difference | 418 |
| No meaning different | 369 |
## Dataset Creation
### Curation Rationale
The curators chose the English-French section of the WikiMatrix corpus because (1) it is likely to contain diverse, interesting divergence types since it consists of mined parallel sentences of diverse topics which are not necessarily generated by (human) translations, and (2) Wikipedia and WikiMatrix are widely used resources to train semantic representations and perform cross-lingual transfer in NLP.
### Source Data
#### Initial Data Collection and Normalization
The source for this corpus is the English and French portion of the [WikiMatrix corpus](https://arxiv.org/abs/1907.05791), which itself was extracted from Wikipedia articles. The curators excluded noisy samples by filtering out sentence pairs that a) were too short or too long, b) consisted mostly of numbers, or c) had a small token-level edit difference.
#### Who are the source language producers?
Some content of Wikipedia articles has been (human) translated from existing articles in another language while others have been written or edited independently in each language. Therefore, information on how the original text is created is not available.
### Annotations
#### Annotation process
The annotations were collected over the span of three weeks in April 2020. Annotators were presented with an English sentence and a French sentence. First, they highlighted spans and labeled them as 'added', 'changed', or 'other', where added spans contain information not contained in the other sentence, changed spans contain some information that is in the other sentence but whose meaning is not the same, and other spans have some different meaning not covered in the previous two cases, such as idioms. They then assessed the relation between the two sentences as either 'unrelated', 'some meaning differences', or 'no meaning difference'. See the [annotation guidelines](https://elbria.github.io/post/refresd/files/REFreSD_Annotation_Guidelines.pdf) for more information about the task and the annotation interface, and see the [DataSheet](https://elbria.github.io/post/refresd/files/REFreSD_Datasheet.pdf) for information about the annotator compensation.
The following table contains Inter-Annotator Agreement metrics for the dataset:
| Granularity | Method | IAA |
| ----------- | --------------- | ------------ |
| Sentence | Krippendorf's α | 0.60 |
| Span | macro F1 | 45.56 ± 7.60 |
| Token | macro F1 | 33.94 ± 8.24 |
#### Who are the annotators?
This dataset includes annotations from 6 participants recruited from the University of Maryland, College Park (UMD) educational institution. Participants ranged in age from 20–25 years, including one man and five women. For each participant, the curators ensured they were proficient in both languages of interest: three of them self-reported as English native speakers, one as a French native speaker, and two as bilingual English-French speakers.
### Personal and Sensitive Information
The dataset contains discussions of people as they appear in Wikipedia articles. It does not contain confidential information, nor does it contain identifying information about the source language producers or the annotators.
## Considerations for Using the Data
### Social Impact of Dataset
Models that are successful in the supported task require sophisticated semantic representations at the sentence level beyond the combined representations of the individual tokens in isolation. Such models could be used to curate parallel corpora for tasks like machine translation, cross-lingual transfer learning, or semantic modeling.
The statements in the dataset, however, are not necessarily representative of the world and may overrepresent one worldview if one language is primarily translated to, rather than an equal distribution of translations between the languages.
### Discussion of Biases
The English Wikipedia is known to have significantly more [contributors](https://en.wikipedia.org/wiki/Wikipedia:Who_writes_Wikipedia%3F) who identify as male than any other gender and who reside in either North America or Europe. This leads to an overrepresentation of male perspectives from these locations in the corpus in terms of both the topics covered and the language used to talk about those topics. It's not clear to what degree this holds true for the French Wikipedia. The REFreSD dataset itself has not yet been examined for the degree to which it contains the gender and other biases seen in the larger Wikipedia datasets.
### Other Known Limitations
It is unknown how many of the sentences in the dataset were written independently, and how many were written as [translations](https://en.wikipedia.org/wiki/Wikipedia:Translation) by either humans or machines from some other language to the languages of interest in this dataset.
## Additional Information
### Dataset Curators
The dataset curators are Eleftheria Briakou and Marine Carpuat, who are both affiliated with the University of Maryland, College Park's Department of Computer Science.
### Licensing Information
The project is licensed under the [MIT License](https://github.com/Elbria/xling-SemDiv/blob/master/LICENSE).
### Citation Information
```BibTeX
@inproceedings{briakou-carpuat-2020-detecting,
title = "Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank",
author = "Briakou, Eleftheria and Carpuat, Marine",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.121",
pages = "1563--1580",
}
```
### Contributions
Thanks to [@mpariente](https://github.com/mpariente) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
swahili | 2022-11-18T21:49:35.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"l... | null | The Swahili dataset developed specifically for language modeling task.
The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train,
valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and,
the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modeling. | @InProceedings{huggingface:dataset,
title = Language modeling data for Swahili (Version 1),
authors={Shivachi Casper Shikali, & Mokhosi Refuoe.
},
year={2019},
link = http://doi.org/10.5281/zenodo.3553423
} | null | 4 | 8 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- sw
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: swahili
dataset_info:
features:
- name: text
dtype: string
config_name: swahili
splits:
- name: train
num_bytes: 7700136
num_examples: 42069
- name: test
num_bytes: 695092
num_examples: 3371
- name: validation
num_bytes: 663520
num_examples: 3372
download_size: 2783330
dataset_size: 9058748
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Repository:**
- **Paper:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Swahili dataset developed specifically for language modeling task.
The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train,
valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and,
the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modeling.
### Supported Tasks and Leaderboards
Language Modeling
### Languages
Swahili (sw)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- text : A line of text in Swahili
### Data Splits
train = 80%, valid = 10%, test = 10%
## Dataset Creation
### Curation Rationale
Enhancing African low-resource languages
### Source Data
#### Initial Data Collection and Normalization
The dataset contains 28,000 unique words with 6.84 M, 970k, and 2 M words for the train, valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and, the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modelling.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Unannotated data
#### Who are the annotators?
NA
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
Enhancing African low-resource languages
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
"""\
@InProceedings{huggingface:dataset,
title = Language modeling data for Swahili (Version 1),
authors={Shivachi Casper Shikali, & Mokhosi Refuoe.
},
year={2019},
link = http://doi.org/10.5281/zenodo.3553423
}
"""
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. |
taskmaster1 | 2022-11-18T21:50:41.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1... | null | Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based dialogs comprising six domains. Two procedures were used to create this collection, each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach in which trained agents and crowdsourced workers interact to complete the task while the second is "self-dialog" in which crowdsourced workers write the entire dialog themselves. | @inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
} | null | 1 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: taskmaster-1
pretty_name: Taskmaster-1
dataset_info:
- config_name: one_person_dialogs
features:
- name: conversation_id
dtype: string
- name: instruction_id
dtype: string
- name: utterances
list:
- name: index
dtype: int32
- name: speaker
dtype: string
- name: text
dtype: string
- name: segments
list:
- name: start_index
dtype: int32
- name: end_index
dtype: int32
- name: text
dtype: string
- name: annotations
list:
- name: name
dtype: string
splits:
- name: train
num_bytes: 18037058
num_examples: 6168
- name: validation
num_bytes: 2239656
num_examples: 770
- name: test
num_bytes: 2224163
num_examples: 770
download_size: 103276427
dataset_size: 22500877
- config_name: woz_dialogs
features:
- name: conversation_id
dtype: string
- name: instruction_id
dtype: string
- name: utterances
list:
- name: index
dtype: int32
- name: speaker
dtype: string
- name: text
dtype: string
- name: segments
list:
- name: start_index
dtype: int32
- name: end_index
dtype: int32
- name: text
dtype: string
- name: annotations
list:
- name: name
dtype: string
splits:
- name: train
num_bytes: 13028593
num_examples: 5507
download_size: 103276427
dataset_size: 13028593
---
# Dataset Card for Taskmaster-1
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Taskmaster-1](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-1-2019)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster-1 is a goal-oriented conversational dataset. It includes 13,215 task-based
dialogs comprising six domains. Two procedures were used to create this collection,
each with unique advantages. The first involves a two-person, spoken "Wizard of Oz" (WOz) approach
in which trained agents and crowdsourced workers interact to complete the task while the second is
"self-dialog" in which crowdsourced workers write the entire dialog themselves.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id":"dlg-336c8165-068e-4b4b-803d-18ef0676f668",
"instruction_id":"restaurant-table-2",
"utterances":[
{
"index":0,
"segments":[
],
"speaker":"USER",
"text":"Hi, I'm looking for a place that sells spicy wet hotdogs, can you think of any?"
},
{
"index":1,
"segments":[
{
"annotations":[
{
"name":"restaurant_reservation.name.restaurant.reject"
}
],
"end_index":37,
"start_index":16,
"text":"Spicy Wet Hotdogs LLC"
}
],
"speaker":"ASSISTANT",
"text":"You might enjoy Spicy Wet Hotdogs LLC."
},
{
"index":2,
"segments":[
],
"speaker":"USER",
"text":"That sounds really good, can you make me a reservation?"
},
{
"index":3,
"segments":[
],
"speaker":"ASSISTANT",
"text":"Certainly, when would you like a reservation?"
},
{
"index":4,
"segments":[
{
"annotations":[
{
"name":"restaurant_reservation.num.guests"
},
{
"name":"restaurant_reservation.num.guests"
}
],
"end_index":20,
"start_index":18,
"text":"50"
}
],
"speaker":"USER",
"text":"I have a party of 50 who want a really sloppy dog on Saturday at noon."
}
]
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instruction_id`: A reference to the file(s) containing the user (and, if applicable, agent) instructions for this conversation.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
- one_person_dialogs
The data in `one_person_dialogs` config is split into `train`, `dev` and `test` splits.
| | train | validation | test |
|--------------|-------:|------------:|------:|
| N. Instances | 6168 | 770 | 770 |
- woz_dialogs
The data in `woz_dialogs` config has no default splits.
| | train |
|--------------|-------:|
| N. Instances | 5507 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
the_pile_stack_exchange | 2023-02-20T15:10:44.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
... | null | This dataset is part of EleutherAI/The Pile dataset and is a dataset for Language Models from processing stackexchange data dump, which is an anonymized dump of all user-contributed content on the Stack Exchange network. | @article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
} | null | 7 | 8 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Stack Exchange
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
features:
- name: domain
dtype: string
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 11075434609
num_examples: 5096117
download_size: 36802959360
dataset_size: 11075434609
---
# Dataset Card for Stack Exchange
## Table of Contents
- [Dataset Card for Stack Exchange](#dataset-card-for-the_pile_stack_exchange)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/EleutherAI/stackexchange-dataset)
- **Repository:** [Needs More Information]
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset is part of EleutherAI/The Pile dataset and is a dataset for Language Models from processing stackexchange data dump, which is an anonymized dump of all user-contributed content on the Stack Exchange network.
|download_size|34.28 Gib|
|dataset_size|10.3 Gib|
### Supported Tasks and Leaderboards
The dataset is used for Language Modeling.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'domain': 'chemistry',
'text':"\nQ: \n \nReviving old questions or asking a new one? \n \nI'm relatively new to the Chemistry SE community, and sometimes when I go to ask a question, I notice that the same (or similar) question has \nalready been asked. However, the previous question doesn't have a good answer (or is unanswered). In this case, is it better to ask the questi\non again in a new post (which might be marked as duplicate) or comment on the old post (which might be several years old)? In other words, wha\nt are the customs of this site in regards to reviving old questions/discussions?\n\nA:\n\nAs Martin commented, it really depends on the type of question. In any case, you always have the following possibilities:\n\nAsk a new question\nEdit the question to bump it to the first page\nAdd a bounty\nBring it to the attention of people in chat\n\nConsider the following cases:\n\nI have exactly the same question as asked and unanswered before!\n\nIf you ask a new question which turns out to be the same question, it may be closed as a dupe (depending on whether users remember the old que\nstion). Not the ideal option.\nIf you can find something substantial to edit and bump the question, do so. Maybe add a comment that you would really love an answer.\nIf you can spare some rep for a bounty (50 is usually enough), do so.\nYou can always bring it to the attention of people in chat.\n",}
```
### Data Fields
- `domain`: Stack Exchange domain of the sample
- `text`: Text content containing both the question and the answer
### Data Splits
|split|num examples|
--------------------------------
|train|5096117|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [sdtblck](https://github.com/sdtblck) for creating the dataset.
Thanks to [richarddwang](https://github.com/richarddwang) for adding the dataset. |
woz_dialogue | 2023-06-01T14:59:51.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:dialogue-modeling",
"task_ids:multi-class-classification",
"task_ids:parsing",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced... | null | Wizard-of-Oz (WOZ) is a dataset for training task-oriented dialogue systems. The dataset is designed around the task of finding a restaurant in the Cambridge, UK area. There are three informable slots (food, pricerange,area) that users can use to constrain the search and six requestable slots (address, phone, postcode plus the three informable slots) that the user can ask a value for once a restaurant has been offered. | @misc{wen2017networkbased,
title={A Network-based End-to-End Trainable Task-oriented Dialogue System},
author={Tsung-Hsien Wen and David Vandyke and Nikola Mrksic and Milica Gasic and Lina M. Rojas-Barahona and Pei-Hao Su and Stefan Ultes and Steve Young},
year={2017},
eprint={1604.04562},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 3 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- de
- en
- it
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- token-classification
- text-classification
task_ids:
- dialogue-modeling
- multi-class-classification
- parsing
paperswithcode_id: wizard-of-oz
pretty_name: Wizard-of-Oz
dataset_info:
- config_name: en
features:
- name: dialogue_idx
dtype: int32
- name: dialogue
list:
- name: turn_label
sequence:
sequence: string
- name: asr
sequence:
sequence: string
- name: system_transcript
dtype: string
- name: turn_idx
dtype: int32
- name: belief_state
list:
- name: slots
sequence:
sequence: string
- name: act
dtype: string
- name: transcript
dtype: string
- name: system_acts
sequence:
sequence: string
splits:
- name: train
num_bytes: 827189
num_examples: 600
- name: validation
num_bytes: 265684
num_examples: 200
- name: test
num_bytes: 537557
num_examples: 400
download_size: 7529221
dataset_size: 1630430
- config_name: de
features:
- name: dialogue_idx
dtype: int32
- name: dialogue
list:
- name: turn_label
sequence:
sequence: string
- name: asr
sequence:
sequence: string
- name: system_transcript
dtype: string
- name: turn_idx
dtype: int32
- name: belief_state
list:
- name: slots
sequence:
sequence: string
- name: act
dtype: string
- name: transcript
dtype: string
- name: system_acts
sequence:
sequence: string
splits:
- name: train
num_bytes: 881478
num_examples: 600
- name: validation
num_bytes: 276758
num_examples: 200
- name: test
num_bytes: 569703
num_examples: 400
download_size: 7626734
dataset_size: 1727939
- config_name: de_en
features:
- name: dialogue_idx
dtype: int32
- name: dialogue
list:
- name: turn_label
sequence:
sequence: string
- name: asr
sequence:
sequence: string
- name: system_transcript
dtype: string
- name: turn_idx
dtype: int32
- name: belief_state
list:
- name: slots
sequence:
sequence: string
- name: act
dtype: string
- name: transcript
dtype: string
- name: system_acts
sequence:
sequence: string
splits:
- name: train
num_bytes: 860151
num_examples: 600
- name: validation
num_bytes: 269966
num_examples: 200
- name: test
num_bytes: 555841
num_examples: 400
download_size: 7584753
dataset_size: 1685958
- config_name: it
features:
- name: dialogue_idx
dtype: int32
- name: dialogue
list:
- name: turn_label
sequence:
sequence: string
- name: asr
sequence:
sequence: string
- name: system_transcript
dtype: string
- name: turn_idx
dtype: int32
- name: belief_state
list:
- name: slots
sequence:
sequence: string
- name: act
dtype: string
- name: transcript
dtype: string
- name: system_acts
sequence:
sequence: string
splits:
- name: train
num_bytes: 842799
num_examples: 600
- name: validation
num_bytes: 270258
num_examples: 200
- name: test
num_bytes: 547759
num_examples: 400
download_size: 7559615
dataset_size: 1660816
- config_name: it_en
features:
- name: dialogue_idx
dtype: int32
- name: dialogue
list:
- name: turn_label
sequence:
sequence: string
- name: asr
sequence:
sequence: string
- name: system_transcript
dtype: string
- name: turn_idx
dtype: int32
- name: belief_state
list:
- name: slots
sequence:
sequence: string
- name: act
dtype: string
- name: transcript
dtype: string
- name: system_acts
sequence:
sequence: string
splits:
- name: train
num_bytes: 845095
num_examples: 600
- name: validation
num_bytes: 270942
num_examples: 200
- name: test
num_bytes: 548979
num_examples: 400
download_size: 7563815
dataset_size: 1665016
config_names:
- de
- de_en
- en
- it
- it_en
---
# Dataset Card for Wizard-of-Oz
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More info needed]
- **Repository:** [GitHub](https://github.com/nmrksic/neural-belief-tracker/tree/master/data/woz)
- **Paper:** [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://arxiv.org/abs/1604.04562)
- **Leaderboard:** [More info needed]
- **Point of Contact:** [More info needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
EMBO/sd-nlp | 2022-10-21T15:34:09.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license... | EMBO | This dataset is based on the SourceData database and is intented to facilitate training of NLP tasks in the cell and molecualr biology domain. | @Unpublished{
huggingface: dataset,
title = {SourceData NLP},
authors={Thomas Lemberger, EMBO},
year={2021}
} | null | 0 | 8 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
- structure-prediction
- text-classification
task_ids:
- multi-class-classification
- named-entity-recognition
- parsing
---
# Dataset Card for sd-nlp
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sourcedata.embo.org
- **Repository:** https://github.com/source-data/soda-roberta
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** thomas.lemberger@embo.org
### Dataset Summary
This dataset is based on the content of the SourceData (https://sourcedata.embo.org) database, which contains manually annotated figure legends written in English and extracted from scientific papers in the domain of cell and molecular biology (Liechti et al, Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471).
The dataset is pre-tokenized with the `roberta-base` tokenizer.
Additional details at https://github.com/source-data/soda-roberta
### Supported Tasks and Leaderboards
Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)).
`PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.
`NER`: biological and chemical entities are labeled. Specifically the following entities are tagged:
- `SMALL_MOLECULE`: small molecules
- `GENEPROD`: gene products (genes and proteins)
- `SUBCELLULAR`: subcellular components
- `CELL`: cell types and cell lines.
- `TISSUE`: tissues and organs
- `ORGANISM`: species
- `EXP_ASSAY`: experimental assays
`ROLES`: the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:
- `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.
- `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements.
`BORING`: entities are marked with the tag `BORING` when they are more of descriptive value and not directly associated with causal hypotheses ('boring' is not an ideal choice of word, but it is short...). Typically, these entities are so-called 'reporter' geneproducts, entities used as common baseline across samples, or specify the context of the experiment (cellular system, species, etc...).
### Languages
The text in the dataset is English.
## Dataset Structure
### Data Instances
```json
{
"tokens": [
"<s>", "Figure", "\u01205", ".", "\u0120Figure", "\u01205", ".", "A", "\u0120ER", "p", "57", "fl", "ox", "/", "fl", "ox", "\u0120mice", "\u0120were", "\u0120crossed", "\u0120with", "\u0120Nest", "in", "\u0120Cre", "\u0120trans", "genic", "\u0120mice", "\u0120to", "\u0120generate", "\u0120nervous", "\u0120system", "\u0120specific", "\u0120ER", "p", "57", "\u0120deficient", "\u0120animals", ".", "\u0120The", "\u0120levels", "\u0120of", "\u0120ER", "p", "57", "\u0120protein", "\u0120in", "\u0120the", "\u0120spinal", "\u0120cord", "\u0120were", "\u0120monitored", "\u0120by", "\u0120Western", "\u0120blot", ".", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "=", "4", "),", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "=", "5", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120(", "n", "=", "4", ")", "\u0120mice", ".", "\u0120H", "SP", "90", "\u0120levels", "\u0120were", "\u0120determined", "\u0120as", "\u0120a", "\u0120loading", "\u0120control", ".", "\u0120Right", "\u0120panel", ":", "\u0120Quant", "ification", "\u0120of", "\u0120ER", "p", "57", "\u0120levels", "\u0120was", "\u0120performed", "\u0120relative", "\u0120to", "\u0120H", "sp", "90", "\u0120levels", ".", "\u0120B", "\u0120Body", "\u0120weight", "\u0120measurements", "\u0120were", "\u0120performed", "\u0120for", "\u0120indicated", "\u0120time", "\u0120points", "\u0120in", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "=", "50", "),", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "=", "32", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120(", "n", "=", "19", ")", "\u0120mice", ".", "\u0120C", "\u0120Rot", "ar", "od", "\u0120performance", "\u0120was", "\u0120performed", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "=", "20", "),", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "=", "15", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120(", "n", "=", "8", ")", "\u0120mice", ".", "\u0120D", "\u0120H", "anging", "\u0120test", "\u0120performance", "\u0120was", "\u0120assessed", "\u0120in", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "=", "41", "),", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "=", "32", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120(", "n", "=", "12", ")", "\u0120mice", ".", "\u0120E", "\u0120Kaplan", "-", "Me", "ier", "\u0120survival", "\u0120curve", "\u0120for", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120mice", "\u0120(", "N", "\u0120=", "\u012019", ")", "\u0120that", "\u0120prematurely", "\u0120died", "\u0120or", "\u0120had", "\u0120to", "\u0120be", "\u0120sacrificed", "\u0120because", "\u0120of", "\u0120health", "\u0120reasons", "\u0120between", "\u0120the", "\u0120ages", "\u012022", "\u0120and", "\u012073", "\u0120days", ".", "\u0120Mean", "\u0120survival", "\u0120of", "\u0120this", "\u0120sub", "group", "\u0120of", "\u0120animals", "\u0120was", "\u012057", "\u0120days", ".", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "=", "50", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "=", "32", ")", "\u0120mice", "\u0120are", "\u0120shown", "\u0120as", "\u0120a", "\u0120reference", ".", "\u0120F", "\u0120Hist", "ological", "\u0120analysis", "\u0120of", "\u0120Ne", "u", "N", "\u0120and", "\u0120GF", "AP", "\u0120st", "aining", "\u0120was", "\u0120performed", "\u0120in", "\u0120spinal", "\u0120cord", "\u0120tissue", "\u0120from", "\u0120ER", "p", "57", "WT", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120mice", "\u0120in", "\u0120three", "\u0120animals", "\u0120per", "\u0120group", "\u0120using", "\u0120indirect", "\u0120immun", "of", "lu", "orescence", ".", "\u0120The", "\u0120nucleus", "\u0120was", "\u0120stained", "\u0120with", "\u0120H", "oe", "ch", "st", ".", "\u0120Representative", "\u0120images", "\u0120from", "\u0120one", "\u0120mouse", "\u0120per", "\u0120group", "\u0120are", "\u0120shown", ".", "\u0120Scale", "\u0120bar", ":", "\u012050", "\u0120\u00ce\u00bc", "m", ".", "\u0120G", "\u0120St", "ere", "ological", "\u0120analysis", "\u0120of", "\u0120the", "\u0120spinal", "\u0120cord", "\u0120from", "\u0120ER", "p", "57", "WT", "\u0120(", "n", "\u0120=", "\u01204", "),", "\u0120ER", "p", "57", "N", "es", "+", "/-", "\u0120(", "n", "\u0120=", "\u01204", ")", "\u0120and", "\u0120ER", "p", "57", "N", "es", "-", "/-", "\u0120(", "n", "\u0120=", "\u01204", ")", "\u0120mice", ".", "\u0120Alternate", "\u0120series", "\u0120of", "\u0120sections", "\u0120from", "\u0120the", "\u0120spinal", "\u0120cord", "\u0120of", "\u0120the", "\u0120mice", "\u0120were", "\u0120either", "\u0120stained", "\u0120for", "\u0120N", "iss", "l", "\u0120(", "top", "\u0120row", "\u0120images", ")", "\u0120or", "\u0120processed", "\u0120for", "\u0120immun", "oh", "ist", "ochemistry", "\u0120for", "\u0120the", "\u0120ch", "olin", "ergic", "\u0120cell", "\u0120marker", "\u0120Ch", "oline", "\u0120Ac", "et", "yl", "\u0120Transfer", "ase", "\u0120(", "Ch", "AT", ",", "\u0120bottom", "\u0120row", "\u0120images", ").", "\u0120The", "\u0120nucle", "oli", "\u0120of", "\u0120the", "</s>"
],
"input_ids": [
0, 40683, 195, 4, 17965, 195, 4, 250, 13895, 642, 4390, 4825, 4325, 73, 4825, 4325, 15540, 58, 7344, 19, 12786, 179, 12022, 6214, 44131, 15540, 7, 5368, 7464, 467, 2167, 13895, 642, 4390, 38396, 3122, 4, 20, 1389, 9, 13895, 642, 4390, 8276, 11, 5, 21431, 13051, 58, 14316, 30, 2027, 39144, 4, 13895, 642, 4390, 25982, 36, 282, 5214, 306, 238, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5214, 245, 43, 8, 13895, 642, 4390, 487, 293, 12, 40398, 36, 282, 5214, 306, 43, 15540, 4, 289, 4186, 3248, 1389, 58, 3030, 25, 10, 16761, 797, 4, 5143, 2798, 35, 28256, 5000, 9, 13895, 642, 4390, 1389, 21, 3744, 5407, 7, 289, 4182, 3248, 1389, 4, 163, 13048, 2408, 19851, 58, 3744, 13, 4658, 86, 332, 11, 13895, 642, 4390, 25982, 36, 282, 5214, 1096, 238, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5214, 2881, 43, 8, 13895, 642, 4390, 487, 293, 12, 40398, 36, 282, 5214, 1646, 43, 15540, 4, 230, 9104, 271, 1630, 819, 21, 3744, 13895, 642, 4390, 25982, 36, 282, 5214, 844, 238, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5214, 996, 43, 8, 13895, 642, 4390, 487, 293, 12, 40398, 36, 282, 5214, 398, 43, 15540, 4, 211, 289, 23786, 1296, 819, 21, 11852, 11, 13895, 642, 4390, 25982, 36, 282, 5214, 4006, 238, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5214, 2881, 43, 8, 13895, 642, 4390, 487, 293, 12, 40398, 36, 282, 5214, 1092, 43, 15540, 4, 381, 25353, 12, 5096, 906, 7967, 9158, 13, 13895, 642, 4390, 487, 293, 12, 40398, 15540, 36, 487, 5457, 753, 43, 14, 30088, 962, 50, 56, 7, 28, 26936, 142, 9, 474, 2188, 227, 5, 4864, 820, 8, 6521, 360, 4, 30750, 7967, 9, 42, 2849, 13839, 9, 3122, 21, 4981, 360, 4, 13895, 642, 4390, 25982, 36, 282, 5214, 1096, 43, 8, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5214, 2881, 43, 15540, 32, 2343, 25, 10, 5135, 4, 274, 31862, 9779, 1966, 9, 3864, 257, 487, 8, 32727, 591, 1690, 8173, 21, 3744, 11, 21431, 13051, 11576, 31, 13895, 642, 4390, 25982, 8, 13895, 642, 4390, 487, 293, 12, 40398, 15540, 11, 130, 3122, 228, 333, 634, 18677, 13998, 1116, 6487, 45094, 4, 20, 38531, 21, 31789, 19, 289, 3540, 611, 620, 4, 10308, 3156, 31, 65, 18292, 228, 333, 32, 2343, 4, 33256, 2003, 35, 654, 46911, 119, 4, 272, 312, 2816, 9779, 1966, 9, 5, 21431, 13051, 31, 13895, 642, 4390, 25982, 36, 282, 5457, 204, 238, 13895, 642, 4390, 487, 293, 2744, 40398, 36, 282, 5457, 204, 43, 8, 13895, 642, 4390, 487, 293, 12, 40398, 36, 282, 5457, 204, 43, 15540, 4, 43510, 651, 9, 9042, 31, 5, 21431, 13051, 9, 5, 15540, 58, 1169, 31789, 13, 234, 3006, 462, 36, 8766, 3236, 3156, 43, 50, 12069, 13, 13998, 2678, 661, 39917, 13, 5, 1855, 21716, 44858, 3551, 17540, 732, 18675, 6208, 594, 4360, 18853, 3175, 36, 4771, 2571, 6, 2576, 3236, 3156, 322, 20, 38898, 6483, 9, 5, 2
],
"label_ids": {
"entity_types": [
"O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "B-GENEPROD", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "B-TISSUE", "I-TISSUE", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "B-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "B-TISSUE", "I-TISSUE", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "B-SUBCELLULAR", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "B-TISSUE", "I-TISSUE", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "O", "O", "O", "B-TISSUE", "I-TISSUE", "O", "O", "B-ORGANISM", "O", "O", "O", "O", "B-SUBCELLULAR", "I-SUBCELLULAR", "I-SUBCELLULAR", "O", "O", "O", "O", "O", "O", "O", "O", "B-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "I-EXP_ASSAY", "O", "O", "O", "O", "O", "O", "O", "B-GENEPROD", "I-GENEPROD", "I-GENEPROD", "I-GENEPROD", "I-GENEPROD", "I-GENEPROD", "I-GENEPROD", "O", "B-GENEPROD", "I-GENEPROD", "O", "O", "O", "O", "O", "O", "B-SUBCELLULAR", "I-SUBCELLULAR", "O", "O", "O"
],
"geneprod_roles": [
"O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "O", "B-MEASURED_VAR", "I-MEASURED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "I-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "I-MEASURED_VAR", "O", "B-MEASURED_VAR", "I-MEASURED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"
],
"boring": [
"O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "O", "O", "O", "B-BORING", "I-BORING", "B-BORING", "O", "O", "B-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "I-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "O", "B-BORING", "I-BORING", "I-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "I-BORING", "I-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "I-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "I-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "O", "O", "O", "O", "O", "O", "O", "B-BORING", "I-BORING", "O", "O", "B-BORING", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"
],
"panel_start": [
"O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-PANEL_START", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"
]
}
}
```
### Data Fields
- `input_ids`: token id in `roberta-base` tokenizers' vocabulary provided as a`list` of `int`
- `label_ids`:
- `entity_types`: `list` of `strings` for the IOB2 tags for entity type; possible value in `["O", "I-SMALL_MOLECULE", "B-SMALL_MOLECULE", "I-GENEPROD", "B-GENEPROD", "I-SUBCELLULAR", "B-SUBCELLULAR", "I-CELL", "B-CELL", "I-TISSUE", "B-TISSUE", "I-ORGANISM", "B-ORGANISM", "I-EXP_ASSAY", "B-EXP_ASSAY"]`
- `geneprod_roles`: `list` of `strings` for the IOB2 tags for experimental roles; values in `["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]`
- `boring`: `list` of `strings` for IOB2 tags for entities unrelated to causal design; values in `["O", "I-BORING", "B-BORING"]`
- `panel_start`: `list` of `strings` for IOB2 tags `["O", "B-PANEL_START"]`
### Data Splits
- train:
- features: ['input_ids', 'labels', 'tag_mask'],
- num_rows: 48_771
- test:
- features: ['input_ids', 'labels', 'tag_mask'],
- num_rows: 13_801
- validation:
- features: ['input_ids', 'labels', 'tag_mask'],
- num_rows: 7_178
## Dataset Creation
### Curation Rationale
The dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling.
### Source Data
#### Initial Data Collection and Normalization
Figure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471). The curation tool at https://curation.sourcedata.io was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (https://api.sourcedata.io) on 21 Jan 2021.
#### Who are the source language producers?
The examples are extracted from the figure legends from scientific papers in cell and molecular biology.
### Annotations
#### Annotation process
The annotations were produced manually with expert curators from the SourceData project (https://sourcedata.embo.org)
#### Who are the annotators?
Curators of the SourceData project.
### Personal and Sensitive Information
None known.
## Considerations for Using the Data
### Social Impact of Dataset
Not applicable.
### Discussion of Biases
The examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (https://embopress.org)
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Thomas Lemberger, EMBO.
### Licensing Information
CC BY 4.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tlemberger](https://github.com/tlemberger>) for adding this dataset.
|
GEM/squad_v2 | 2022-10-24T15:30:29.000Z | [
"task_categories:other",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:1806.03822",
"region:us"
] | GEM | SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from answering. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | null | 0 | 8 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: squad_v2
tags:
- question-generation
---
# Dataset Card for GEM/squad_v2
## Dataset Description
- **Homepage:** https://rajpurkar.github.io/SQuAD-explorer/
- **Repository:** https://rajpurkar.github.io/SQuAD-explorer/
- **Paper:** https://arxiv.org/abs/1806.03822v1
- **Leaderboard:** https://rajpurkar.github.io/SQuAD-explorer/
- **Point of Contact:** Robin Jia
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/squad_v2).
### Dataset Summary
SQuAD2.0 is a dataset that tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard. In GEM, we are using this dataset for the question-generation task in which a model should generate squad-like questions from an input text.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/squad_v2')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/squad_v2).
#### website
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### paper
[Arxiv](https://arxiv.org/abs/1806.03822v1)
#### authors
Pranav Rajpurkar, Robin Jia and Percy Liang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Arxiv](https://arxiv.org/abs/1806.03822v1)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{Rajpurkar2018KnowWY,
title={Know What You Don’t Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Robin Jia and Percy Liang},
booktitle={ACL},
year={2018}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Robin Jia
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
robinjia@stanford.edu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
[Website](https://rajpurkar.github.io/SQuAD-explorer/)
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
SQuAD2.0 tests the ability of a system to not only answer reading comprehension questions, but also abstain when presented with a question that cannot be answered based on the provided paragraph. F1 score is used to evaluate models on the leaderboard.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The idea behind SQuAD2.0 dataset is to make the models understand when a question cannot be answered given a context. This will help in building models such that they know what they don't know, and therefore make the models understand language at a deeper level. The tasks that can be supported by the dataset are machine reading comprehension, extractive QA, and question generation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Question Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Given an input passage and an answer span, the goal is to generate a question that asks for the answer.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Stanford University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Pranav Rajpurkar, Robin Jia and Percy Liang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Facebook and NSF Graduate Research Fellowship under Grant No. DGE-114747
#### Who added the Dataset to GEM?
<!-- 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. -->
<!-- scope: microscope -->
(Abinaya Mahendiran)[https://github.com/AbinayaM02], Manager Data Science, NEXT Labs,
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data fields are the same among all splits.
#### squad_v2
- `id`: a `string` feature.
- `gem_id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
Here is an example of a validation data point. This example was too long and was cropped:
```
{
"gem_id": "gem-squad_v2-validation-1",
"id": "56ddde6b9a695914005b9629",
"answers": {
"answer_start": [94, 87, 94, 94],
"text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
},
"context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
"question": "When were the Normans in Normandy?",
"title": "Normans"
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The original SQuAD2.0 dataset has only training and dev (validation) splits. The train split is further divided into test split and added as part of the GEM datasets.
| name | train | validation | test |
| -------------- | --------: | -------------: | -------: |
| squad_v2 | 90403 | 11873 | 39916 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SQuAD2.0 will encourage the development of new reading comprehension models
that know what they don’t know, and therefore understand language at a deeper level. It can also help in building better models for answer-aware question generation .
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Reasoning capability
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
The train(80%) and validation(10%) split of SQuAD2.0 are made available to public whereas the test(10%) split is not available.
As part of GEM, the train split, 80% of the original data is split into two train split (90%) and test split (remaining 10%). The idea is to provide all three splits for the users to use.
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Extractive QA, Question Generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`, `METEOR`, `ROUGE`, `BLEU`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Extractive QA uses Exact Match and F1 Score
- Question generation users METEOR, ROUGE-L, BLEU-4
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Question generation users METEOR, ROUGE-L, BLEU-4
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
@article{Dong2019UnifiedLM,
title={Unified Language Model Pre-training for Natural Language Understanding and Generation},
author={Li Dong and Nan Yang and Wenhui Wang and Furu Wei and Xiaodong Liu and Yu Wang and Jianfeng Gao and M. Zhou and Hsiao-Wuen Hon},
journal={ArXiv},
year={2019},
volume={abs/1905.03197}
}
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is curated in three stages:
- Curating passages,
- Crowdsourcing question-answers on those passages,
- Obtaining additional answers
As part of SQuAD1.1, 10000 high-quality articles from English Wikipedia is extracted using Project Nayuki’s Wikipedia’s internal PageRanks, from which 536 articles are sampled uniformly at random. From each of these articles, individual paragraphs are extracted, stripping away images, figures, tables, and discarding paragraphs shorter than 500 characters.
SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
To build systems that not only answer questions when possible, but also determine when no
answer is supported by the paragraph and abstain from answering.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
Wikipedia
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset contains 536 articles covering a wide range of topics, from musical celebrities to abstract concepts.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
From the sampled articles from Wikipedia, individual paragraphs are extracted, stripping
away images, figures, tables, and discarding paragraphs shorter than 500 characters and partitioned into training(80%), development set(10%) and test set(10%).
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
To retrieve high-quality articles, Project Nayuki’s Wikipedia’s internal PageRanks was used to obtain the top 10000 articles of English Wikipedia, from which 536 articles are sampled uniformly at random.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
unknown
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Crowdworkers from the United States or Canada with a 97% HIT acceptance rate, a minimum of 1000 HITs, were employed to create questions.
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
0
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
0
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
yes
#### Which Annotation Service
<!-- info: Which annotation services were used? -->
<!-- scope: periscope -->
`other`, `Amazon Mechanical Turk`
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
For SQuAD 1.1 , crowdworkers were tasked with asking and answering up to 5 questions on the
content of that paragraph. The questions had to be entered in a text field, and the answers had to be
highlighted in the paragraph.
For SQuAD2.0, each task consisted of an entire article from SQuAD 1.1. For each paragraph in the article, workers were asked to pose up to five questions that were impossible to answer
based on the paragraph alone, while referencing entities in the paragraph and ensuring that a plausible answer is present.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by another rater
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Questions from workers who wrote 25 or fewer questions on an article is removed; this filter
helped remove noise from workers who had trouble understanding the task, and therefore quit before completing the whole article. This filter to both SQuAD2.0 and the existing answerable questions from SQuAD 1.1.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- 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? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- 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). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- 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. -->
<!-- scope: telescope -->
yes
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
### Known Technical Limitations
|
IlyaGusev/headline_cause | 2023-02-12T00:02:58.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ru",
"language:en",
"license:cc0-1.0",
"causal-reasoni... | IlyaGusev | null | @misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Casualties},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 1 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
- en
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: HeadlineCause
tags:
- causal-reasoning
---
# Dataset Card for HeadlineCause
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/IlyaGusev/HeadlineCause
- **Paper:** [HeadlineCause: A Dataset of News Headlines for Detecting Causalities](https://arxiv.org/abs/2108.12626)
- **Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
### Dataset Summary
A dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations.
### Usage
Loading Russian Simple task:
```python
from datasets import load_dataset
dataset = load_dataset("IlyaGusev/headline_cause", "ru_simple")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset consists of two parts, Russian and English.
## Dataset Structure
### Data Instances
There is an URL, a title, and a timestamp for each of the two headlines in every data instance. A label is presented in three fields. 'Result' field is a textual label, 'label' field is a numeric label, and the 'agreement' field shows the majority vote agreement between annotators. Additional information includes instance ID and the presence of the link between two articles.
```
{
'left_url': 'https://www.kommersant.ru/doc/4347456',
'right_url': 'https://tass.ru/kosmos/8488527',
'left_title': 'NASA: информация об отказе сотрудничать с Россией по освоению Луны некорректна',
'right_title': 'NASA назвало некорректными сообщения о нежелании США включать РФ в соглашение по Луне',
'left_timestamp': datetime.datetime(2020, 5, 15, 19, 46, 20),
'right_timestamp': datetime.datetime(2020, 5, 15, 19, 21, 36),
'label': 0,
'result': 'not_cause',
'agreement': 1.0,
'id': 'ru_tg_101',
'has_link': True
}
```
### Data Splits
| Dataset | Split | Number of Instances |
|:---------|:---------|:---------|
| ru_simple | train | 7,641 |
| | validation | 955 |
| | test | 957 |
| en_simple | train | 4,332 |
| | validation | 542 |
| | test | 542 |
| ru_full | train | 5,713 |
| | validation | 715 |
| | test | 715 |
| en_full | train | 2,009 |
| | validation | 251 |
| | test | 252 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Every candidate pair was annotated with [Yandex Toloka](https://toloka.ai/), a crowdsourcing platform. The task was to determine a relationship between two headlines, A and B. There were seven possible options: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is not linked to B. An annotation guideline was in Russian for Russian news and in English for English news.
Guidelines:
* Russian: [link](https://ilyagusev.github.io/HeadlineCause/toloka/ru/instruction.html)
* English: [link](https://ilyagusev.github.io/HeadlineCause/toloka/en/instruction.html)
Ten workers annotated every pair. The total annotation budget was 870$, with the estimated hourly wage paid to participants of 45 cents. Annotation management was semi-automatic. Scripts are available in the [Github repository](https://github.com/IlyaGusev/HeadlineCause).
#### Who are the annotators?
Yandex Toloka workers were the annotators, 457 workers for the Russian part, 180 workers for the English part.
### Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. No information about annotators is included except a platform worker ID.
## Considerations for Using the Data
### Social Impact of Dataset
We do not see any direct malicious applications of our work. The data probably do not contain offensive content, as news agencies usually do not produce it, and a keyword search returned nothing. However, there are news documents in the dataset on several sensitive topics.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data was collected by Ilya Gusev.
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{gusev2021headlinecause,
title={HeadlineCause: A Dataset of News Headlines for Detecting Causalities},
author={Ilya Gusev and Alexey Tikhonov},
year={2021},
eprint={2108.12626},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A] |
KETI-AIR/kor_corpora | 2021-09-16T07:32:28.000Z | [
"region:us"
] | KETI-AIR | null | null | null | 0 | 8 | Entry not found |
andstor/smart_contracts | 2023-10-03T21:03:56.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"arxiv:2309.09826",
"doi:10.57967/hf/1182",
"region:us"
] | andstor | Smart Contracts Dataset.
This is a dataset of verified (Etherscan.io) Smart Contracts that are deployed to the Ethereum blockchain. A set of about 100,000 to 200,000 contracts are provided, containing both Solidity and Vyper code. | @misc{storhaug2022smartcontracts,
title = {Smart Contracts Dataset},
author={André Storhaug},
year={2022}
} | null | 1 | 8 | ---
annotations_creators: []
language_creators: []
language:
- en
multilinguality:
- monolingual
pretty_name: Smart Contracts
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: verified-smart-contracts
---
# Dataset Card for Smart Contracts
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [flattened](#flattened)
- [flattened_plain_text](#flattened_plain_text)
- [inflated](#inflated)
- [inflated_plain_text](#inflated_plain_text)
- [parsed](#parsed)
- [Data Fields](#data-fields)
- [flattened](#flattened-1)
- [flattened_plain_text](#flattened_plain_text-1)
- [inflated](#inflated-1)
- [inflated_plain_text](#inflated_plain_text-1)
- [parsed](#parsed-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://andstor.github.io/smart-contracts
- **Repository:** https://github.com/andstor/verified-smart-contracts
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [André Storhaug](mailto:andr3.storhaug@gmail.com)
### Dataset Summary
This is a dataset of verified Smart Contracts from Etherscan.io that are deployed to the Ethereum blockchain. A set of about 100,000 to 200,000 contracts are provided, containing both Solidity and Vyper code.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
#### flattened
```
{
'contract_name': 'MiaKhalifaDAO',
'contract_address': '0xb3862ca215d5ed2de22734ed001d701adf0a30b4',
'language': 'Solidity',
'source_code': '// File: @openzeppelin/contracts/utils/Strings.sol\r\n\r\n\r\n// OpenZeppelin Contracts v4.4.1 (utils/Strings.sol)\r\n\r\npragma solidity ^0.8.0;\r\n\r\n/**\r\n * @dev String operations.\r\n */\r\nlibrary Strings {\r\n...',
'abi': '[{"inputs":[{"internalType":"uint256","name":"maxBatchSize_","type":"uint256"}...]',
'compiler_version': 'v0.8.7+commit.e28d00a7',
'optimization_used': False,
'runs': 200,
'constructor_arguments': '000000000000000000000000000000000000000000000000000000000000000a000...',
'evm_version': 'Default',
'library': '',
'license_type': 'MIT',
'proxy': False,
'implementation': '',
'swarm_source': 'ipfs://e490df69bd9ca50e1831a1ac82177e826fee459b0b085a00bd7a727c80d74089'
}
```
#### flattened_extended
Same fields as `flattened` but with the following additional fields:
```
{
...
'tx_count': 1074,
'balance': 38
}
```
#### flattened_plain_text
```
{
'language': 'Solidity',
'text': '// File: SafeMath.sol\r\npragma solidity =0.5.16;\r\n\r\n// a library for performing overflow-safe math...'
}
```
#### inflated
```
{
'contract_name': 'PinkLemonade',
'file_path': 'PinkLemonade.sol',
'contract_address': '0x9a5be3cc368f01a0566a613aad7183783cff7eec',
'language': 'Solidity',
'source_code': '/**\r\n\r\nt.me/pinklemonadecoin\r\n*/\r\n\r\n// SPDX-License-Identifier: MIT\r\npragma solidity ^0.8.0;\r\n\r\n\r\n/*\r\n * @dev Provides information about the current execution context, including the\r\n * sender of the transaction and its data. While these are generally available...',
'abi': '[{"inputs":[],"stateMutability":"nonpayable","type":"constructor"}...]',
'compiler_version': 'v0.8.4+commit.c7e474f2',
'optimization_used': False,
'runs': 200,
'constructor_arguments': '',
'evm_version': 'Default',
'library': '',
'license_type': 'MIT',
'proxy': False,
'implementation': '',
'swarm_source': 'ipfs://eb0ac9491a04e7a196280fd27ce355a85d79b34c7b0a83ab606d27972a06050c'
}
```
#### inflated_plain_text
```
{
'language': 'Solidity',
'text': '\\npragma solidity ^0.4.11;\\n\\ncontract ERC721 {\\n // Required methods\\n function totalSupply() public view returns (uint256 total);...'
}
```
#### parsed
```
{
'contract_name': 'BondedECDSAKeep',
'file_path': '@keep-network/keep-core/contracts/StakeDelegatable.sol',
'contract_address': '0x61935dc4ffc5c5f1d141ac060c0eef04a792d8ee',
'language': 'Solidity',
'class_name': 'StakeDelegatable',
'class_code': 'contract StakeDelegatable {\n using OperatorParams for uint256;\n\n mapping(address => Operator) internal operators;\n\n struct Operator {\n uint256 packedParams;\n address owner;\n address payable beneficiary;\n address authorizer;\n }\n\n...',
'class_documentation': '/// @title Stake Delegatable\n/// @notice A base contract to allow stake delegation for staking contracts.',
'class_documentation_type': 'NatSpecSingleLine',
'func_name': 'balanceOf',
'func_code': 'function balanceOf(address _address) public view returns (uint256 balance) {\n return operators[_address].packedParams.getAmount();\n }',
'func_documentation': '/// @notice Gets the stake balance of the specified address.\n/// @param _address The address to query the balance of.\n/// @return An uint256 representing the amount staked by the passed address.',
'func_documentation_type': 'NatSpecSingleLine',
'compiler_version': 'v0.5.17+commit.d19bba13',
'license_type': 'MIT',
'swarm_source': 'bzzr://63a152bdeccda501f3e5b77f97918c5500bb7ae07637beba7fae76dbe818bda4'
}
```
### Data Fields
#### flattened
- `contract_name` (`string`): containing the smart contract name.
- `contract_address` (`string`): containing the Ethereum address for the smart contract.
- `language` (`string`): containing the language of the smart contract.
- `source_code ` (`string`): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.
- `abi` (`string`): containing the Application Binary Interface (ABI) of the smart contract.
- `compiler_version` (`string`): containing the compiler version used to compile the smart contract.
- `optimization_used` (`boolean`): indicating if the smart contract used optimization.
- `runs` (`number`): containing the number of optimization steps used.
- `constructor_arguments` (`string`): containing the constructor arguments of the smart contract.
- `evm_version` (`string`): containing the EVM version used to compile the smart contract.
- `library` (`string`): containing the `name:address` of libraries used separated by `;`.
- `license_type` (`string`): containing the license type of the smart contract.
- `proxy` (`boolean`): indicating if the smart contract is a proxy.
- `implementation` (`string`): containing the implementation of the smart contract if it is a proxy.
- `swarm_source` (`string`): containing the swarm source of the smart contract.
#### flattened_extended
Same fields as `flattened` but with the following additional fields:
- `tx_count` (`number`): containing the number of transactions made to the smart contract.
- `balance` (`string`): containing the ether balancce of the smart contract.
#### flattened_plain_text
- `text` (`string`): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.
- `language` (`string`): containing the language of the smart contract.
#### inflated
Same fields as `flattened` but with the following additional fields:
- `file_path` (`string`): containing the original path to the file.
#### inflated_plain_text
- `text` (`string`): containing the source code of the smart contract. This contains all code needed for compilation of the contract, including libraries.
- `language` (`string`): containing the language of the smart contract.
#### parsed
- `contract_name` (`string`): containing the smart contract name.
- `file_path` (`string`): containing the original path to the file.
- `contract_address` (`string`): containing the Ethereum address for the smart contract.
- `language` (`string`): containing the language of the smart contract.
- `class_name` (`string`): containing the name of the "class" (contract).
- `class_code` (`string`): containing the source code of the "class" (contract).
- `class_documentation` (`string`): containing the documentation (code comment) of the "class" (contract).
- `class_documentation_type` (`string`): containing the documentation type of the "class" (contract). Can be one of: `NatSpecMultiLine`, `NatSpecSingleLine`, `LineComment` or `Comment`.
- `func_name` (`string`): containing the name of the function definition.
- `func_code` (`string`): containing the source code of the function.
- `func_documentation` (`string`): containing the documentation (code comment) of the contract definition (or "class").
- `func_documentation_type` (`string`): containing the documentation type of the function. Can be one of: `NatSpecMultiLine`, `NatSpecSingleLine`, `LineComment` or `Comment`.
- `compiler_version` (`string`): containing the compiler version used to compile the smart contract.
- `license_type` (`string`): containing the license type of the smart contract.
- `swarm_source` (`string`): containing the swarm source of the smart contract.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@misc{storhaug2023efficient,
title={Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding},
author={André Storhaug and Jingyue Li and Tianyuan Hu},
year={2023},
eprint={2309.09826},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
```
### Contributions
Thanks to [@andstor](https://github.com/andstor) for adding this dataset. |
gigant/african_accented_french | 2022-10-24T17:39:03.000Z | [
"task_categories:automatic-speech-recognition",
"language:fr",
"license:cc",
"region:us"
] | gigant | \
This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts:
1. Yaounde
Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female.
2. CA16
This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon.
3. Niger
This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen. | \ | null | 3 | 8 | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: African Accented French
---
## Dataset Description
- **Homepage:** http://www.openslr.org/57/
### Dataset Summary
This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts:
1. Yaounde
Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female.
2. CA16
This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon.
3. Niger
This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen.
### Languages
French
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
The train split consists of 9401 audio clips and the related sentences.
The test split consists of 1985 audio clips and the related sentences.
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. |
imvladikon/knesset_meetings_corpus | 2022-10-23T11:45:02.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:he",
"license:pddl",
"region:us"
] | imvladikon | null | null | null | 1 | 8 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- he
license:
- pddl
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Knesset Meetings Corpus
---
# Dataset Card
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://zenodo.org/record/2707356](https://zenodo.org/record/2707356)
- **Repository:** [https://github.com/NLPH/knesset-2004-2005](https://github.com/NLPH/knesset-2004-2005)
- **Paper:**
- **Point of Contact:**
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
An example of a sample:
```
{
"text": <text content of given document>,
"path": <file path to docx>
}
```
Dataset usage
Available "kneset16","kneset17","knesset_tagged" configurations
And only train set.
```python
train_ds = load_dataset("imvladikon/knesset_meetings_corpus", "kneset16", split="train")
```
The Knesset Meetings Corpus 2004-2005 is made up of two components:
* Raw texts - 282 files made up of 867,725 lines together. These can be downloaded in two formats:
* As ``doc`` files, encoded using ``windows-1255`` encoding:
* ``kneset16.zip`` - Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset16.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset16.zip?raw=true>`_
* ``kneset17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset17.zip?raw=true>`_
* As ``txt`` files, encoded using ``utf8`` encoding:
* ``kneset.tar.gz`` - An archive of all the raw text files, divided into two folders: `[Github mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset.tar.gz>`_
* ``16`` - Contains 164 text files made up of 543,228 lines together.
* ``17`` - Contains 118 text files made up of 324,497 lines together.
* ``knesset_txt_16.tar.gz``- Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_16.tar.gz>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_16.tar.gz?raw=true>`_
* ``knesset_txt_17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_17.zip?raw=true>`_
* Tokenized and morphologically tagged texts - Tagged versions exist only for the files in the ``16`` folder. The texts are encoded using `MILA's XML schema for corpora <http://www.mila.cs.technion.ac.il/eng/resources_standards.html>`_. These can be downloaded in two ways:
* ``knesset_tagged_16.tar.gz`` - An archive of all tokenized and tagged files. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/tagged/knesset_tagged_16.tar.gz>`_ `[Archive.org mirror] <https://archive.org/details/knesset_transcripts_2004_2005>`_
Mirrors
-------
This repository is a mirror of this dataset `found on MILA's website <http://www.mila.cs.technion.ac.il/eng/resources_corpora_haknesset.html>`_.
Zenodo mirror: `https://zenodo.org/record/2707356 <https://zenodo.org/record/2707356>`_
License
-------
All Knesset meeting protocols are in the `public domain <https://en.wikipedia.org/wiki/Public_domain>`_ (`רשות הציבור <https://he.wikipedia.org/wiki/%D7%A8%D7%A9%D7%95%D7%AA_%D7%94%D7%A6%D7%99%D7%91%D7%95%D7%A8>`_) by law. These files are thus in the public doamin and do not require any license or public domain dedication to set their status.
.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2707356.svg
:target: https://doi.org/10.5281/zenodo.2707356
.. |LICENCE| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain_shield.svg
:target: https://en.wikipedia.org/wiki/Public_domain
.. |PUBDOM| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain.png
:target: https://en.wikipedia.org/wiki/Public_domain
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is available under the [ Open Data Commons Public Domain Dedication & License 1.0](https://opendatacommons.org/licenses/pddl/).
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
|
it5/datasets | 2022-04-26T09:21:47.000Z | [
"region:us"
] | it5 | """
_HOMEPAGE = ""
_LICENSE = ""
_BASE_URL = "https://huggingface.co/datasets/it5/datasets/resolve/main/data/{config}_{split}.json.gz"
# Formality Style Transfer with XFormal
_FST_SPLITS = ["train", "valid", "test_0", "test_1", "test_2", "test_3"]
# Headline Generation with CHANGE-it
_HG_SPLITS = ["train", "valid", "test"]
# News Summarization with Fanpage/IlPost
_NS_SPLITS = ["train", "valid", "test_fanpage", "test_ilpost"]
# Question Answering with SQUAD-it
_QA_SPLITS = ["train", "valid", "test"]
# Question Generation with SQUAD-it
_QG_SPLITS = ["train", "valid", "test"]
# Headline Style Transfer Giornale -> Repubblica with CHANGE-it
_ST_G2R_SPLITS = ["train", "valid", "test"]
# Headline Style Transfer Repubblica -> Giornale with CHANGE-it
_ST_R2G_SPLITS = ["train", "valid", "test"]
# Wikipedia Summarization with WITS
_WITS_SPLITS = ["train", "valid", "test"]
_CONFIG_SPLITS = {
"fst": _FST_SPLITS,
"hg": _HG_SPLITS,
"ns": _NS_SPLITS,
"qa": _QA_SPLITS,
"qg": _QG_SPLITS,
"st_g2r": _ST_G2R_SPLITS,
"st_r2g": _ST_R2G_SPLITS,
"wits": _WITS_SPLITS,
}
_CONFIG_FEATS = {
"fst": ["formal", "informal"],
"hg": ["text", "target"],
"ns": ["source", "target"],
"qa": ["source", "target"],
"qg": ["text", "target"],
"st_g2r": ["headline", "full_text"],
"st_r2g": ["headline", "full_text"],
"wits": ["summary", "source"]
}
class IT5ExperimentsConfig(datasets.BuilderConfig):
def __init__(self, features, **kwargs): | """
_DESCRIPTION = | null | 0 | 8 | Entry not found |
lara-martin/Scifi_TV_Shows | 2022-09-15T18:08:56.000Z | [
"task_categories:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"Story Generation",
"region:us"
] | lara-martin | null | null | null | 2 | 8 | ---
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Scifi_TV_Shows
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids:
- other-other-story-generation
tags:
- Story Generation
paperswithcode_id: scifi-tv-plots
---
# Dataset Card for Science Fiction TV Show Plots Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Format](#format)
- [Using the Dataset with Hugging Face](#call-scifi)
- [Original Dataset Structure](#dataset-structure)
- [Files in _OriginalStoriesSeparated_ Directory](#original-stories)
- [Additional Information](#additional-information)
- [Citation](#citation)
- [Licensing](#licensing)
## Dataset Description
A collection of long-running (80+ episodes) science fiction TV show plot synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story".
Contains plot summaries from:
- Babylon 5 (https://babylon5.fandom.com/wiki/Main_Page) - 84 stories
- Doctor Who (https://tardis.fandom.com/wiki/Doctor_Who_Wiki) - 311 stories
- Doctor Who spin-offs - 95 stories
- Farscape (https://farscape.fandom.com/wiki/Farscape_Encyclopedia_Project:Main_Page) - 90 stories
- Fringe (https://fringe.fandom.com/wiki/FringeWiki) - 87 stories
- Futurama (https://futurama.fandom.com/wiki/Futurama_Wiki) - 87 stories
- Stargate (https://stargate.fandom.com/wiki/Stargate_Wiki) - 351 stories
- Star Trek (https://memory-alpha.fandom.com/wiki/Star_Trek) - 701 stories
- Star Wars books (https://starwars.fandom.com/wiki/Main_Page) - 205 stories, each book is a story
- Star Wars Rebels (https://starwarsrebels.fandom.com/wiki/Main_page) - 65 stories
- X-Files (https://x-files.fandom.com/wiki/Main_Page) - 200 stories
Total: 2276 stories
Dataset is "eventified" and generalized (see LJ Martin, P Ammanabrolu, X Wang, W Hancock, S Singh, B Harrison, and MO Riedl. Event Representations for Automated Story Generation with Deep Neural Nets, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. for details on these processes.) and split into train-test-validation sets—separated by story so that full stories will stay together—for converting events into full sentences.
---
### Format
| Dataset Split | Number of Stories in Split | Number of Sentences in Split |
| ------------- |--------------------------- |----------------------------- |
| Train | 1737 | 257,108 |
| Validation | 194 | 32,855 |
| Test | 450 | 30,938 |
#### Using the Dataset with Hugging Face
```
from datasets import load_dataset
#download and load the data
dataset = load_dataset('lara-martin/Scifi_TV_Shows')
#you can then get the individual splits
train = dataset['train']
test = dataset['test']
validation = dataset['validation']
```
Each split has 7 attributes (explained in more detail in the next section):
```
>>> print(train)
Dataset({
features: ['story_num', 'story_line', 'event', 'gen_event', 'sent', 'gen_sent', 'entities'],
num_rows: 257108
})
```
---
## Original Dataset Structure
* File names: scifi-val.txt, scifi-test.txt, & scifi-train.txt
* Each sentence of the stories are split into smaller sentences and the events are extracted.
* Each line of the file contains information about a single sentence, delimited by "|||". Each line contains, in order:
* The story number
* The line number (within the story)
* 5-tuple events in a list (subject, verb, direct object, modifier noun, preposition); e.g.,
``
[[u'Voyager', u'run', 'EmptyParameter', u'deuterium', u'out'], [u'Voyager', u'force', u'go', 'EmptyParameter', 'EmptyParameter'], [u'Voyager', u'go', 'EmptyParameter', u'mode', u'into']]
``
* generalized 5-tuple events in a list; events are generalized using WordNet and VerbNet; e.g.,
``
[['<VESSEL>0', 'function-105.2.1', 'EmptyParameter', "Synset('atom.n.01')", u'out'], ['<VESSEL>0', 'urge-58.1-1', u'escape-51.1-1', 'EmptyParameter', 'EmptyParameter'], ['<VESSEL>0', u'escape-51.1-1', 'EmptyParameter', "Synset('statistic.n.01')", u'into']]
``
* original sentence (These sentences are split to contain fewer events per sentence. For the full original sentence, see the OriginalStoriesSeparated directory.); e.g.,
``
The USS Voyager is running out of deuterium as a fuel and is forced to go into Gray mode.
``
* generalized sentence; only nouns are generalized (using WordNet); e.g.,
``
the <VESSEL>0 is running out of Synset('atom.n.01') as a Synset('matter.n.03') and is forced to go into Synset('horse.n.01') Synset('statistic.n.01').
``
* a dictionary of numbered entities by tag within the _entire story_ (e.g. the second entity in the "<ORGANIZATION>" list in the dictionary would be <ORGANIZATION>1 in the story above—index starts at 0); e.g.,
``
{'<ORGANIZATION>': ['seven of nine', 'silver blood'], '<LOCATION>': ['sickbay', 'astrometrics', 'paris', 'cavern', 'vorik', 'caves'], '<DATE>': ['an hour ago', 'now'], '<MISC>': ['selected works', 'demon class', 'electromagnetic', 'parises', 'mimetic'], '<DURATION>': ['less than a week', 'the past four years', 'thirty seconds', 'an hour', 'two hours'], '<NUMBER>': ['two', 'dozen', '14', '15'], '<ORDINAL>': ['first'], '<PERSON>': ['tom paris', 'harry kim', 'captain kathryn janeway', 'tuvok', 'chakotay', 'jirex', 'neelix', 'the doctor', 'seven', 'ensign kashimuro nozawa', 'green', 'lt jg elanna torres', 'ensign vorik'], '<VESSEL>': ['uss voyager', 'starfleet']}
``
### Files in _OriginalStoriesSeparated_ Directory
* Contains unedited, unparsed original stories scraped from the respective Fandom wikis.
* Each line is a story with sentences space-separated. After each story, there is a <EOS> tag on a new line.
* There is one file for each of the 11 domains listed above.
* These are currently not set up to be called through the Hugging Face API and must be extracted from the zip directly.
---
## Additional Information
### Citation
```
@inproceedings{Ammanabrolu2020AAAI,
title={Story Realization: Expanding Plot Events into Sentences},
author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl},
journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020},
volume={34},
number={05},
url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
}
```
---
### Licensing
The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ |
metaeval/ethics | 2023-06-02T14:45:34.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"region:us"
] | metaeval | Probing for ethics understanding | null | null | 4 | 8 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
pretty_name: ethics
size_categories:
- unknown
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids: []
---
https://github.com/hendrycks/ethics |
ngdiana/uaspeech_severity_high | 2022-02-03T22:59:37.000Z | [
"region:us"
] | ngdiana | null | null | null | 0 | 8 | Entry not found |
peixian/equity_evaluation_corpus | 2022-10-20T23:35:15.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"gender-classification",
"region:us"
] | peixian | Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 ‘Affect in Tweets’. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks. | @article{DBLP:journals/corr/abs-1805-04508,
author = {Svetlana Kiritchenko and
Saif M. Mohammad},
title = {Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems},
journal = {CoRR},
volume = {abs/1805.04508},
year = {2018},
url = {http://arxiv.org/abs/1805.04508},
archivePrefix = {arXiv},
eprint = {1805.04508},
timestamp = {Mon, 13 Aug 2018 16:47:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1805-04508.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 3 | 8 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
tags:
- gender-classification
---
# Dataset Card for equity-evaluation-corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Automatic machine learning systems can inadvertently accentuate and perpetuate inappropriate human biases. Past work on examining inappropriate biases has largely focused on just individual systems and resources. Further, there is a lack of benchmark datasets for examining inappropriate biases in system predictions. Here, we present the Equity Evaluation Corpus (EEC), which consists of 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We used the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 Affect in Tweets. We found that several of the systems showed statistically significant bias; that is, they consistently provide slightly higher sentiment intensity predictions for one race or one gender. We make the EEC freely available, and encourage its use to evaluate biases in sentiment and other NLP tasks.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `sentence`: a `string` feature.
- `template`: a `string` feature.
- `person`: a `string` feature.
- `race`: a `string` feature.
- `emotion`: a `string` feature.
- `emotion word`: a `string` feature.
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
|
joangaes/depression | 2022-03-10T13:04:18.000Z | [
"region:us"
] | joangaes | null | null | null | 0 | 8 | Entry not found |
Khedesh/PeymaNER | 2022-03-11T11:30:13.000Z | [
"region:us"
] | Khedesh | null | null | null | 1 | 8 | Entry not found |
tomekkorbak/pile-toxicity-balanced | 2022-04-06T11:07:05.000Z | [
"region:us"
] | tomekkorbak | null | null | null | 0 | 8 | ## Generation procedure
The dataset was constructed using documents from [the Pile](https://pile.eleuther.ai/) scored using using [Perspective API](http://perspectiveapi.com) toxicity scores.
The procedure was the following:
1. A chunk of the Pile (3%, 7m documents) was scored using the Perspective API.
1. The first half of this dataset is [tomekkorbak/pile-toxic-chunk-0](https://huggingface.co/datasets/tomekkorbak/pile-toxic-chunk-0), 100k *most* toxic documents of the scored chunk
2. The first half of this dataset is [tomekkorbak/pile-nontoxic-chunk-0](https://huggingface.co/datasets/tomekkorbak/pile-nontoxic-chunk-0), 100k *least* toxic documents of the scored chunk
3. Then, the dataset was shuffled and a 9:1 train-test split was done
## Basic stats
The average scores of the good and bad half are 0.0014 and 0.67, respectively. The average score of the whole dataset is 0.33; the median is 0.51.
However, the weighted average score (weighted by document length) is 0.45. Correlation between score and document length is 0.2.
Score histogram:
Mean score per Pile subset
| pile_set_name | score | length |
|:------------------|----------:|------------:|
| ArXiv | 0.141808 | 9963.82 |
| Books3 | 0.405541 | 8911.67 |
| DM Mathematics | 0.535474 | 8194 |
| Enron Emails | 0.541136 | 1406.76 |
| EuroParl | 0.373395 | 4984.36 |
| FreeLaw | 0.279582 | 8986.73 |
| Github | 0.495742 | 2184.86 |
| Gutenberg (PG-19) | 0.583263 | 4034 |
| HackerNews | 0.617917 | 3714.83 |
| NIH ExPorter | 0.0376628 | 1278.83 |
| OpenSubtitles | 0.674261 | 14881.1 |
| OpenWebText2 | 0.613273 | 2634.41 |
| PhilPapers | 0.549582 | 9693 |
| Pile-CC | 0.525136 | 2925.7 |
| PubMed Abstracts | 0.0388705 | 1282.29 |
| PubMed Central | 0.235012 | 7418.34 |
| StackExchange | 0.590904 | 2210.16 |
| USPTO Backgrounds | 0.0100077 | 2086.39 |
| Ubuntu IRC | 0.598423 | 4396.67 |
| Wikipedia (en) | 0.0136901 | 1515.89 |
| YoutubeSubtitles | 0.65201 | 4729.52 | |
hackathon-pln-es/neutral-es | 2022-10-25T10:20:48.000Z | [
"task_categories:text2text-generation",
"task_categories:translation",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:es",
"region:us"
] | hackathon-pln-es | null | null | null | 5 | 8 | ---
language:
- es
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- text2text-generation
- translation
task_ids: []
pretty_name: neutralES
---
# Spanish Gender Neutralization
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/2/29/Gender_equality_symbol_%28clipart%29.png" width="250"/>
</p>
Spanish is a beautiful language and it has many ways of referring to people, neutralizing the genders and using some of the resources inside the language. One would say *Todas las personas asistentes* instead of *Todos los asistentes* and it would end in a more inclusive way for talking about people. This dataset collects a set of manually anotated examples of gendered-to-neutral spanish transformations.
The intended use of this dataset is to train a spanish language model for translating from gendered to neutral, in order to have more inclusive sentences.
### Compiled sources
One of the major challenges was to obtain a valuable dataset that would suit gender inclusion purpose, therefore, when building the dataset, the team opted to dedicate a considerable amount of time to build it from a scratch. You can find here the results.
The data used for the model training has been manually created form a compilation of sources, obtained from a series of guidelines and manuals issued by Spanish Ministry of Health, Social Services and Equality in the matter of the usage of non-sexist language, stipulated in this linked [document](https://www.inmujeres.gob.es/servRecursos/formacion/GuiasLengNoSexista/docs/Guiaslenguajenosexista_.pdf).
**NOTE: Appart from manually anotated samples, this dataset has been further increased by applying data augmentation so a minumin number of training examples are generated.**
* [Guía para un discurso igualitario en la universidad de alicante](https://ieg.ua.es/es/documentos/normativasobreigualdad/guia-para-un-discurso-igualitario-en-la-ua.pdf)
* [Guía UC de Comunicación en Igualdad](<https://web.unican.es/unidades/igualdad/SiteAssets/igualdad/comunicacion-en-igualdad/guia%20comunicacion%20igualdad%20(web).pdf>)
* [Buenas prácticas para el tratamiento del lenguaje en igualdad](https://e-archivo.uc3m.es/handle/10016/22811)
* [Guía del lenguaje no sexista de la Universidad de Castilla-La Mancha](https://unidadigualdad.ugr.es/page/guiialenguajeuniversitarionosexista_universidaddecastillalamancha/!)
* [Guía de Lenguaje Para el Ámbito Educativo](https://www.educacionyfp.gob.es/va/dam/jcr:8ce318fd-c8ff-4ad2-97b4-7318c27d1682/guialenguajeambitoeducativo.pdf)
* [Guía para un uso igualitario y no sexista del lenguaje y dela imagen en la Universidad de Jaén](https://www.ujaen.es/servicios/uigualdad/sites/servicio_uigualdad/files/uploads/Guia_lenguaje_no_sexista.pdf)
* [Guía de uso no sexista del vocabulario español](https://www.um.es/documents/2187255/2187763/guia-leng-no-sexista.pdf/d5b22eb9-b2e4-4f4b-82aa-8a129cdc83e3)
* [Guía para el uso no sexista de la lengua castellana y de imágnes en la UPV/EHV](https://www.ehu.eus/documents/1734204/1884196/Guia_uso_no_sexista_EHU.pdf)
* [Guía de lenguaje no sexista UNED](http://portal.uned.es/pls/portal/docs/PAGE/UNED_MAIN/LAUNIVERSIDAD/VICERRECTORADOS/GERENCIA/OFICINA_IGUALDAD/CONCEPTOS%20BASICOS/GUIA_LENGUAJE.PDF)
* [COMUNICACIÓN AMBIENTAL CON PERSPECTIVA DE GÉNERO](https://cima.cantabria.es/documents/5710649/5729124/COMUNICACI%C3%93N+AMBIENTAL+CON+PERSPECTIVA+DE+G%C3%89NERO.pdf/ccc18730-53e3-35b9-731e-b4c43339254b)
* [Recomendaciones para la utilización de lenguaje no sexista](https://www.csic.es/sites/default/files/guia_para_un_uso_no_sexista_de_la_lengua_adoptada_por_csic2.pdf)
* [Estudio sobre lenguaje y contenido sexista en la Web](https://www.mujeresenred.net/IMG/pdf/Estudio_paginas_web_T-incluye_ok.pdf)
* [Nombra.en.red. En femenino y en masculino](https://www.inmujeres.gob.es/areasTematicas/educacion/publicaciones/serieLenguaje/docs/Nombra_en_red.pdf)
## Team Members
- Fernando Velasco [(fermaat)](https://huggingface.co/fermaat)
- Cibeles Redondo [(CibelesR)](https://huggingface.co/CibelesR)
- Juan Julian Cea [(Juanju)](https://huggingface.co/Juanju)
- Magdalena Kujalowicz [(MacadellaCosta)](https://huggingface.co/MacadellaCosta)
- Javier Blasco [(javiblasco)](https://huggingface.co/javiblasco)
### Enjoy and feel free to collaborate with this dataset 🤗 |
huggan/selfie2anime | 2022-04-19T20:27:47.000Z | [
"region:us"
] | huggan | null | null | null | 0 | 8 | Entry not found |
pietrolesci/scitail | 2022-04-25T10:40:47.000Z | [
"region:us"
] | pietrolesci | null | null | null | 0 | 8 | ## Overview
Original dataset is available on the HuggingFace Hub [here](https://huggingface.co/datasets/scitail).
## Dataset curation
This is the same as the `snli_format` split of the SciTail dataset available on the HuggingFace Hub (i.e., same data, same splits, etc).
The only differences are the following:
- selecting only the columns `["sentence1", "sentence2", "gold_label", "label"]`
- renaming columns with the following mapping `{"sentence1": "premise", "sentence2": "hypothesis"}`
- creating a new column "label" from "gold_label" with the following mapping `{"entailment": "entailment", "neutral": "not_entailment"}`
- encoding labels with the following mapping `{"not_entailment": 0, "entailment": 1}`
Note that there are 10 overlapping instances (as found by merging on columns "label", "premise", and "hypothesis") between
`train` and `test` splits.
## Code to create the dataset
```python
from datasets import Features, Value, ClassLabel, Dataset, DatasetDict, load_dataset
# load datasets from the Hub
dd = load_dataset("scitail", "snli_format")
ds = {}
for name, df_ in dd.items():
df = df_.to_pandas()
# select important columns
df = df[["sentence1", "sentence2", "gold_label"]]
# rename columns
df = df.rename(columns={"sentence1": "premise", "sentence2": "hypothesis"})
# encode labels
df["label"] = df["gold_label"].map({"entailment": "entailment", "neutral": "not_entailment"})
df["label"] = df["label"].map({"not_entailment": 0, "entailment": 1})
# cast to dataset
features = Features({
"premise": Value(dtype="string", id=None),
"hypothesis": Value(dtype="string", id=None),
"label": ClassLabel(num_classes=2, names=["not_entailment", "entailment"]),
})
ds[name] = Dataset.from_pandas(df, features=features)
dataset = DatasetDict(ds)
dataset.push_to_hub("scitail", token="<token>")
# check overlap between splits
from itertools import combinations
for i, j in combinations(dataset.keys(), 2):
print(
f"{i} - {j}: ",
pd.merge(
dataset[i].to_pandas(),
dataset[j].to_pandas(),
on=["label", "premise", "hypothesis"],
how="inner",
).shape[0],
)
#> train - test: 10
#> train - validation: 0
#> test - validation: 0
``` |
bigscience-data/roots_zh_uncorpus | 2022-12-12T10:59:49.000Z | [
"language:zh",
"license:cc-by-4.0",
"region:us"
] | bigscience-data | null | null | null | 2 | 8 | ---
language: zh
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_zh_uncorpus
# uncorpus
- Dataset uid: `uncorpus`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 2.8023 % of total
- 10.7390 % of ar
- 5.7970 % of fr
- 9.7477 % of es
- 2.0417 % of en
- 1.2540 % of zh
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
|
Pavithree/askHistorians | 2022-04-22T16:22:10.000Z | [
"region:us"
] | Pavithree | null | null | null | 0 | 8 | This dataset is the subset of original eli5 dataset from hugging face. |
pietrolesci/joci | 2022-04-25T13:33:08.000Z | [
"region:us"
] | pietrolesci | null | null | null | 0 | 8 | ## Overview
Original dataset available [here](https://github.com/sheng-z/JOCI/tree/master/data).
This dataset is the "full" JOCI dataset, which is the file named `joci.csv.zip`.
# Dataset curation
The following processing is applied,
- `label` column renamed to `original_label`
- creation of the `label` column using the following mapping, using common practices ([1](https://github.com/rabeehk/robust-nli/blob/c32ff958d4df68ac2fad9bf990f70d30eab9f297/data/scripts/joci.py#L22-L27), [2](https://github.com/azpoliak/hypothesis-only-NLI/blob/b045230437b5ba74b9928ca2bac5e21ae57876b9/data/convert_joci.py#L7-L12))
```
{
0: "contradiction",
1: "contradiction",
2: "neutral",
3: "neutral",
4: "neutral",
5: "entailment",
}
```
- finally, converting this to the usual NLI classes, that is `{"entailment": 0, "neutral": 1, "contradiction": 2}`
## Code to create dataset
```python
import pandas as pd
from datasets import Features, Value, ClassLabel, Dataset
# read data
df = pd.read_csv("<path to folder>/joci.csv")
# column name to lower
df.columns = df.columns.str.lower()
# rename label column
df = df.rename(columns={"label": "original_label"})
# encode labels
df["label"] = df["original_label"].map({
0: "contradiction",
1: "contradiction",
2: "neutral",
3: "neutral",
4: "neutral",
5: "entailment",
})
# encode labels
df["label"] = df["label"].map({"entailment": 0, "neutral": 1, "contradiction": 2})
# cast to dataset
features = Features({
"context": Value(dtype="string"),
"hypothesis": Value(dtype="string"),
"label": ClassLabel(num_classes=3, names=["entailment", "neutral", "contradiction"]),
"original_label": Value(dtype="int32"),
"context_from": Value(dtype="string"),
"hypothesis_from": Value(dtype="string"),
"subset": Value(dtype="string"),
})
ds = Dataset.from_pandas(df, features=features)
ds.push_to_hub("joci", token="<token>")
```
|
alisaqallah/emotion-with-length | 2022-05-10T17:57:30.000Z | [
"region:us"
] | alisaqallah | null | null | null | 0 | 8 | Entry not found |
HuggingFaceM4/something_something_v2 | 2022-10-20T21:35:22.000Z | [
"task_categories:other",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:1706.04261",
"region:us"
] | HuggingFaceM4 | The Something-Something dataset (version 2) is a collection of 220,847 labeled video clips of humans performing pre-defined, basic actions with everyday objects. It is designed to train machine learning models in fine-grained understanding of human hand gestures like putting something into something, turning something upside down and covering something with something. | @inproceedings{goyal2017something,
title={The" something something" video database for learning and evaluating visual common sense},
author={Goyal, Raghav and Ebrahimi Kahou, Samira and Michalski, Vincent and Materzynska, Joanna and Westphal, Susanne and Kim, Heuna and Haenel, Valentin and Fruend, Ingo and Yianilos, Peter and Mueller-Freitag, Moritz and others},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={5842--5850},
year={2017}
} | null | 2 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: something-something
pretty_name: Something Something v2
tags: []
---
# Dataset Card for Something Something v2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.qualcomm.com/software/ai-datasets/something-something
- **Repository:**
- **Paper:** https://arxiv.org/abs/1706.04261
- **Leaderboard:** https://paperswithcode.com/sota/action-recognition-in-videos-on-something
- **Point of Contact:** mailto: research.datasets@qti.qualcomm.com
### Dataset Summary
The Something-Something dataset (version 2) is a collection of 220,847 labeled video clips of humans performing pre-defined, basic actions with everyday objects. It is designed to train machine learning models in fine-grained understanding of human hand gestures like putting something into something, turning something upside down and covering something with something.
### Supported Tasks and Leaderboards
- `action-recognition`: The goal of this task is to classify actions happening in a video. This is a multilabel classification. The leaderboard is available [here](https://paperswithcode.com/sota/action-recognition-in-videos-on-something)
### Languages
The annotations in the dataset are in English.
## Dataset Structure
### Data Instances
```
{
"video_id": "41775",
"video": "<ExFileObject name="">",
"text": "moving drawer of night stand",
"label": 33,
"placeholders": ["drawer", "night stand"]}
}
```
### Data Fields
- `video_id`: `str` Unique identifier for each video.
- `video`: `str` File object
- `placeholders`: `List[str]` Objects present in the video
- `text`: `str` Description of what is happening in the video
- `labels`: `int` Action found in the video. Indices from 0 to 173.
<details>
<summary>
Click here to see the full list of Something-Something-v2 class labels mapping:
</summary>
|0 | Approaching something with your camera |
|1 | Attaching something to something |
|2 | Bending something so that it deforms |
|3 | Bending something until it breaks |
|4 | Burying something in something |
|5 | Closing something |
|6 | Covering something with something |
|7 | Digging something out of something |
|8 | Dropping something behind something |
|9 | Dropping something in front of something |
|10 | Dropping something into something |
|11 | Dropping something next to something |
|12 | Dropping something onto something |
|13 | Failing to put something into something because something does not fit |
|14 | Folding something |
|15 | Hitting something with something |
|16 | Holding something |
|17 | Holding something behind something |
|18 | Holding something in front of something |
|19 | Holding something next to something |
|20 | Holding something over something |
|21 | Laying something on the table on its side, not upright |
|22 | Letting something roll along a flat surface |
|23 | Letting something roll down a slanted surface |
|24 | Letting something roll up a slanted surface, so it rolls back down |
|25 | Lifting a surface with something on it but not enough for it to slide down |
|26 | Lifting a surface with something on it until it starts sliding down |
|27 | Lifting something up completely without letting it drop down |
|28 | Lifting something up completely, then letting it drop down |
|29 | Lifting something with something on it |
|30 | Lifting up one end of something without letting it drop down |
|31 | Lifting up one end of something, then letting it drop down |
|32 | Moving away from something with your camera |
|33 | Moving part of something |
|34 | Moving something across a surface until it falls down |
|35 | Moving something across a surface without it falling down |
|36 | Moving something and something away from each other |
|37 | Moving something and something closer to each other |
|38 | Moving something and something so they collide with each other |
|39 | Moving something and something so they pass each other |
|40 | Moving something away from something |
|41 | Moving something away from the camera |
|42 | Moving something closer to something |
|43 | Moving something down |
|44 | Moving something towards the camera |
|45 | Moving something up |
|46 | Opening something |
|47 | Picking something up |
|48 | Piling something up |
|49 | Plugging something into something |
|50 | Plugging something into something but pulling it right out as you remove your hand |
|51 | Poking a hole into some substance |
|52 | Poking a hole into something soft |
|53 | Poking a stack of something so the stack collapses |
|54 | Poking a stack of something without the stack collapsing |
|55 | Poking something so it slightly moves |
|56 | Poking something so lightly that it doesn't or almost doesn't move |
|57 | Poking something so that it falls over |
|58 | Poking something so that it spins around |
|59 | Pouring something into something |
|60 | Pouring something into something until it overflows |
|61 | Pouring something onto something |
|62 | Pouring something out of something |
|63 | Pretending or failing to wipe something off of something |
|64 | Pretending or trying and failing to twist something |
|65 | Pretending to be tearing something that is not tearable |
|66 | Pretending to close something without actually closing it |
|67 | Pretending to open something without actually opening it |
|68 | Pretending to pick something up |
|69 | Pretending to poke something |
|70 | Pretending to pour something out of something, but something is empty |
|71 | Pretending to put something behind something |
|72 | Pretending to put something into something |
|73 | Pretending to put something next to something |
|74 | Pretending to put something on a surface |
|75 | Pretending to put something onto something |
|76 | Pretending to put something underneath something |
|77 | Pretending to scoop something up with something |
|78 | Pretending to spread air onto something |
|79 | Pretending to sprinkle air onto something |
|80 | Pretending to squeeze something |
|81 | Pretending to take something from somewhere |
|82 | Pretending to take something out of something |
|83 | Pretending to throw something |
|84 | Pretending to turn something upside down |
|85 | Pulling something from behind of something |
|86 | Pulling something from left to right |
|87 | Pulling something from right to left |
|88 | Pulling something onto something |
|89 | Pulling something out of something |
|90 | Pulling two ends of something but nothing happens |
|91 | Pulling two ends of something so that it gets stretched |
|92 | Pulling two ends of something so that it separates into two pieces |
|93 | Pushing something from left to right |
|94 | Pushing something from right to left |
|95 | Pushing something off of something |
|96 | Pushing something onto something |
|97 | Pushing something so it spins |
|98 | Pushing something so that it almost falls off but doesn't |
|99 | Pushing something so that it falls off the table |
|100 | Pushing something so that it slightly moves |
|101 | Pushing something with something |
|102 | Putting number of something onto something |
|103 | Putting something and something on the table |
|104 | Putting something behind something |
|105 | Putting something in front of something |
|106 | Putting something into something |
|107 | Putting something next to something |
|108 | Putting something on a flat surface without letting it roll |
|109 | Putting something on a surface |
|110 | Putting something on the edge of something so it is not supported and falls down |
|111 | Putting something onto a slanted surface but it doesn't glide down |
|112 | Putting something onto something |
|113 | Putting something onto something else that cannot support it so it falls down |
|114 | Putting something similar to other things that are already on the table |
|115 | Putting something that can't roll onto a slanted surface, so it slides down |
|116 | Putting something that can't roll onto a slanted surface, so it stays where it is |
|117 | Putting something that cannot actually stand upright upright on the table, so it falls on its side |
|118 | Putting something underneath something |
|119 | Putting something upright on the table |
|120 | Putting something, something and something on the table |
|121 | Removing something, revealing something behind |
|122 | Rolling something on a flat surface |
|123 | Scooping something up with something |
|124 | Showing a photo of something to the camera |
|125 | Showing something behind something |
|126 | Showing something next to something |
|127 | Showing something on top of something |
|128 | Showing something to the camera |
|129 | Showing that something is empty |
|130 | Showing that something is inside something |
|131 | Something being deflected from something |
|132 | Something colliding with something and both are being deflected |
|133 | Something colliding with something and both come to a halt |
|134 | Something falling like a feather or paper |
|135 | Something falling like a rock |
|136 | Spilling something behind something |
|137 | Spilling something next to something |
|138 | Spilling something onto something |
|139 | Spinning something so it continues spinning |
|140 | Spinning something that quickly stops spinning |
|141 | Spreading something onto something |
|142 | Sprinkling something onto something |
|143 | Squeezing something |
|144 | Stacking number of something |
|145 | Stuffing something into something |
|146 | Taking one of many similar things on the table |
|147 | Taking something from somewhere |
|148 | Taking something out of something |
|149 | Tearing something into two pieces |
|150 | Tearing something just a little bit |
|151 | Throwing something |
|152 | Throwing something against something |
|153 | Throwing something in the air and catching it |
|154 | Throwing something in the air and letting it fall |
|155 | Throwing something onto a surface |
|156 | Tilting something with something on it slightly so it doesn't fall down |
|157 | Tilting something with something on it until it falls off |
|158 | Tipping something over |
|159 | Tipping something with something in it over, so something in it falls out |
|160 | Touching (without moving) part of something |
|161 | Trying but failing to attach something to something because it doesn't stick |
|162 | Trying to bend something unbendable so nothing happens |
|163 | Trying to pour something into something, but missing so it spills next to it |
|164 | Turning something upside down |
|165 | Turning the camera downwards while filming something |
|166 | Turning the camera left while filming something |
|167 | Turning the camera right while filming something |
|168 | Turning the camera upwards while filming something |
|169 | Twisting (wringing) something wet until water comes out |
|170 | Twisting something |
|171 | Uncovering something |
|172 | Unfolding something |
|173 | Wiping something off of something |
</details>
### Data Splits
| |train |validation| test |
|-------------|------:|---------:|------:|
|# of examples|168913|24777 |27157 |
## Dataset Creation
### Curation Rationale
From the paper:
> Neural networks trained on datasets such as ImageNet have led to major advances
in visual object classification. One obstacle that prevents networks from reasoning more
deeply about complex scenes and situations, and from integrating visual knowledge with natural language,
like humans do, is their lack of common sense knowledge about the physical world.
Videos, unlike still images, contain a wealth of detailed information about the physical world.
However, most labelled video datasets represent high-level concepts rather than detailed physical aspects
about actions and scenes. In this work, we describe our ongoing collection of the
“something-something” database of video prediction tasks whose solutions require a common sense
understanding of the depicted situation
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> As outlined is Section 3 videos available online are largely unsuitable for the goal of learning
simple (but finegrained) visual concepts. We therefore ask crowd-workers to provide videos
given labels instead of the other way around.
#### Who are the source language producers?
The dataset authors
### Annotations
#### Annotation process
The label is given first and then the video is collected by an AMT worker. More fine-grained details on the process are in the Section 4 of the work.
#### Who are the annotators?
AMT workers
### Personal and Sensitive Information
Nothing specifically discussed in the paper.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset is useful for action recognition pretraining due to diverse set of actions that happen in it.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
### Licensing Information
License is a one-page document as defined by QualComm. Please read the license document in detail before using this dataset [here](https://developer.qualcomm.com/downloads/data-license-agreement-research-use?referrer=node/68935).
### Citation Information
```bibtex
@inproceedings{goyal2017something,
title={The" something something" video database for learning and evaluating visual common sense},
author={Goyal, Raghav and Ebrahimi Kahou, Samira and Michalski, Vincent and Materzynska, Joanna and Westphal, Susanne and Kim, Heuna and Haenel, Valentin and Fruend, Ingo and Yianilos, Peter and Mueller-Freitag, Moritz and others},
booktitle={Proceedings of the IEEE international conference on computer vision},
pages={5842--5850},
year={2017}
}
```
### Contributions
Thanks to [@apsdehal](https://github.com/apsdehal) for adding this dataset. |
bigscience-data/roots_en_no_code_stackexchange | 2022-12-12T11:02:06.000Z | [
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | bigscience-data | null | null | null | 0 | 8 | ---
language: en
license: cc-by-sa-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_no_code_stackexchange
# Stack Exchange Website
- Dataset uid: `no_code_stackexchange`
### Description
Launched in 2010, the Stack Exchange network comprises 173 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.
### Homepage
https://stackexchange.com/
### Licensing
- open license
- cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
Subscriber Content
You agree that any and all content, including without limitation any and all text, graphics, logos, tools, photographs, images, illustrations, software or source code, audio and video, animations, and product feedback (collectively, “Content”) that you provide to the public Network (collectively, “Subscriber Content”), is perpetually and irrevocably licensed to Stack Overflow on a worldwide, royalty-free, non-exclusive basis pursuant to Creative Commons licensing terms (CC BY-SA 4.0), and you grant Stack Overflow the perpetual and irrevocable right and license to access, use, process, copy, distribute, export, display and to commercially exploit such Subscriber Content, even if such Subscriber Content has been contributed and subsequently removed by you as reasonably necessary to, for example (without limitation):
Provide, maintain, and update the public Network
Process lawful requests from law enforcement agencies and government agencies
Prevent and address security incidents and data security features, support features, and to provide technical assistance as it may be required
Aggregate data to provide product optimization
This means that you cannot revoke permission for Stack Overflow to publish, distribute, store and use such content and to allow others to have derivative rights to publish, distribute, store and use such content. The CC BY-SA 4.0 license terms are explained in further detail by Creative Commons, and the license terms applicable to content are explained in further detail here. You should be aware that all Public Content you contribute is available for public copy and redistribution, and all such Public Content must have appropriate attribution.
As stated above, by agreeing to these Public Network Terms you also agree to be bound by the terms and conditions of the Acceptable Use Policy incorporated herein, and hereby acknowledge and agree that any and all Public Content you provide to the public Network is governed by the Acceptable Use Policy.
### Speaker Locations
- Northern America
### Sizes
- 0.5414 % of total
- 2.9334 % of en
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
|
bigscience-data/roots_en_wikipedia | 2022-12-12T11:03:18.000Z | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | null | 2 | 8 | ---
language: en
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
|
bigscience-data/roots_en_wiktionary | 2022-12-12T11:03:23.000Z | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | null | 1 | 8 | ---
language: en
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
|
strombergnlp/ans-stance | 2022-10-25T21:45:09.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:apache-2.0",
"stance-detection",
"arxiv:2005.10410",
"... | strombergnlp | The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance. | @inproceedings{,
title = "Stance Prediction and Claim Verification: An {A}rabic Perspective",
author = "Khouja, Jude",
booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
} | null | 1 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: ans-stance
tags:
- stance-detection
---
# Dataset Card for AraStance
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/latynt/ans](https://github.com/latynt/ans)
- **Paper:** [https://arxiv.org/abs/2005.10410](https://arxiv.org/abs/2005.10410)
- **Point of Contact:** [Jude Khouja](jude@latynt.com)
### Dataset Summary
The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.
### Languages
Arabic
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'id': '0',
's1': 'هجوم صاروخي يستهدف مطار في طرابلس ويجبر ليبيا على تغيير مسار الرحلات الجوية',
's2': 'هدوء الاشتباكات فى طرابلس',
'stance': 0
}
```
### Data Fields
- `id`: a 'string' feature.
- `s1`: a 'string' expressing a claim/topic.
- `s2`: a 'string' to be classified for its stance to the source.
- `stance`: a class label representing the stance the article expresses towards the claim. Full tagset with indices:
```
0: "disagree",
1: "agree",
2: "other",
```
### Data Splits
|name|instances|
|----|----:|
|train|2652|
|validation|755|
|test|379|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors
### Licensing Information
The authors distribute this data under the Apache License, Version 2.0
### Citation Information
```
@inproceedings{,
title = "Stance Prediction and Claim Verification: An {A}rabic Perspective",
author = "Khouja, Jude",
booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
}
```
### Contributions
Thanks to [mkonxd](https://github.com/mkonxd) for adding this dataset. |
strombergnlp/ara-stance | 2022-10-25T21:47:05.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"stance-detection",
"arxiv:2104.13559",
"r... | strombergnlp | The AraStance dataset contains true and false claims, where each claim is paired with one or more documents. Each claim–article pair has a stance label: agree, disagree, discuss, or unrelated. | @article{arastance,
url = {https://arxiv.org/abs/2104.13559},
author = {Alhindi, Tariq and Alabdulkarim, Amal and Alshehri, Ali and Abdul-Mageed, Muhammad and Nakov, Preslav},
title = {AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking},
year = {2021},
copyright = {Creative Commons Attribution 4.0 International}
} | null | 1 | 8 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: ara-stance
tags:
- stance-detection
---
# Dataset Card for AraStance
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/Tariq60/arastance](https://github.com/Tariq60/arastance)
- **Paper:** [https://arxiv.org/abs/2104.13559](https://arxiv.org/abs/2104.13559)
- **Point of Contact:** [Tariq Alhindi](tariq@cs.columbia.edu)
### Dataset Summary
The AraStance dataset contains true and false claims, where each claim is paired with one or more documents. Each claim–article pair has a stance label: agree, disagree, discuss, or unrelated.
### Languages
Arabic
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'id': '0',
'claim': 'تم رفع صورة السيسي في ملعب ليفربول',
'article': 'خطفت مكة محمد صلاح نجلة نجم ليفربول الإنجليزي الأنظار في ظهورها بملعب آنفيلد عقب مباراة والدها أمام برايتون في ختام الدوري الإنجليزي والتي انتهت بفوز الأول برباعية نظيفة. وأوضحت صحيفة "ميرور" البريطانية أن مكة محمد صلاح أضفت حالة من المرح في ملعب آنفيلد أثناء مداعبة الكرة بعد تتويج نجم منتخب مصر بجائزة هداف الدوري الإنجليزي. وأشارت إلى أن مكة أظهرت بعضًا من مهاراتها بمداعبة الكرة ونجحت في خطف قلوب مشجعي الريدز.',
'stance': 3
}
```
### Data Fields
- `id`: a 'string' feature.
- `claim`: a 'string' expressing a claim/topic.
- `article`: a 'string' to be classified for its stance to the source.
- `stance`: a class label representing the stance the article expresses towards the claim. Full tagset with indices:
```
0: "Agree",
1: "Disagree",
2: "Discuss",
3: "Unrelated",
```
### Data Splits
|name|instances|
|----|----:|
|train|2848|
|validation|569|
|test|646|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0
### Citation Information
```
@article{arastance,
url = {https://arxiv.org/abs/2104.13559},
author = {Alhindi, Tariq and Alabdulkarim, Amal and Alshehri, Ali and Abdul-Mageed, Muhammad and Nakov, Preslav},
title = {AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking},
year = {2021},
copyright = {Creative Commons Attribution 4.0 International}
}
```
### Contributions
Thanks to [mkonxd](https://github.com/mkonxd) for adding this dataset. |
anton-l/earnings22 | 2022-06-08T12:39:18.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | anton-l | The Earnings 22 dataset ( also referred to as earnings22 ) is a 119-hour corpus of English-language earnings calls collected from global companies.
The primary purpose is to serve as a benchmark for industrial and academic automatic speech recognition (ASR) models on real-world accented speech. | @misc{https://doi.org/10.48550/arxiv.2203.15591,
doi = {10.48550/ARXIV.2203.15591},
url = {https://arxiv.org/abs/2203.15591},
author = {Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Earnings-22: A Practical Benchmark for Accents in the Wild},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
} | null | 0 | 8 | ---
license: cc-by-sa-4.0
---
|
BeIR/quora-qrels | 2022-10-23T06:07:21.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 0 | 8 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- 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/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- 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/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
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.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `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...."}`
- `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?"}`
- `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`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| 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`` |
| 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`` |
| 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`` |
| 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) |
| 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`` |
| 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`` |
| 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`` |
| 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) |
| 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) |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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`` |
| 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) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
AhmedSSabir/Textual-Image-Caption-Dataset | 2023-07-11T07:47:22.000Z | [
"task_categories:image-to-text",
"task_categories:image-classification",
"task_categories:visual-question-answering",
"task_categories:sentence-similarity",
"language:en",
"image captioning",
"language grounding",
"visual semantic",
"semantic similarity",
"arxiv:2301.08784",
"arxiv:1408.5882",
... | AhmedSSabir | null | null | null | 4 | 8 | ---
task_categories:
- image-to-text
- image-classification
- visual-question-answering
- sentence-similarity
language:
- en
tags:
- image captioning
- language grounding
- visual semantic
- semantic similarity
pretty_name: ' image captioning language grounding visual semantic '
---
#update: June-2023 add both soft/*hard-label* to visual_caption_cosine_score (0.2, 0.3, 0.4, and 0.5)
# Introduction
Modern image captaining relies heavily on extracting knowledge, from images such as objects,
to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset
for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information
about the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task,
such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.
Please refer to [project page](https://sabirdvd.github.io/project_page/Dataset_2022/index.html) and [Github](https://github.com/ahmedssabir/Visual-Semantic-Relatedness-Dataset-for-Image-Captioning) for more information. [](https://arxiv.org/abs/2301.08784) [](https://ahmed.jp/project_page/Dataset_2022/index.html)
For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic)
# Overview
We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP,
and Faster R-CNN to extract object information for each image. We use three filter approaches
to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier
is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects.
(3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong
relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then
we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage
of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow 1D-CNN (Kim, 2014)
to estimate the visual relatedness score.
<!--
## Dataset
(<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>)
### Sample
```
|---------------+--------------+---------+---------------------------------------------------|
| VC1 | VC2 | VC3 | human annoated caption |
| ------------- | ----------- | --------| ------------------------------------------------- |
| cheeseburger | plate | hotdog | a plate with a hamburger fries and tomatoes |
| bakery | dining table | website | a table having tea and a cake on it |
| gown | groom | apron | its time to cut the cake at this couples wedding |
|---------------+--------------+---------+---------------------------------------------------|
```
-->
### Download
0. [Dowload Raw data with ID and Visual context](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> original dataset with related ID caption [train2014](https://cocodataset.org/#download)
1. [Downlod Data with cosine score](https://www.dropbox.com/s/55sit8ow9tems4u/visual_caption_cosine_score.zip?dl=0)-> soft cosine lable with **th** 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1]
2. [Dowload Overlaping visual with caption](https://www.dropbox.com/s/br8nhnlf4k2czo8/COCO_overlaping_dataset.txt?dl=0)-> Overlap visual context and the human annotated caption
3. [Download Dataset (tsv file)](https://www.dropbox.com/s/dh38xibtjpohbeg/train_all.zip?dl=0) 0.0-> raw data with hard lable without cosine similairty and with **th**reshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4
4. [Download Dataset GenderBias](https://www.dropbox.com/s/1wki0b0d21078mj/gender%20natural.zip?dl=0)-> man/woman replaced with person class label
For future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by
employing unsupervised learning (i.e. contrastive learning). (work in progress)
1. [Download CC](https://www.dropbox.com/s/pc1uv2rf6nqdp57/CC_caption_40.txt.zip) -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions)
2. [Download CC+wiki](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> CC+1M-wiki 3M (3255928)
3. [Download CC+wiki+COCO](https://www.dropbox.com/s/k7oqwr9a1a0h8x1/CC_caption_40%2Bwiki%2BCOCO.txt.zip) -> CC+wiki+COCO-Caption 3.5M (366984)
4. [Download COCO-caption+wiki](https://www.dropbox.com/s/wc4k677wp24kzhh/COCO%2Bwiki.txt.zip) -> COCO-caption +wiki 1.4M (1413915)
5. [Download COCO-caption+wiki+CC+8Mwiki](https://www.dropbox.com/s/xhfx32sjy2z5bpa/11M_wiki_7M%2BCC%2BCOCO.txt.zip) -> COCO-caption+wiki+CC+8Mwiki 11M (11541667)
## Citation
The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it:
```bibtex
@article{sabir2023visual,
title={Visual Semantic Relatedness Dataset for Image Captioning},
author={Sabir, Ahmed and Moreno-Noguer, Francesc and Padr{\'o}, Llu{\'\i}s},
journal={arXiv preprint arXiv:2301.08784},
year={2023}
}
``` |
omarxadel/MaWPS-ar | 2022-07-12T15:31:07.000Z | [
"task_categories:text2text-generation",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"language:en",
"language:ar",
"license:mit",
"region:us"
] | omarxadel | null | null | null | 0 | 8 | ---
annotations_creators:
- crowdsourced
language:
- en
- ar
language_creators:
- found
license:
- mit
multilinguality:
- multilingual
pretty_name: MAWPS_ar
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- text2text-generation
task_ids:
- explanation-generation
---
# Dataset Card for MAWPS_ar
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
MAWPS: A Math Word Problem Repository
### Supported Tasks
Math Word Problem Solving
### Languages
Supports Arabic and English
## Dataset Structure
### Data Fields
- `text_en`: a `string` feature.
- `text_ar`: a `string` feature.
- `eqn`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 3636| 1040| 520|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[Rik Koncel-Kedziorski**, Subhro Roy**, Aida Amini, Nate Kushman and Hannaneh Hajishirzi.](https://aclanthology.org/N16-1136.pdf)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
Special thanks to Associate Professor Marwan Torki and all my colleagues in CC491N (NLP) class for helping me translate this dataset. |
CloverSearch/cc-news-mutlilingual | 2022-06-19T07:13:18.000Z | [
"region:us"
] | CloverSearch | null | null | null | 3 | 8 | Entry not found |
c17hawke/stackoverflow-dataset | 2022-06-18T21:27:37.000Z | [
"region:us"
] | c17hawke | null | null | null | 4 | 8 | Entry not found |
lewtun/dog_food | 2022-07-03T05:15:18.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | lewtun | null | null | null | 0 | 8 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Dog vs Food Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for the Dog 🐶 vs. Food 🍔 (a.k.a. Dog Food) Dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**: https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-
- **Repository:** : https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-
- **Paper:** : N/A
- **Leaderboard:**: N/A
- **Point of Contact:**: @sasha
### Dataset Summary
This is a dataset for multiclass image classification, between 'dog', 'chicken', and 'muffin' classes.
The 'dog' class contains images of dogs that look like fried chicken and some that look like images of muffins, while the 'chicken' and 'muffin' classes contains images of (you guessed it) fried chicken and muffins 😋
### Supported Tasks and Leaderboards
TBC
### Languages
The labels are in English (['dog', 'chicken', 'muffin'])
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=300x470 at 0x7F176094EF28>,
'label': 0}
}
```
### Data Fields
- img: A `PIL.JpegImageFile` object containing the 300x470. image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- label: 0-1 with the following correspondence
0 dog
1 food
### Data Splits
Train (1875 images) and Test (625 images)
## Dataset Creation
### Curation Rationale
N/A
### Source Data
#### Initial Data Collection and Normalization
This dataset was taken from the [qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins?](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-) Github repository and randomly splitting 25% of the data for validation.
### Annotations
#### Annotation process
This data was scraped from the internet and annotated based on the query words.
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
N/A
### Discussion of Biases
This dataset is balanced -- it has an equal number of images of dogs (1000) compared to chicken (1000 and muffin (1000). This should be taken into account when evaluating models.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
This dataset was created by @lanceyjt, @yl3829, @wesleytao, @qw2243c and @asyouhaveknown
### Licensing Information
No information is indicated on the original [github repository](https://github.com/qw2243c/Image-Recognition-Dogs-Fried-Chicken-or-Blueberry-Muffins-).
### Citation Information
N/A
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset.
|
Toygar/turkish-offensive-language-detection | 2022-11-17T18:52:14.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-2.0",
"offensive-language-classification",
"region:us"
] | Toygar | null | null | null | 4 | 8 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- tr
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids: []
pretty_name: Turkish Offensive Language Detection Dataset
tags:
- offensive-language-classification
---
# Dataset Summary
This dataset is enhanced version of existing offensive language studies. Existing studies are highly imbalanced, and solving this problem is too costly. To solve this, we proposed contextual data mining method for dataset augmentation. Our method is basically prevent us from retrieving random tweets and label individually. We can directly access almost exact hate related tweets and label them directly without any further human interaction in order to solve imbalanced label problem.
In addition, existing studies *(can be found at Reference section)* are merged to create even more comprehensive and robust dataset for Turkish offensive language detection task.
The file train.csv contains 42,398, test.csv contains 8,851, valid.csv contains 1,756 annotated tweets.
# Dataset Structure
A binary dataset with with (0) Not Offensive and (1) Offensive tweets.
### Task and Labels
Offensive language identification:
- (0) Not Offensive - Tweet does not contain offense or profanity.
- (1) Offensive - Tweet contains offensive language or a targeted (veiled or direct) offense
### Data Splits
| | train | test | dev |
|------:|:------|:-----|:-----|
| 0 (Not Offensive) | 22,589 | 4,436 | 1,402 |
| 1 (Offensive) | 19,809 | 4,415 | 354 |
### Citation Information
```
T. Tanyel, B. Alkurdi and S. Ayvaz, "Linguistic-based Data Augmentation Approach for Offensive Language Detection," 2022 7th International Conference on Computer Science and Engineering (UBMK), 2022, pp. 1-6, doi: 10.1109/UBMK55850.2022.9919562.
```
### Paper codes
https://github.com/toygarr/lingda
# References
We merged open-source offensive language dataset studies in Turkish to increase contextuality with existing data even more, before our method is applied.
- https://huggingface.co/datasets/offenseval2020_tr
- https://github.com/imayda/turkish-hate-speech-dataset-2
- https://www.kaggle.com/datasets/kbulutozler/5k-turkish-tweets-with-incivil-content
|
ziwenyd/transcoder-geeksforgeeks | 2022-08-03T14:59:08.000Z | [
"license:mit",
"region:us"
] | ziwenyd | null | null | null | 3 | 8 | ---
license: mit
---
# statistics
cpp-java: 627 pairs
python-java: 616 pairs
cpp-python: 545 pairs
|
owaiskha9654/PubMed_MultiLabel_Text_Classification_Dataset_MeSH | 2023-01-30T09:50:44.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"size_categories:10K<n<100K",
"source_datasets:BioASQ Task A",
"language:en",
"license:afl-3.0",
"region:us"
] | owaiskha9654 | null | null | null | 6 | 8 | ---
language:
- en
license: afl-3.0
source_datasets:
- BioASQ Task A
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: BioASQ, PUBMED
size_categories:
- 10K<n<100K
---
This dataset consists of a approx 50k collection of research articles from **PubMed** repository. Originally these documents are manually annotated by Biomedical Experts with their MeSH labels and each articles are described in terms of 10-15 MeSH labels. In this Dataset we have huge numbers of labels present as a MeSH major which is raising the issue of extremely large output space and severe label sparsity issues. To solve this Issue Dataset has been Processed and mapped to its root as Described in the Below Figure.
 |
philschmid/processed_bert_dataset | 2022-08-14T08:43:17.000Z | [
"region:us"
] | philschmid | null | null | null | 1 | 8 | Entry not found |
clips/20Q | 2022-08-21T20:54:06.000Z | [
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"20Q",
"Twenty Questions",
"20 Questions",
"region:us"
] | clips | 20Q | null | null | 2 | 8 | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: 20Q - World Knowledge Benchmark
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- 20Q
- Twenty Questions
- 20 Questions
task_categories:
- question-answering
task_ids: []
---
# Dataset Card for 20Q
|
pinecone/movielens-recent-ratings | 2022-08-23T10:00:17.000Z | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"movielens",
"recommendation",
"collaborative filtering",
"region:us"
] | pinecone | This dataset streams recent user ratings from the MovieLens 25M dataset and adds poster URLs. | @InProceedings{huggingface:dataset,
title = {MovieLens Ratings},
author={Ismail Ashraq, James Briggs},
year={2022}
} | null | 1 | 8 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: MovieLens User Ratings
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- movielens
- recommendation
- collaborative filtering
task_categories: []
task_ids: []
---
# MovieLens User Ratings
This dataset contains ~1M user ratings, consisting of ~10k of the most recent movies from the MovieLens 25M dataset, for which over 30k unique users have rated. The dataset is streamed from the MovieLens 25M dataset, filters for the recent movies, and returns the user ratings for those. After a few joins and checks, we get this dataset. Included are the URLs of the respective movie posters.
The dataset is part of an example on [building a movie recommendation engine](https://www.pinecone.io/docs/examples/movie-recommender-system/) with vector search. |
jamescalam/unsplash-image-text | 2022-09-06T22:37:14.000Z | [
"region:us"
] | jamescalam | This is a dataset that streams photos data from the Unsplash 25K servers. | @InProceedings{huggingface:dataset,
title = {Unsplash Lite Dataset Images},
author={Unsplash},
year={2022}
} | null | 1 | 8 | Entry not found |
bigbio/biosses | 2022-12-22T15:32:58.000Z | [
"multilinguality:monolingual",
"language:en",
"license:gpl-3.0",
"region:us"
] | bigbio | BIOSSES computes similarity of biomedical sentences by utilizing WordNet as the
general domain ontology and UMLS as the biomedical domain specific ontology.
The original paper outlines the approaches with respect to using annotator
score as golden standard. Source view will return all annotator score
individually whereas the Bigbio view will return the mean of the annotator
score. | @article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={Soğancıoğlu, Gizem, Hakime Öztürk, and Arzucan Özgür},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
} | null | 0 | 8 | ---
language:
- en
bigbio_language:
- English
license: gpl-3.0
multilinguality: monolingual
bigbio_license_shortname: GPL_3p0
pretty_name: BIOSSES
homepage: https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
bigbio_pubmed: false
bigbio_public: true
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for BIOSSES
## Dataset Description
- **Homepage:** https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** STS
BIOSSES computes similarity of biomedical sentences by utilizing WordNet as the general domain ontology and UMLS as the biomedical domain specific ontology. The original paper outlines the approaches with respect to using annotator score as golden standard. Source view will return all annotator score individually whereas the Bigbio view will return the mean of the annotator score.
## Citation Information
```
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={Soğancıoğlu, Gizem, Hakime Öztürk, and Arzucan Özgür},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
```
|
vialibre/splittedspanish3bwc | 2023-01-24T18:17:47.000Z | [
"multilinguality:monolingual",
"language:es",
"license:mit",
"region:us"
] | vialibre | null | @dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
} | null | 0 | 8 | ---
language:
- 'es'
multilinguality:
- monolingual
pretty_name: "Unannotated Spanish 3 Billion Words Corpora"
license:
- mit
---
# Dataset Card for Unannotated Spanish 3 Billion Words Corpora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Source Data](#source-data)
- [Data Subset](#data-subset)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/josecannete/spanish-corpora
- **Paper:** https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf
### Dataset Summary
* Number of lines: 300904000 (300M)
* Number of tokens: 2996016962 (3B)
* Number of chars: 18431160978 (18.4B)
### Languages
* Spanish
### Source Data
* Available to download here: [Zenodo](https://doi.org/10.5281/zenodo.3247731)
### Data Subset
* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019.
* ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php)
* EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php)
* MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php)
* OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php)
* DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php)
* DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php)
* ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php)
* EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php)
* Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php)
* GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php)
* JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php)
* News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php)
* TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php)
* UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php)
## Additional Information
### Licensing Information
* [MIT Licence](https://github.com/josecannete/spanish-corpora/blob/master/LICENSE)
### Citation Information
```
@dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
}
@inproceedings{CaneteCFP2020,
title={Spanish Pre-Trained BERT Model and Evaluation Data},
author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
booktitle={PML4DC at ICLR 2020},
year={2020}
}
``` |
kejian/codesearchnet-python-rebalanced | 2022-09-21T06:27:05.000Z | [
"region:us"
] | kejian | null | null | null | 1 | 8 | Entry not found |
truongpdd/vietnamese_poetry | 2022-09-23T04:30:49.000Z | [
"region:us"
] | truongpdd | null | null | null | 1 | 8 | Entry not found |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.