datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
Atharva07/hc3_finance
|
Atharva07
| 2023-10-30T14:15:03Z | 32 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-10-30T13:52:45Z |
---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: human_answers
dtype: string
- name: chatgpt_answers
sequence: string
- name: source
dtype: string
- name: embeddings
sequence: float32
- name: label
dtype: int64
splits:
- name: train
num_bytes: 12514923
num_examples: 3104
- name: validation
num_bytes: 1655672
num_examples: 414
- name: test
num_bytes: 1696431
num_examples: 415
download_size: 13908983
dataset_size: 15867026
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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ShrinivasSK/en_hi_1
|
ShrinivasSK
| 2023-11-04T19:22:36Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:11:45Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 6435127.8
num_examples: 18000
- name: test
num_bytes: 715014.2
num_examples: 2000
download_size: 3824291
dataset_size: 7150142.0
---
# Dataset Card for "data_hi_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_hi_2
|
ShrinivasSK
| 2023-11-04T19:22:44Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:22:37Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 6356499.3
num_examples: 18000
- name: test
num_bytes: 706277.7
num_examples: 2000
download_size: 3784127
dataset_size: 7062777.0
---
# Dataset Card for "data_hi_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_kn_1
|
ShrinivasSK
| 2023-11-04T19:22:57Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:22:51Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4017735.9
num_examples: 18000
- name: test
num_bytes: 446415.1
num_examples: 2000
download_size: 2392888
dataset_size: 4464151.0
---
# Dataset Card for "data_kn_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_kn_2
|
ShrinivasSK
| 2023-11-04T19:23:04Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:22:57Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4039020.0
num_examples: 18000
- name: test
num_bytes: 448780.0
num_examples: 2000
download_size: 2402763
dataset_size: 4487800.0
---
# Dataset Card for "data_kn_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ShrinivasSK/en_kn_3
|
ShrinivasSK
| 2023-11-04T19:23:10Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:23:05Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 3976936.2
num_examples: 18000
- name: test
num_bytes: 441881.8
num_examples: 2000
download_size: 2363947
dataset_size: 4418818.0
---
# Dataset Card for "data_kn_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_mr_1
|
ShrinivasSK
| 2023-11-04T19:23:18Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:23:11Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4557684.6
num_examples: 18000
- name: test
num_bytes: 506409.4
num_examples: 2000
download_size: 2671507
dataset_size: 5064094.0
---
# Dataset Card for "data_mr_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_te_1
|
ShrinivasSK
| 2023-11-04T19:23:24Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:23:19Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4048999.2
num_examples: 18000
- name: test
num_bytes: 449888.8
num_examples: 2000
download_size: 2422001
dataset_size: 4498888.0
---
# Dataset Card for "data_te_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/en_te_2
|
ShrinivasSK
| 2023-11-04T19:23:31Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:23:25Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 4108551.3
num_examples: 18000
- name: test
num_bytes: 456505.7
num_examples: 2000
download_size: 2453776
dataset_size: 4565057.0
---
# Dataset Card for "data_te_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/hi_kn
|
ShrinivasSK
| 2023-11-04T19:28:20Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:28:12Z |
---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 5132190.6
num_examples: 18000
- name: test
num_bytes: 570243.4
num_examples: 2000
download_size: 2596240
dataset_size: 5702434.0
---
# Dataset Card for "hi-kn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ShrinivasSK/hi_te
|
ShrinivasSK
| 2023-11-04T19:28:34Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-04T19:28:29Z |
---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 5294133.0
num_examples: 18000
- name: test
num_bytes: 588237.0
num_examples: 2000
download_size: 2685371
dataset_size: 5882370.0
---
# Dataset Card for "hi-te"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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autumnjohnson/project_ceti
|
autumnjohnson
| 2023-11-08T04:32:12Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-05T22:29:20Z |
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 29115.75
num_examples: 7058
download_size: 0
dataset_size: 29115.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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ShengLiang-HH/sentences_es
|
ShengLiang-HH
| 2023-11-23T10:44:04Z | 32 | 0 |
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:es",
"region:us"
] |
[
"text-classification"
] | 2023-11-06T16:59:28Z |
---
task_categories:
- text-classification
language:
- es
size_categories:
- 100K<n<1M
---
|
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ShrinivasSK/hi_en_2
|
ShrinivasSK
| 2023-11-06T19:07:04Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:29Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 6376404.6
num_examples: 18000
- name: test
num_bytes: 708489.4
num_examples: 2000
download_size: 3796444
dataset_size: 7084894.0
---
# Dataset Card for "hi_en_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/hi_en_3
|
ShrinivasSK
| 2023-11-06T19:07:15Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:34Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 6366803.4
num_examples: 18000
- name: test
num_bytes: 707422.6
num_examples: 2000
download_size: 3789240
dataset_size: 7074226.0
---
# Dataset Card for "hi_en_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ShrinivasSK/kn_en_2
|
ShrinivasSK
| 2023-11-06T19:07:36Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:43Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 3982082.4
num_examples: 18000
- name: test
num_bytes: 442453.6
num_examples: 2000
download_size: 2369798
dataset_size: 4424536.0
---
# Dataset Card for "kn_en_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/kn_en_3
|
ShrinivasSK
| 2023-11-06T19:07:47Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:47Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 3978395.1
num_examples: 18000
- name: test
num_bytes: 442043.9
num_examples: 2000
download_size: 2367278
dataset_size: 4420439.0
---
# Dataset Card for "kn_en_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/te_en_1
|
ShrinivasSK
| 2023-11-06T19:08:08Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:54:57Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 4096206.9
num_examples: 18000
- name: test
num_bytes: 455134.1
num_examples: 2000
download_size: 2442401
dataset_size: 4551341.0
---
# Dataset Card for "te_en_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/te_en_2
|
ShrinivasSK
| 2023-11-06T19:08:19Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:55:01Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 4065421.5
num_examples: 18000
- name: test
num_bytes: 451713.5
num_examples: 2000
download_size: 2431811
dataset_size: 4517135.0
---
# Dataset Card for "te_en_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/te_en_3
|
ShrinivasSK
| 2023-11-06T19:12:56Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T18:55:06Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 4067874.0
num_examples: 18000
- name: test
num_bytes: 451986.0
num_examples: 2000
download_size: 2432870
dataset_size: 4519860.0
---
# Dataset Card for "te_en_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ShrinivasSK/hi_kn_1
|
ShrinivasSK
| 2023-11-06T19:22:17Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-06T19:22:07Z |
---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 5155860.6
num_examples: 18000
- name: test
num_bytes: 572873.4
num_examples: 2000
download_size: 2612672
dataset_size: 5728734.0
---
# Dataset Card for "hi_kn_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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hippocrates/CitationGPTv2_test
|
hippocrates
| 2023-11-10T17:06:54Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-07T19:12:27Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 186018625
num_examples: 99360
- name: valid
num_bytes: 24082667
num_examples: 12760
- name: test
num_bytes: 21458598
num_examples: 11615
download_size: 8627917
dataset_size: 231559890
---
# Dataset Card for "CitationGPTv2_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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tomaarsen/setfit-absa-semeval-restaurants
|
tomaarsen
| 2023-11-16T10:37:20Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-09T15:14:48Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: span
dtype: string
- name: label
dtype: string
- name: ordinal
dtype: int64
splits:
- name: train
num_bytes: 490223
num_examples: 3693
- name: test
num_bytes: 138187
num_examples: 1134
download_size: 193352
dataset_size: 628410
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "tomaarsen/setfit-absa-semeval-restaurants"
### Dataset Summary
This dataset contains the manually annotated restaurant reviews from SemEval-2014 Task 4, in the format as
understood by [SetFit](https://github.com/huggingface/setfit) ABSA.
For more details, see https://aclanthology.org/S14-2004/
### Data Instances
An example of "train" looks as follows.
```json
{"text": "But the staff was so horrible to us.", "span": "staff", "label": "negative", "ordinal": 0}
{"text": "To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora.", "span": "food", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "food", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "kitchen", "label": "positive", "ordinal": 0}
{"text": "The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not.", "span": "menu", "label": "neutral", "ordinal": 0}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
- `span`: a `string` feature showing the aspect span from the text.
- `label`: a `string` feature showing the polarity of the aspect span.
- `ordinal`: an `int64` feature showing the n-th occurrence of the span in the text. This is useful for if the span occurs within the same text multiple times.
### Data Splits
| name |train|test|
|---------|----:|---:|
|tomaarsen/setfit-absa-semeval-restaurants|3693|1134|
### Training ABSA models using SetFit ABSA
To train using this dataset, first install the SetFit library:
```bash
pip install setfit
```
And then you can use the following script as a guideline of how to train an ABSA model on this dataset:
```python
from setfit import AbsaModel, AbsaTrainer, TrainingArguments
from datasets import load_dataset
from transformers import EarlyStoppingCallback
# You can initialize a AbsaModel using one or two SentenceTransformer models, or two ABSA models
model = AbsaModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
# The training/eval dataset must have `text`, `span`, `polarity`, and `ordinal` columns
dataset = load_dataset("tomaarsen/setfit-absa-semeval-restaurants")
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
args = TrainingArguments(
output_dir="models",
use_amp=True,
batch_size=256,
eval_steps=50,
save_steps=50,
load_best_model_at_end=True,
)
trainer = AbsaTrainer(
model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[EarlyStoppingCallback(early_stopping_patience=5)],
)
trainer.train()
metrics = trainer.evaluate(eval_dataset)
print(metrics)
trainer.push_to_hub("tomaarsen/setfit-absa-restaurants")
```
You can then run inference like so:
```python
from setfit import AbsaModel
# Download from Hub and run inference
model = AbsaModel.from_pretrained(
"tomaarsen/setfit-absa-restaurants-aspect",
"tomaarsen/setfit-absa-restaurants-polarity",
)
# Run inference
preds = model([
"The best pizza outside of Italy and really tasty.",
"The food here is great but the service is terrible",
])
```
### Citation Information
```bibtex
@inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
editor = "Nakov, Preslav and
Zesch, Torsten",
booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S14-2004",
doi = "10.3115/v1/S14-2004",
pages = "27--35",
}
```
|
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hippocrates/clinical_ner_train
|
hippocrates
| 2023-11-13T19:52:52Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-13T19:52:44Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8527346
num_examples: 10661
- name: valid
num_bytes: 4951299
num_examples: 6254
- name: test
num_bytes: 5307591
num_examples: 6806
download_size: 5455050
dataset_size: 18786236
---
# Dataset Card for "clinical_ner_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Patt/HellaSwag_thai
|
Patt
| 2023-11-16T16:34:13Z | 32 | 0 |
[
"language:th",
"language:en",
"arxiv:1907.04307",
"region:us"
] | null | 2023-11-16T16:29:16Z |
---
language:
- th
- en
---
# Dataset Card for HellaSwag_TH_drop
### Dataset Description
This dataset is Thai translated version of [hellaswag](https://huggingface.co/datasets/hellaswag) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
### Languages
- EN
- TH
|
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kopyl/833-icons-dataset-1024-blip-large
|
kopyl
| 2023-11-17T19:35:17Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-17T19:34:14Z |
---
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: 21063249.0
num_examples: 833
download_size: 19766635
dataset_size: 21063249.0
---
# Dataset Card for "833-icons-dataset-1024-blip-large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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bdsaglam/web_nlg-erx-instruction-alpaca
|
bdsaglam
| 2023-12-03T17:28:57Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-18T16:49:48Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 22746351
num_examples: 35426
- name: dev
num_bytes: 2869862
num_examples: 4464
download_size: 2781916
dataset_size: 25616213
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
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bdsaglam/web_nlg-erx-instruction-llama2chat-debug
|
bdsaglam
| 2023-11-19T09:23:43Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-19T09:23:42Z |
---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 256432
num_examples: 354
- name: dev
num_bytes: 32118
num_examples: 44
download_size: 78888
dataset_size: 288550
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
|
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] |
PKU-Alignment/PKU-SafeRLHF-30K
|
PKU-Alignment
| 2023-11-20T10:23:37Z | 32 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"llm",
"lm",
"human-feedback",
"rlhf",
"safe-rlhf",
"arxiv:2307.04657",
"region:us"
] |
[
"text-generation"
] | 2023-11-20T10:20:10Z |
---
license: cc-by-nc-4.0
task_categories:
- text-generation
language:
- en
tags:
- safe
- safety
- ai-safety
- llm
- lm
- human-feedback
- rlhf
- safe-rlhf
size_categories:
- 10K<n<100K
---
# Dataset Card for PKU-SafeRLHF
<span style="color: red;">Warning: this dataset contains data that may be offensive or harmful. The data are intended for research purposes, especially research that can make models less harmful. The views expressed in the data do not reflect the views of PKU-Alignment Team or any of its members. </span>
## Dataset Summary
The preference dataset consists of 30k+ expert comparison data. Each entry in this dataset includes two responses to a question, along with safety meta-labels and preferences for both responses, taking into consideration their helpfulness and harmlessness.
The annotation pipeline for this dataset is depicted in the following image:
## Human-Preference on Harmlessness and Helpfulness
### Harmlessness of a QA Pair
The harmlessness of a QA pair is evaluated based on ethical considerations and safety implications inherent in the interaction.
A QA pair is deemed harmless when it is classified as risk-neutral across all 14 harm categories outlined in our paper.
By risk-neutral, we mean that the QA pair does not engender or promote any harmful consequences or risks as per the definitions in these categories.
Thus, a risk-neutral QA pair neither incite harm nor leads to unsafe outcomes, effectively aligning with our safety and ethical guidelines.
### Helpfulness of a Response
The helpfulness of a response pertains to how effectively it addresses a given prompt. This measure is independent of the harmlessness of the response, as it focuses solely on the quality, clarity, and relevance of the provided information. Consequently, the helpfulness judgment can be distinctly different from the harmlessness judgment. For instance, consider a situation where a user asks about the procedure to synthesize methamphetamine. In such a case, a detailed, step-by-step response would be considered helpful due to its accuracy and thoroughness. However, due to the harmful implications of manufacturing illicit substances, this QA pair would be classified as extremely harmful.
### Ranking of Responses
Once the helpfulness and harmlessness of responses are evaluated, they are ranked accordingly. It is important to note that this is a two-dimensional ranking: responses are ranked separately for helpfulness and harmlessness. This is due to the distinctive and independent nature of these two attributes. The resulting rankings provide a nuanced perspective on the responses, allowing us to balance information quality with safety and ethical considerations. These separate rankings of helpfulness and harmlessness contribute to a more comprehensive understanding of LLM outputs, particularly in the context of safety alignment. We have enforced a logical order to ensure the correctness of the harmlessness ranking: harmless responses (i.e. all 14 harm categories risk-neutral) are always ranked higher than harmful ones (i.e., at least 1 category risky).
## Usage
To load our dataset, use the `load_dataset()` function as follows:
```python
from datasets import load_dataset
dataset = load_dataset("PKU-Alignment/PKU-SafeRLHF-30K")
```
## Paper
You can find more information in our paper
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails.
|
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] |
GotItAI/rocketbook_gpt4_responses_combined
|
GotItAI
| 2023-11-21T09:46:10Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-21T07:47:02Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt_id
dtype: int64
splits:
- name: train
num_bytes: 16879635
num_examples: 1459
- name: test
num_bytes: 6182180
num_examples: 530
download_size: 0
dataset_size: 23061815
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "rocketbook_gpt4_responses_combined"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Giacinta/label
|
Giacinta
| 2023-11-24T05:57:25Z | 32 | 0 |
[
"task_categories:text-classification",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2023-11-22T16:26:40Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
pretty_name: ai
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: filter.csv
---
|
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openerotica/erotica-analysis
|
openerotica
| 2023-11-26T04:24:54Z | 32 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-23T18:47:00Z |
---
license: apache-2.0
---
This dataset is roughly 27k examples of erotica stories which I've fed through GPT-3.5-turbo-16k to obtain a summary, writing prompt, and tags as a response. I've filtered out all the refusals, and deleted a fair ammount of "GPT-isms". I'd still like to go through this again to prune any remaining low quality responses I've missed, but I think this is a good start. Most of the context size comes from the stories themselves, not the responses.
Please consider supporting my Patreon (https://www.patreon.com/openerotica). I'm only asking for about tree fiddy and it all goes toward helping me create more models and datasets.
|
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] |
KrafterDen/SISUS_V1
|
KrafterDen
| 2023-11-25T19:05:22Z | 32 | 0 |
[
"license:llama2",
"region:us"
] | null | 2023-11-24T12:59:38Z |
---
license: llama2
---
|
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] |
nlewins/cebuano-dictionary-words
|
nlewins
| 2023-11-24T13:38:08Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-24T13:38:07Z |
---
dataset_info:
features:
- name: ceb
dtype: string
- name: en
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 472229
num_examples: 15334
download_size: 247755
dataset_size: 472229
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cebuano-dictionary-words"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
DianaJin/miniproject_train
|
DianaJin
| 2023-11-28T12:18:55Z | 32 | 0 |
[
"region:us"
] | null | 2023-11-28T05:16:36Z |
---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 26901736
num_examples: 28
download_size: 14755214
dataset_size: 26901736
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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benwoodyear/guardian_crosswords
|
benwoodyear
| 2022-04-02T11:41:59Z | 31 | 0 |
[
"license:afl-3.0",
"region:us"
] | null | 2022-03-31T12:19:26Z |
---
license: afl-3.0
---
|
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hackathon-pln-es/spanish-to-quechua
|
hackathon-pln-es
| 2022-10-25T10:03:46Z | 31 | 6 |
[
"task_categories:translation",
"language:es",
"language:qu",
"region:us"
] |
[
"translation"
] | 2022-04-03T04:02:58Z |
---
language:
- es
- qu
task_categories:
- translation
task:
- translation
---
# Spanish to Quechua
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [team members](#team-members)
## Dataset Description
This dataset is a recopilation of webs and others datasets that shows in [dataset creation section](#dataset-creation). This contains translations from spanish (es) to Qechua of Ayacucho (qu).
## Dataset Structure
### Data Fields
- es: The sentence in Spanish.
- qu: The sentence in Quechua of Ayacucho.
### Data Splits
- train: To train the model (102 747 sentences).
- Validation: To validate the model during training (12 844 sentences).
- test: To evaluate the model when the training is finished (12 843 sentences).
## Dataset Creation
### Source Data
This dataset has generated from:
- "Mundo Quechua" by "Ivan Acuña" - [available here](https://mundoquechua.blogspot.com/2006/07/frases-comunes-en-quechua.html)
- "Kuyakuykim (Te quiero): Apps con las que podrías aprender quechua" by "El comercio" - [available here](https://elcomercio.pe/tecnologia/actualidad/traductor-frases-romanticas-quechua-noticia-467022-noticia/)
- "Piropos y frases de amor en quechua" by "Soy Quechua" - [available here](https://www.soyquechua.org/2019/12/palabras-en-quechua-de-amor.html)
- "Corazón en quechua" by "Soy Quechua" - [available here](https://www.soyquechua.org/2020/05/corazon-en-quechua.html)
- "Oraciones en Español traducidas a Quechua" by "Tatoeba" - [available here](https://tatoeba.org/es/sentences/search?from=spa&query=&to=que)
- "AmericasNLP 2021 Shared Task on Open Machine Translation" by "americasnlp2021" - [available here](https://github.com/AmericasNLP/americasnlp2021/tree/main/data/quechua-spanish/parallel_data/es-quy)
### Data cleaning
- The dataset was manually cleaned during compilation, as some words of one language were related to several words of the other language.
## Considerations for Using the Data
This is a first version of the dataset, we expected improve it over time and especially to neutralize the biblical themes.
## Team members
- [Sara Benel](https://huggingface.co/sbenel)
- [Jose Vílchez](https://huggingface.co/JCarlos)
|
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] |
StanBienaives/french-open-fiscal-texts
|
StanBienaives
| 2022-10-25T10:03:56Z | 31 | 0 |
[
"task_categories:summarization",
"task_categories:feature-extraction",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc0-1.0",
"region:us"
] |
[
"summarization",
"feature-extraction"
] | 2022-04-06T11:42:06Z |
---
annotations_creators:
- no-annotation
language_creators:
- other
language:
- fr-FR
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: french-open-fiscal-texts
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
- feature-extraction
task_ids: []
---
# Dataset Card for french-open-fiscal-texts
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://echanges.dila.gouv.fr/OPENDATA/JADE/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court "Conseil d'Etat".
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
fr-FR
## Dataset Structure
### Data Instances
```json
{
"file": "CETATEXT000007584427.xml",
"title": "Cour administrative d'appel de Marseille, 3�me chambre - formation � 3, du 21 octobre 2004, 00MA01080, in�dit au recueil Lebon",
"summary": "",
"content": "Vu la requête, enregistrée le 22 mai 2000, présentée pour M. Roger X, par Me Luherne, élisant domicile ...), et les mémoires complémentaires en date des 28 octobre 2002, 22 mars 2004 et 16 septembre 2004 ; M. X demande à la Cour :\n\n\n \n 11/ d'annuler le jugement n° 951520 en date du 16 mars 2000 par lequel le Tribunal administratif de Montpellier a rejeté sa requête tendant à la réduction des cotisations supplémentaires à l'impôt sur le revenu et des pénalités dont elles ont été assorties, auxquelles il a été assujetti au titre des années 1990, 1991 et 1992 ;\n\n\n \n 22/ de prononcer la réduction desdites cotisations ;\n\n\n \n 3°/ de condamner de l'Etat à lui verser une somme de 32.278 francs soit 4.920,75 euros"
}
```
### Data Fields
`file`: identifier on the JADE OPENDATA file
`title`: Name of the law case
`summary`: Summary provided by JADE (may be missing)
`content`: Text content of the case law
### Data Splits
train
test
## Dataset Creation
### Curation Rationale
This dataset is an attempt to gather multiple tax related french text law.
The first intent it to build model to summarize law cases
### Source Data
#### Initial Data Collection and Normalization
Collected from the https://echanges.dila.gouv.fr/OPENDATA/
- Filtering xml files containing "Code général des impôts" (tax related)
- Extracting content, summary, identifier, title
#### Who are the source language producers?
DILA
### 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]
|
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Aniemore/resd
|
Aniemore
| 2023-06-10T22:15:40Z | 31 | 3 |
[
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:mit",
"doi:10.57967/hf/1273",
"region:us"
] |
[
"audio-classification"
] | 2022-05-23T22:57:03Z |
---
license:
- mit
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- crowdsourced
language:
- ru
multilinguality:
- monolingual
pretty_name: Russian Emotional Speech Dialogs
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
dataset_info:
features:
- name: name
dtype: string
- name: path
dtype: string
- name: emotion
dtype: string
- name: speech
dtype: audio
splits:
- name: test
num_bytes: 96603538.0
num_examples: 280
- name: train
num_bytes: 398719157.336
num_examples: 1116
download_size: 485403675
dataset_size: 495322695.336
---
# Dataset Card for resd
## 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://huggingface.co/datasets/Aniemore/resd**
- **Repository: https://github.com/aniemore/Aniemore**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Russian dataset of emotional speech dialogues. This dataset was assembled from ~3.5 hours of live speech by actors who voiced pre-distributed emotions in the dialogue for ~3 minutes each.
### 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
This dataset was created by Artem Amentes, Nikita Davidchuk and Ilya Lubenets
### Citation Information
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
email = {[email protected]}
}
```
### Contributions
Thanks to [@Ar4ikov](https://github.com/Ar4ikov) for adding this dataset.
|
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BeIR/scifact-generated-queries
|
BeIR
| 2022-10-23T06:12:34Z | 31 | 0 |
[
"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"
] |
[
"text-retrieval",
"zero-shot-retrieval",
"information-retrieval",
"zero-shot-information-retrieval"
] | 2022-06-17T12:52:14Z |
---
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:** [email protected]
### 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.
|
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projecte-aina/ca_zh_wikipedia
|
projecte-aina
| 2023-01-09T07:56:07Z | 31 | 3 |
[
"task_categories:translation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ca",
"language:zh",
"language:multilingual",
"license:cc-by-4.0",
"region:us"
] |
[
"translation"
] | 2022-06-27T09:03:00Z |
---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- ca
- zh
- multilingual
license:
- cc-by-4.0
multilinguality:
- translation
pretty_name: CA-ZH Wikipedia Parallel Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
---
# Dataset Card for CA-ZH Wikipedia datasets
## 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:** [[email protected]]([email protected])
### Dataset Summary
The CA-ZH Parallel Corpus is a Catalan-Chinese dataset of mutual translations automatically crawled from Wikipedia. Two separate corpora are included, namely CA-ZH 1.05 Wikipedia and CA-ZH 1.10 Wikipedia, the latter has better general quality than the former. The dataset was created to support Catalan NLP tasks, e.g., Machine Translation.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for Multilingual Machine Translation. Success on this task is typically measured by achieving a high BLEU score. The dataset can be used to finetune a large-scale multilingual MT system such as m2m-100.
### Languages
The texts in the dataset are in Catalan and Chinese.
## Dataset Structure
### Data Instances
A typical data point comprises a pair of translations in Catalan and Chinese. An example from the Ca-Zh Parallel Corpus looks as follows:
```
{ "ca": "1591è Batalló Separat d'Artilleria autorpopulsada", "zh": "第1591自走砲营" }
```
### Data Fields
- "ca": Text in Catalan.
- "zh": Text in Chinese.
### Data Splits
The dataset contains a single split: `train`.
## Dataset Creation
### Curation Rationale
The Ca-Zh Parallel Corpus was built to provide more language data for MT tasks dedicated to low-resource languages. The dataset was built by gathering texts on the same topic in Catalan and Chinese from Wikipedia.
### Source Data
#### Initial Data Collection and Normalization
The data was obtained by automatic crawling, a quality filter was applied to improve the data quality. The original Chinese data was mixed into Traditional Chinese and Simplified Chinese, a simplification process was conducted in order to guarantee the unification.
#### Who are the source language producers?
All the texts in this dataset come from the Wikipedia.
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No anonymisation process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop Machines Translation tasks for low-resource languages such as Catalan.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages and non-curated texts, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
Wikipedia provides data of a more general domain. Application of this dataset in more specific domains such as biomedical, legal etc. would be of limited use.
## Additional Information
### Dataset Curators
Carlos Escolano, Chenuye Zhou and Zixuan Liu, Barcelona Supercomputing Center (cescolano3 at gmail dot com)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
[Creative Commons Attribution Share Alike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@mastersthesis{MasterThesisChenuyeZhou,
author = "Chenuye Zhou",
title = "Building a Catalan-Chinese parallel corpus for use in MT",
school = "Universitat Pompeu Fabra",
year = 2022,
address = "Barcelona",
url = "https://repositori.upf.edu/handle/10230/54140"
}
@mastersthesis{MasterThesisZixuanLiu,
author = "Zixuan Liu",
title = "Improving Chinese-Catalan Machine Translation with Wikipedia Parallel",
school = "Universitat Pompeu Fabra",
year = 2022,
address = "Barcelona",
url= "https://repositori.upf.edu/handle/10230/54142"
}
```
|
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] |
ThierryZhou/test
|
ThierryZhou
| 2022-08-22T02:13:10Z | 31 | 0 |
[
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"source_datasets:original",
"language:en",
"arxiv:2111.11431",
"region:us"
] |
[
"image-to-text"
] | 2022-06-29T02:31:45Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: test
pretty_name: Test
---
# Dataset Card for "test"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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:** [RedCaps homepage](https://redcaps.xyz/)
- **Repository:** [RedCaps repository](https://github.com/redcaps-dataset/redcaps-downloader)
- **Paper:** [RedCaps: web-curated image-text data created by the people, for the people](https://arxiv.org/abs/2111.11431)
- **Leaderboard:**
- **Point of Contact:** [Karan Desai](mailto:[email protected])
### Dataset Summary
### Dataset Preprocessing
|
[
-0.7132156491279602,
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nateraw/pizza_not_pizza
|
nateraw
| 2022-07-07T19:58:03Z | 31 | 1 |
[
"license:other",
"region:us"
] | null | 2022-07-07T19:57:37Z |
---
license:
- other
kaggle_id: carlosrunner/pizza-not-pizza
---
# Dataset Card for Pizza or Not Pizza?
## 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://kaggle.com/datasets/carlosrunner/pizza-not-pizza
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Who doesn't like pizza? This dataset contains about 1000 images of pizza and 1000 images of dishes other than pizza. It can be used for a simple binary image classification task.
All images were rescaled to have a maximum side length of 512 pixels.
This is a subset of the Food-101 dataset. Information about the original dataset can be found in the following paper:
Bossard, Lukas, Matthieu Guillaumin, and Luc Van Gool. "Food-101 – Mining Discriminative Components with Random Forests." In *European conference on computer vision*, pp. 446-461. Springer, Cham, 2014.
The original dataset can be found in the following locations:
https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/
https://www.kaggle.com/datasets/dansbecker/food-101
https://paperswithcode.com/dataset/food-101
https://www.tensorflow.org/datasets/catalog/food101
Number of instances in each class:
Pizza: 983
Not Pizza: 983
##Acknowledgements
The Food-101 data set consists of images from Foodspotting [1] which are not property of the Federal Institute of Technology Zurich (ETHZ). Any use beyond scientific fair use must be negociated with the respective picture owners according to the Foodspotting terms of use [2].
[1] http://www.foodspotting.com/
[2] http://www.foodspotting.com/terms/
### 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
This dataset was shared by [@carlosrunner](https://kaggle.com/carlosrunner)
### Licensing Information
The license for this dataset is other
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed]
|
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Muennighoff/xstory_cloze
|
Muennighoff
| 2022-10-20T19:44:18Z | 31 | 0 |
[
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"language:es",
"language:eu",
"language:hi",
"language:id",
"language:zh",
"language:ru",
"language:my",
"license:unknown",
"other-story-completion",
"region:us"
] | null | 2022-07-22T11:52:19Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- es
- eu
- hi
- id
- zh
- ru
- my
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_ids: []
tags:
- other-story-completion
---
# Dataset Card for "story_cloze"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story.
### Data Instances
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
| name |validation |test|
|-------|-----:|---:|
|lang|1871|1871|
|
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biglam/contentious_contexts
|
biglam
| 2022-08-01T17:02:11Z | 31 | 2 |
[
"task_categories:text-classification",
"task_ids:sentiment-scoring",
"task_ids:multi-label-classification",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:nl",
"license:cc-by-2.0",
"newspapers",
"historic",
"dutch",
"problematic",
"ConConCor",
"region:us"
] |
[
"text-classification"
] | 2022-07-26T22:07:48Z |
---
annotations_creators:
- expert-generated
- crowdsourced
language:
- nl
language_creators:
- machine-generated
license:
- cc-by-2.0
multilinguality:
- monolingual
pretty_name: Contentious Contexts Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- newspapers
- historic
- dutch
- problematic
- ConConCor
task_categories:
- text-classification
task_ids:
- sentiment-scoring
- multi-label-classification
---
# Dataset Card for Contentious Contexts 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:** [ConConCor](https://github.com/cultural-ai/ConConCor)
- **Repository:** [ConConCor](https://github.com/cultural-ai/ConConCor)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Jacco van Ossenbruggen](https://github.com/jrvosse)
**Note** One can also find a Datasheet produced by the creators of this dataset as a [PDF document](https://github.com/cultural-ai/ConConCor/blob/main/Dataset/DataSheet.pdf)
### Dataset Summary
This dataset contains extracts from historical Dutch newspapers containing keywords of potentially contentious words (according to present-day sensibilities). The dataset contains multiple annotations per instance, given the option to quantify agreement scores for annotations. This dataset can be used to track how words and their meanings have changed over time
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used for tracking how the meanings of words in different contexts have changed and become contentious over time
### Languages
The text in the dataset is in Dutch. The responses are available in both English and Dutch. Suggestions, where present, are only in Dutch. The associated BCP-47 code is `nl`
## Dataset Structure
### Data Instances
```
{
'extract_id': 'H97',
'text': 'en waardoor het eerste doel wordt voorbijgestreefd om voor den 5D5c5Y 5d-5@5j5g5d5e5Z5V5V5c een speciale eigen werkingssfeer te
scheppen.Intusschen is het',
'target': '5D 5c5Y5d-5@5j5g5d5e5Z5V5V5c',
'annotator_responses_english': [
{'id': 'unknown_2a', 'response': 'Not contentious'},
{'id': 'unknown_2b', 'response': 'Contentious according to current standards'},
{'id': 'unknown_2c', 'response': "I don't know"},
{'id': 'unknown_2d', 'response': 'Contentious according to current standards'},
{'id': 'unknown_2e', 'response': 'Not contentious'},
{'id': 'unknown_2f', 'response': "I don't know"},
{'id': 'unknown_2g', 'response': 'Not contentious'}],
'annotator_responses_dutch': [
{'id': 'unknown_2a', 'response': 'Niet omstreden'},
{'id': 'unknown_2b', 'response': 'Omstreden naar huidige maatstaven'},
{'id': 'unknown_2c', 'response': 'Weet ik niet'},
{'id': 'unknown_2d', 'response': 'Omstreden naar huidige maatstaven'},
{'id': 'unknown_2e', 'response': 'Niet omstreden'},
{'id': 'unknown_2f', 'response': 'Weet ik niet'},
{'id': 'unknown_2g', 'response': 'Niet omstreden'}],
'annotator_suggestions': [
{'id': 'unknown_2a', 'suggestion': ''},
{'id': 'unknown_2b', 'suggestion': 'ander ras nodig'},
{'id': 'unknown_2c', 'suggestion': 'personen van ander ras'},
{'id': 'unknown_2d', 'suggestion': ''},
{'id': 'unknown_2e', 'suggestion': ''},
{'id': 'unknown_2f', 'suggestion': ''},
{'id': 'unknown_2g', 'suggestion': 'ras'}]
}
```
### Data Fields
|extract_id|text|target|annotator_responses_english|annotator_responses_dutch|annotator_suggestions|
|---|---|---|---|---|---|
|Unique identifier|Text|Target phrase or word|Response(translated to English)|Response in Dutch|Suggestions, if present|
### Data Splits
Train: 2720
## Dataset Creation
### Curation Rationale
> Cultural heritage institutions recognise the problem of language use in their collections. The cultural objects in archives, libraries, and museums contain words and phrases that are inappropriate in modern society but were used broadly back in times. Such words can be offensive and discriminative. In our work, we use the term "contentious" to refer to all (potentially) inappropriate or otherwise sensitive words. For example, words suggestive of some (implicit or explicit) bias towards or against something. The National Archives of the Netherlands stated that they "explore the possibility of explaining language that was acceptable and common in the past and providing it with contemporary alternatives", meanwhile "keeping the original descriptions [with contentious words], because they give an idea of the time in which they were made or included in the collection". There is a page on the institution website where people can report "offensive language".
### Source Data
#### Initial Data Collection and Normalization
> The queries were run on OCR'd versions of the Europeana Newspaper collection, as provided by the KB National Library of the Netherlands. We limited our pool to text categorised as "article", thus excluding other types of texts such as advertisements and family notices. We then only focused our sample on the 6 decades between 1890-01-01 and 1941-12-31, as this is the period available in the Europeana newspaper corpus. The dataset represents a stratified sample set over target word, decade, and newspaper issue distribution metadata. For the final set of extracts for annotation, we gave extracts sampling weights proportional to their actual probabilities, as estimated from the initial set of extracts via trigram frequencies, rather than sampling uniformly.
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
> The annotation process included 3 stages: pilot annotation, expert annotation, and crowdsourced annotation on the "Prolific" platform. All stages required the participation of Dutch speakers. The pilot stage was intended for testing the annotation layout, the instructions clarity, the number of sentences provided as context, the survey questions, and the difficulty of the task in general. The Dutch-speaking members of the Cultural AI Lab were asked to test the annotation process and give their feedback anonymously using Google Sheets. Six volunteers contributed to the pilot stage, each annotating the same 40 samples where either a context of 3 or 5 sentences surrounding the term were given. An individual annotation sheet had a table layout with 4 options to choose for every sample
> - 'Omstreden'(Contentious)
> - 'Niet omstreden'(Not contentious)
> - 'Weet ik niet'(I don't know)
> - 'Onleesbare OCR'(Illegible OCR)</br>
2 open fields
> - 'Andere omstreden termen in de context'(Other contentious terms in the context)
> - 'Notities'(Notes)</br>
and the instructions in the header. The rows were the samples with the highlighted words, the tickboxes for every option, and 2 empty cells for the open questions. The obligatory part of the annotation was to select one of the 4 options for every sample. Finding other contentious terms in the given sample, leaving notes, and answering 4 additional open questions at the end of the task were optional. Based on the received feedback and the answers to the open questions in the pilot study, the following decisions were made regarding the next, experts' annotation stage:
> - The annotation layout was built in Google Forms as a questionnaire instead of the table layout in Google Sheets to make the data collection and analysis faster as the number of participants would increase;
> - The context window of 5 sentences per sample was found optimal;
> - The number of samples per annotator was increased to 50;
> - The option 'Omstreden' (Contentious) was changed to 'Omstreden naar huidige maatstaven' ('Contentious according to current standards') to clarify that annotators should judge contentiousness of the word's use in context from today's perspective;
> - The annotation instruction was edited to clarify 2 points: (1) that annotators while judging contentiousness should take into account not only a bolded word but also the context surrounding it, and (2) if a word seems even slightly contentious to an annotator, they should choose the option 'Omstreden naar huidige maatstaven' (Contentious according to current standards);
> - The non-required field for every sample 'Notities' (Notes) was removed as there was an open question at the end of the annotation, where participants could leave their comments;
> - Another open question was added at the end of the annotation asking how much time it took to complete the annotation.
#### Who are the annotators?
Volunteers and Expert annotators
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
## Accessing the annotations
Each example text has multiple annotations. These annotations may not always agree. There are various approaches one could take to calculate agreement, including a majority vote, rating some annotators more highly, or calculating a score based on the 'votes' of annotators. Since there are many ways of doing this, we have not implemented this as part of the dataset loading script.
An example of how one could generate an "OCR quality rating" based on the number of times an annotator labelled an example with `Illegible OCR`:
```python
from collections import Counter
def calculate_ocr_score(example):
annotator_responses = [response['response'] for response in example['annotator_responses_english']]
counts = Counter(annotator_responses)
bad_ocr_ratings = counts.get("Illegible OCR")
if bad_ocr_ratings is None:
bad_ocr_ratings = 0
return round(1 - bad_ocr_ratings/len(annotator_responses),3)
dataset = dataset.map(lambda example: {"ocr_score":calculate_ocr_score(example)})
```
To take the majority vote (or return a tie) based on whether a example is labelled contentious or not:
```python
def most_common_vote(example):
annotator_responses = [response['response'] for response in example['annotator_responses_english']]
counts = Counter(annotator_responses)
contentious_count = counts.get("Contentious according to current standards")
if not contentious_count:
contentious_count = 0
not_contentious_count = counts.get("Not contentious")
if not not_contentious_count:
not_contentious_count = 0
if contentious_count > not_contentious_count:
return "contentious"
if contentious_count < not_contentious_count:
return "not_contentious"
if contentious_count == not_contentious_count:
return "tied"
```
### Social Impact of Dataset
This dataset can be used to see how words change in meaning over time
### Discussion of Biases
> Due to the nature of the project, some examples used in this documentation may be shocking or offensive. They are provided only as an illustration or explanation of the resulting dataset and do not reflect the opinions of the project team or their organisations.
Since this project was explicitly created to help assess bias, it should be used primarily in the context of assess bias, and methods for detecting bias.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Cultural AI](https://github.com/cultural-ai)
### Licensing Information
CC-BY
### Citation Information
```
@misc{ContentiousContextsCorpus2021,
author = {Cultural AI},
title = {Contentious Contexts Corpus},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/cultural-ai/ConConCor}},
}
```
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allenai/multinews_sparse_max
|
allenai
| 2022-11-24T21:34:53Z | 31 | 0 |
[
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
] |
[
"summarization"
] | 2022-08-26T21:41:47Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: Multi-News
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: multi-news
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"max"`, i.e. the number of documents retrieved, `k`, is set as the maximum number of documents seen across examples in this dataset, in this case `k==10`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8793 | 0.7460 | 0.2213 | 0.8264 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8748 | 0.7453 | 0.2173 | 0.8232 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.2187 | 0.8250 |
|
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olm/olm-wikipedia-20221001
|
olm
| 2022-10-18T19:18:07Z | 31 | 0 |
[
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"pretraining",
"language modelling",
"wikipedia",
"web",
"region:us"
] |
[] | 2022-10-10T18:06:43Z |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM October 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Dataset Card for OLM October 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from an October 2022 Wikipedia snapshot.
|
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] |
bburns/celeb-identities
|
bburns
| 2022-10-14T15:20:20Z | 31 | 0 |
[
"region:us"
] | null | 2022-10-14T04:21:48Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: Geohot
1: Grimes
2: Kanye
3: PG
4: Riva
5: Trump
splits:
- name: train
num_bytes: 4350264.0
num_examples: 18
download_size: 4342420
dataset_size: 4350264.0
---
# Dataset Card for "celeb-identities"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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lmqg/qag_squad
|
lmqg
| 2022-12-18T07:39:03Z | 31 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:lmqg/qg_squad",
"language:en",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-11-11T14:12:30Z |
---
license: cc-by-sa-4.0
pretty_name: SQuAD for question generation
language: en
multilinguality: monolingual
size_categories: 1k<n<10K
source_datasets: lmqg/qg_squad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_squad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the SQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
English (en)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": "\"4 Minutes\" was released as the album's lead single and peaked at number three on the Billboard Hot 100. It was Madonna's 37th top-ten hit on the chart—it pushed Madonna past Elvis Presley as the artist with the most top-ten hits. In the UK she retained her record for the most number-one singles for a female artist; \"4 Minutes\" becoming her thirteenth. At the 23rd Japan Gold Disc Awards, Madonna received her fifth Artist of the Year trophy from Recording Industry Association of Japan, the most for any artist. To further promote the album, Madonna embarked on the Sticky & Sweet Tour; her first major venture with Live Nation. With a gross of $280 million, it became the highest-grossing tour by a solo artist then, surpassing the previous record Madonna set with the Confessions Tour; it was later surpassed by Roger Waters' The Wall Live. It was extended to the next year, adding new European dates, and after it ended, the total gross was $408 million.",
"questions": [
"Which single was released as the album's lead single?",
"Madonna surpassed which artist with the most top-ten hits?",
"4 minutes became Madonna's which number one single in the UK?",
"What is the name of the first tour with Live Nation?",
"How much did Stick and Sweet Tour grossed?"
],
"answers": [
"4 Minutes",
"Elvis Presley",
"thirteenth",
"Sticky & Sweet Tour",
"$280 million,"
],
"questions_answers": "question: Which single was released as the album's lead single?, answer: 4 Minutes | question: Madonna surpassed which artist with the most top-ten hits?, answer: Elvis Presley | question: 4 minutes became Madonna's which number one single in the UK?, answer: thirteenth | question: What is the name of the first tour with Live Nation?, answer: Sticky & Sweet Tour | question: How much did Stick and Sweet Tour grossed?, answer: $280 million,"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|16462| 2067 | 2429|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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] |
101arrowz/vox_celeb
|
101arrowz
| 2023-08-20T03:04:07Z | 31 | 1 |
[
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:image-classification",
"task_ids:speaker-identification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"license:cc-by-4.0",
"region:us"
] |
[
"automatic-speech-recognition",
"audio-classification",
"image-classification"
] | 2022-11-13T01:43:46Z |
---
annotations_creators:
- crowdsourced
language: []
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: VoxCeleb
size_categories:
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
source_datasets: []
tags: []
task_categories:
- automatic-speech-recognition
- audio-classification
- image-classification
task_ids:
- speaker-identification
---
# Dataset Card for VoxCeleb
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
VoxCeleb is an audio-visual dataset consisting of short clips of human speech, extracted from interview videos uploaded to YouTube.
NOTE: Although this dataset can be automatically downloaded, you must manually request credentials to access it from the creators' website.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each datapoint has a path to the audio/video clip along with metadata about the speaker.
```
{
'file': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'file_format': 'wav',
'dataset_id': 'vox1',
'speaker_id': 'id10271',
'speaker_gender': 'm',
'speaker_name': 'Ed_Westwick',
'speaker_nationality': 'UK',
'video_id': '_YimahVgI1A',
'clip_id': '00003',
'audio': {
'path': '/datasets/downloads/extracted/[hash]/wav/id10271/_YimahVgI1A/00003.wav',
'array': array([...], dtype=float32),
'sampling_rate': 16000
}
}
```
### Data Fields
Each row includes the following fields:
- `file`: The path to the audio/video clip
- `file_format`: The file format in which the clip is stored (e.g. `wav`, `aac`, `mp4`)
- `dataset_id`: The ID of the dataset this clip is from (`vox1`, `vox2`)
- `speaker_id`: The ID of the speaker in this clip
- `speaker_gender`: The gender of the speaker (`m`/`f`)
- `speaker_name` (VoxCeleb1 only): The full name of the speaker in the clip
- `speaker_nationality` (VoxCeleb1 only): The speaker's country of origin
- `video_id`: The ID of the video from which this clip was taken
- `clip_index`: The index of the clip for this specific video
- `audio` (Audio dataset only): The audio signal data
### Data Splits
The dataset has a predefined dev set and test set. The dev set has been renamed to a "train" split.
## 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
The dataset includes recordings of clips (mostly of celebrities and public figures) from public YouTube videos. The names of speakers in VoxCeleb1 are provided.
## 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
The VoxCeleb authors request that anyone who uses VoxCeleb1 or VoxCeleb2 includes the following three citations:
```
@Article{Nagrani19,
author = "Arsha Nagrani and Joon~Son Chung and Weidi Xie and Andrew Zisserman",
title = "Voxceleb: Large-scale speaker verification in the wild",
journal = "Computer Science and Language",
year = "2019",
publisher = "Elsevier",
}
@InProceedings{Chung18b,
author = "Chung, J.~S. and Nagrani, A. and Zisserman, A.",
title = "VoxCeleb2: Deep Speaker Recognition",
booktitle = "INTERSPEECH",
year = "2018",
}
@InProceedings{Nagrani17,
author = "Nagrani, A. and Chung, J.~S. and Zisserman, A.",
title = "VoxCeleb: a large-scale speaker identification dataset",
booktitle = "INTERSPEECH",
year = "2017",
}
```
### Contributions
Thanks to [@101arrowz](https://github.com/101arrowz) for adding this dataset.
|
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] |
bigbio/seth_corpus
|
bigbio
| 2022-12-22T15:46:51Z | 31 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2022-11-13T22:12:17Z |
---
language:
- en
bigbio_language:
- English
license: apache-2.0
multilinguality: monolingual
bigbio_license_shortname: APACHE_2p0
pretty_name: SETH Corpus
homepage: https://github.com/rockt/SETH
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for SETH Corpus
## Dataset Description
- **Homepage:** https://github.com/rockt/SETH
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE
SNP named entity recognition corpus consisting of 630 PubMed citations.
## Citation Information
```
@Article{SETH2016,
Title = {SETH detects and normalizes genetic variants in text.},
Author = {Thomas, Philippe and Rockt{"{a}}schel, Tim and Hakenberg, J{"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
Journal = {Bioinformatics},
Year = {2016},
Month = {Jun},
Doi = {10.1093/bioinformatics/btw234},
Language = {eng},
Medline-pst = {aheadofprint},
Pmid = {27256315},
Url = {http://dx.doi.org/10.1093/bioinformatics/btw234
}
```
|
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tarteel-ai/everyayah
|
tarteel-ai
| 2022-12-09T19:33:08Z | 31 | 6 |
[
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:mit",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-12-07T21:53:59Z |
---
pretty_name: Tarteel AI - EveryAyah Dataset
dataset_info:
features:
- name: audio
dtype: audio
- name: duration
dtype: float64
- name: text
dtype: string
- name: reciter
dtype: string
splits:
- name: train
num_bytes: 262627688145.3
num_examples: 187785
- name: test
num_bytes: 25156009734.72
num_examples: 23473
- name: validation
num_bytes: 23426886730.218
num_examples: 23474
download_size: 117190597305
dataset_size: 311210584610.23804
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ar
license:
- mit
multilinguality:
- monolingual
paperswithcode_id: tarteel-everyayah
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
train-eval-index:
- config: clean
task: automatic-speech-recognition
task_id: speech_recognition
splits:
train_split: train
eval_split: test
validation_split: validation
col_mapping:
audio: audio
text: text
reciter: text
metrics:
- type: wer
name: WER
- type: cer
name: CER
---
﷽
# Dataset Card for Tarteel AI's EveryAyah 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:** [Tarteel AI](https://www.tarteel.ai/)
- **Repository:** [Needs More Information]
- **Point of Contact:** [Mohamed Saad Ibn Seddik](mailto:[email protected])
### Dataset Summary
This dataset is a collection of Quranic verses and their transcriptions, with diacritization, by different reciters.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises the audio file `audio`, and its transcription called `text`.
The `duration` is in seconds, and the author is `reciter`.
An example from the dataset is:
```
{
'audio': {
'path': None,
'array': array([ 0. , 0. , 0. , ..., -0.00057983,
-0.00085449, -0.00061035]),
'sampling_rate': 16000
},
'duration': 6.478375,
'text': 'بِسْمِ اللَّهِ الرَّحْمَنِ الرَّحِيمِ',
'reciter': 'abdulsamad'
}
```
### 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]`.
- text: The transcription of the audio file.
- duration: The duration of the audio file.
- reciter: The reciter of the verses.
### Data Splits
| | Train | Test | Validation |
| ----- | ----- | ---- | ---------- |
| dataset | 187785 | 23473 | 23474 |
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## 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
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
```
### Contributions
This dataset was created by:
|
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lmqg/qag_ruquad
|
lmqg
| 2022-12-18T07:59:33Z | 31 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:1k<n<10K",
"source_datasets:lmqg/qg_ruquad",
"language:ru",
"license:cc-by-sa-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-12-18T07:05:48Z |
---
license: cc-by-sa-4.0
pretty_name: SQuAD for question generation
language: ru
multilinguality: monolingual
size_categories: 1k<n<10K
source_datasets: lmqg/qg_ruquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qag_ruquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is the question & answer generation dataset based on the RUQuAD.
### Supported Tasks and Leaderboards
* `question-answer-generation`: The dataset is assumed to be used to train a model for question & answer generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Russian (ru)
## Dataset Structure
An example of 'train' looks as follows.
```
{
"paragraph": " Everybody , как и хотела Мадонна, выпускают синглом. При нулевом бюджете на раскрутку фото певицы решают не помещать на обложке, чтобы не отпугнуть цветную аудиторию якобы негритянской диско-соул-певицы . Everybody поднимается на 3-е место в чарте Hot Dance Club Songs, а потом на 107 место в основном, немного не дотянув до первой сотни Hot 100 журнала Billboard[91]. Менеджмент считает это отличным результатом, учитывая нулевые затраты на пиар, и хочет убедиться, что взлёт Everybody не случаен. По просьбе Мадонны вместо Каминса берут более опытного штатного аранжировщика Warner Bros. Records Регги Лукаса (англ.)русск.. Второй сингл Burning Up тоже достигает в чарте танцевальных хитов 3-го места, повторив успех Everybody . И только после этого Мадонне позволяют арендовать студию для записи первого альбома[91].",
"questions": [ "При каком бюджете на раскрутку фото певицы решают не помещать на обложке ?", "Какой альбом Мадонны выпускают синглом?", "Имя более опытного штатного аранжировщика берут по просьбе Мадонны вместо Каминсаболее ?", "Почему при нулевом бджете фото певицы решают не помещать на обложке ?", "На каое место Everybody поднимается в чарте Hot Dance Club Songs?" ],
"answers": [ "При нулевом", " Everybody ", "Warner Bros", "чтобы не отпугнуть цветную аудиторию якобы негритянской диско-соул-певицы ", "на 3-е место" ],
"questions_answers": "question: При каком бюджете на раскрутку фото певицы решают не помещать на обложке ?, answer: При нулевом | question: Какой альбом Мадонны выпускают синглом?, answer: Everybody | question: Имя более опытного штатного аранжировщика берут по просьбе Мадонны вместо Каминсаболее ?, answer: Warner Bros | question: Почему при нулевом бджете фото певицы решают не помещать на обложке ?, answer: чтобы не отпугнуть цветную аудиторию якобы негритянской диско-соул-певицы | question: На каое место Everybody поднимается в чарте Hot Dance Club Songs?, answer: на 3-е место"
}
```
The data fields are the same among all splits.
- `questions`: a `list` of `string` features.
- `answers`: a `list` of `string` features.
- `paragraph`: a `string` feature.
- `questions_answers`: a `string` feature.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
|10407| 4079 | 4017|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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nielsr/coco-panoptic-val2017
|
nielsr
| 2022-12-25T17:26:14Z | 31 | 0 |
[
"region:us"
] | null | 2022-12-25T16:56:03Z |
---
dataset_info:
features:
- name: label
dtype: image
- name: segments_info
list:
- name: id
dtype: int64
- name: category_id
dtype: int64
- name: iscrowd
dtype: int64
- name: bbox
sequence: int64
- name: area
dtype: int64
- name: image_id
dtype: int64
- name: image
dtype: image
splits:
- name: train
num_bytes: 850795822.0
num_examples: 5000
download_size: 849210800
dataset_size: 850795822.0
---
# Dataset Card for "coco-panoptic-val2017"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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NeelNanda/wiki-10k
|
NeelNanda
| 2022-12-27T00:22:23Z | 31 | 0 |
[
"region:us"
] | null | 2022-12-27T00:22:16Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 222757944
num_examples: 10000
download_size: 129077566
dataset_size: 222757944
---
# Dataset Card for "wiki-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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AresEkb/prof_standards_sbert_large_mt_nlu_ru
|
AresEkb
| 2023-01-11T12:47:13Z | 31 | 0 |
[
"size_categories:100K<n<1M",
"language:ru",
"region:us"
] | null | 2023-01-08T12:04:10Z |
---
language:
- ru
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splits:
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splits:
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features:
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splits:
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- config_name: knowledges
features:
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splits:
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num_bytes: 1041374369
num_examples: 234283
download_size: 1022695670
dataset_size: 1041374369
pretty_name: Professional Standards
size_categories:
- 100K<n<1M
---
|
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LFBMS/class_dataset_donut2
|
LFBMS
| 2023-02-12T16:25:06Z | 31 | 0 |
[
"region:us"
] | null | 2023-02-12T16:22:15Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bilanz_datev
'1': bilanz_lexware
'2': guv
'3': other
- name: ground_truth
dtype: string
splits:
- name: test
num_bytes: 559064953.0
num_examples: 500
- name: train
num_bytes: 4343890380.0
num_examples: 4000
- name: validation
num_bytes: 548645901.0
num_examples: 500
download_size: 5424719748
dataset_size: 5451601234.0
---
# Dataset Card for "class_dataset_donut2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
artem9k/ai-text-detection-pile
|
artem9k
| 2023-02-27T03:37:54Z | 31 | 2 |
[
"license:mit",
"region:us"
] | null | 2023-02-27T02:52:29Z |
---
license: mit
---
# Dataset Card for AI Text Dectection Pile
## Dataset Description
- **Point of Contact:[email protected]
### Dataset Summary
This is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.
Here is the (tentative) breakdown:
#### Human Text
| Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- |
| Reddit WritingPromps | 570k | [Link](https://www.kaggle.com/datasets/ratthachat/writing-prompts) |
| OpenAI Webtext | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| HC3 (Human Responses) | 58k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ivypanda-essays | TODO | TODO |
| **Total** | **990k** | **-** |
#### AI-Generated Text
| Model | Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- | ----------- |
| GPT2 | OpenAI gpt2-output-dataset | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| GPT3 | pairwise-davinci | 44k | TODO |
| GPT3 | synthetic-instruct-davinci-pairwise | 30k | [Link](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses) |
| GPTJ | synthetic-instruct-gptj-pairwise | 44k | [Link](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) |
| ChatGPT | Scraped from twitter | 5k | **-** |
| ChatGPT | HC3 (ChatGPT Responses) | 27k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ChatGPT | ChatGPT Prompts/emergentmind | 500 | [Link](https://huggingface.co/datasets/MohamedRashad/ChatGPT-prompts/tree/main) |
| **Total** | **340k** | **-** | **-** |
### Supported Tasks and Leaderboards
Text Classification, AI Text Detection.
### Languages
English.
### Data Fields
TEXT: The text of the sample.
SOURCE: either "human" or "ai"
|
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] |
sedthh/cmu_wiki_qa
|
sedthh
| 2023-02-28T20:46:45Z | 31 | 2 |
[
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"Carnegie Mellon University",
"University of Pittsburgh",
"Wikipedia",
"Q&A",
"region:us"
] |
[
"question-answering",
"summarization"
] | 2023-02-28T20:46:15Z |
---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 410246
num_examples: 1610
download_size: 105516
dataset_size: 410246
license: mit
task_categories:
- question-answering
- summarization
language:
- en
tags:
- Carnegie Mellon University
- University of Pittsburgh
- Wikipedia
- Q&A
pretty_name: Question-Answer Dataset
size_categories:
- 1K<n<10K
---
# Dataset Card for "cmu_wiki_qa"
A filtered / cleaned version of the http://www.cs.cmu.edu/~ark/QA-data/ Q&A dataset, which provides manually-generated factoid questions from Wikipedia articles.
**Acknowledgments**
These data were collected by Noah Smith, Michael Heilman, Rebecca Hwa, Shay Cohen, Kevin Gimpel, and many students at Carnegie Mellon University and the University of Pittsburgh between 2008 and 2010.
Their research project was supported by NSF IIS-0713265 (to Smith), an NSF Graduate Research Fellowship (to Heilman), NSF IIS-0712810 and IIS-0745914 (to Hwa), and Institute of Education Sciences, U.S. Department of Education R305B040063 (to Carnegie Mellon).
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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intfloat/wikipedia
|
intfloat
| 2023-04-23T08:36:49Z | 31 | 1 |
[
"size_categories:100M<n<1B",
"region:us"
] | null | 2023-03-23T09:12:08Z |
---
size_categories:
- 100M<n<1B
---
### Dataset Summary
This dataset is based on [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia).
The main difference is that we add `Section::::` prefix to each section title to keep the section structure information.
We also use `:` to join the hierarchical section titles.
Following is an example.
```text
Alison Jane Horner (born June 1966) is a British businesswoman, and, until it was sold in 2020, was the CEO of the Asian arm of the Tesco supermarket chain.
Section::::Early life
Alison Jane Horner was born in June 1966. She earned a bachelor's degree in chemistry from the University of Manchester, and an MBA from Manchester Business School.
Section::::Career
Section::::Career:Tesco
Horner joined Tesco as a personnel manager in 1999 and was on Tesco's executive committee from 2011.
In October 2013, Horner became a founding member of The Guardian's Women in Leadership network. in 2015, she became a member of Alliance Manchester Business School's advisory board.
Horner was Tesco' chief people officer (chief human resources officer) of Tesco until May 2018, when she was promoted to be chief executive of Tesco's Asia business in Malaysia and Thailand, until it was sold in late 2020. She was set to step down in February 2021 after 22 years with Tesco.
Section::::Career:Carillion non-executive role
Horner was a non-executive director of Carillion from December 2013, chairing the remuneration committee from June 2014. As of 30 December 2016 her basic compensation was £61,000. After the company went into liquidation in January 2018, Horner was one of the non-executive directors who gave evidence to the House of Commons Business and Work and Pensions select committees on 6 February 2018. In the final report of the Parliamentary Inquiry, published on 16 May 2018, Horner was criticised by MPs; the report concluded:
"... Alison Horner presided over growing salaries and bonuses at the top of the company as its performance faltered. In her evidence to us, she sought to justify her approach by pointing to industry standards, the guidance of advisors, and conversations with shareholders. She failed to demonstrate to us any sense of challenge to the advice she was given, any concern about the views of stakeholders, or any regret at the largesse at the top of Carillion. Ms Horner continues to hold the role of Chief People Officer of Tesco, where she has responsibilities to more than half a million employees. We hope that, in that post, she will reflect on the lessons learned from Carillion and her role in its collapse."
In January 2021, the Insolvency Service said it would seek to ban eight former Carillion directors, including Horner, from holding senior boardroom positions.
Section::::References
Living people
1966 births
British businesspeople in retailing
Tesco people
Alumni of the University of Manchester
Alumni of the Manchester Business School
Carillion people
```
### Data Fields
- `title`: a `string` feature.
- `text`: a `string` feature.
### How to use this dataset
To load this dataset you need to install these first:
```shell
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
dataset = load_dataset("intfloat/wikipedia", language="en", date="20230401")
```
For more information,
please check out [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia).
## Supported Languages
```
aa
ab
ace
ady
af
ak
als
am
an
ang
ar
arc
arz
as
ast
atj
av
ay
az
azb
ba
bar
bat-smg
bcl
be
be-x-old
bg
bh
bi
bjn
bm
bn
bo
bpy
br
bs
bug
bxr
ca
cbk-zam
cdo
ce
ceb
ch
cho
chr
chy
ckb
co
cr
crh
cs
csb
cu
cv
cy
da
de
din
diq
dsb
dty
dv
dz
ee
el
eml
en
eo
es
et
eu
ext
fa
ff
fi
fiu-vro
fj
fo
fr
frp
frr
fur
fy
ga
gag
gan
gd
gl
glk
gn
gom
gor
got
gu
gv
ha
hak
haw
he
hi
hif
ho
hr
hsb
ht
hu
hy
ia
id
ie
ig
ii
ik
ilo
inh
io
is
it
iu
ja
jam
jbo
jv
ka
kaa
kab
kbd
kbp
kg
ki
kj
kk
kl
km
kn
ko
koi
krc
ks
ksh
ku
kv
kw
ky
la
lad
lb
lbe
lez
lfn
lg
li
lij
lmo
ln
lo
lrc
lt
ltg
lv
mai
map-bms
mdf
mg
mh
mhr
mi
min
mk
ml
mn
mr
mrj
ms
mt
mus
mwl
my
myv
mzn
na
nah
nap
nds
nds-nl
ne
new
ng
nl
nn
no
nov
nrm
nso
nv
ny
oc
olo
om
or
os
pa
pag
pam
pap
pcd
pdc
pfl
pi
pih
pl
pms
pnb
pnt
ps
pt
qu
rm
rmy
rn
ro
roa-rup
roa-tara
ru
rue
rw
sa
sah
sat
sc
scn
sco
sd
se
sg
sh
si
simple
sk
sl
sm
sn
so
sq
sr
srn
ss
st
stq
su
sv
sw
szl
ta
tcy
te
tet
tg
th
ti
tk
tl
tn
to
tpi
tr
ts
tt
tum
tw
ty
tyv
udm
ug
uk
ur
uz
ve
vec
vep
vi
vls
vo
wa
war
wo
wuu
xal
xh
xmf
yi
yo
za
zea
zh
zh-classical
zh-min-nan
zh-yue
zu
```
|
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] |
suolyer/pile_books3
|
suolyer
| 2023-03-27T03:01:19Z | 31 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-26T16:35:38Z |
---
license: apache-2.0
---
|
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] |
Chinese-Vicuna/guanaco_belle_merge_v1.0
|
Chinese-Vicuna
| 2023-03-30T07:49:30Z | 31 | 80 |
[
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
] | null | 2023-03-30T07:29:07Z |
---
license: gpl-3.0
language:
- zh
- en
- ja
---
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna)
|
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mstz/acute_inflammation
|
mstz
| 2023-04-15T11:37:39Z | 31 | 0 |
[
"task_categories:tabular-classification",
"size_categories:100<n<1K",
"language:en",
"acute_inflammation",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-05T11:13:27Z |
---
language:
- en
tags:
- acute_inflammation
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Acute Inflammation
size_categories:
- 100<n<1K
task_categories:
- tabular-classification
configs:
- inflammation
- nephritis
- bladder
---
# Acute Inflammation
The [Acute Inflammation dataset](https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations) from the [UCI ML repository](https://archive-beta.ics.uci.edu).
Predict whether the patient has an acute inflammation.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| inflammation | Binary classification | Does the patient have an acute inflammation? |
| nephritis | Binary classification | Does the patient have a nephritic pelvis? |
| bladder | Binary classification | Does the patient have bladder inflammation? |
nephritis
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/acute_inflammation", "inflammation")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
| **Feature** | **Type** |
|---------------------------------------|---------------|
| `temperature` | `[float64]` |
| `has_nausea` | `[bool]` |
| `has_lumbar_pain` | `[bool]` |
| `has_urine_pushing` | `[bool]` |
| `has_micturition_pains` | `[bool]` |
| `has_burnt_urethra` | `[bool]` |
| `has_inflammed_bladder` | `[bool]` |
| `has_nephritis_of_renal_pelvis` | `[bool]` |
| `has_acute_inflammation` | `[int8]` |
|
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mstz/mushroom
|
mstz
| 2023-04-16T17:34:40Z | 31 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"mushroom",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-06T17:42:03Z |
---
language:
- en
tags:
- mushroom
- tabular_classification
- binary_classification
- UCI
pretty_name: Mushroom
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- mushroom
license: cc
---
# Mushroom
The [Mushroom dataset](https://archive.ics.uci.edu/ml/datasets/Mushroom) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|---------------------------|
| mushroom | Binary classification | Is the mushroom poisonous?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/mushroom")["train"]
```
|
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] |
mstz/wall_following
|
mstz
| 2023-04-16T18:03:59Z | 31 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<5K",
"language:en",
"license:cc",
"wall_following",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-14T15:49:57Z |
---
language:
- en
tags:
- wall_following
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: WallFollowing
size_categories:
- 1K<n<5K
task_categories:
- tabular-classification
configs:
- wall_following
license: cc
---
# WallFollowing
The [WallFollowing dataset](https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| wall_following | Multiclass classification.| |
| wall_following_0 | Binary classification. | Is the instance of class 0? |
| wall_following_1 | Binary classification. | Is the instance of class 1? |
| wall_following_2 | Binary classification. | Is the instance of class 2? |
| wall_following_3 | Binary classification. | Is the instance of class 3? |
|
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vietgpt-archive/camel_vi
|
vietgpt-archive
| 2023-04-26T14:25:19Z | 31 | 0 |
[
"region:us"
] | null | 2023-04-18T09:28:33Z |
---
dataset_info:
features:
- name: role_1
dtype: string
- name: role_2
dtype: string
- name: original_task
dtype: string
- name: specified_task
dtype: string
- name: messages
list:
- name: input
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 171026076
num_examples: 10744
download_size: 52918251
dataset_size: 171026076
---
# Dataset Card for "camel_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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zhengyun21/PMC-Patients-ReCDS
|
zhengyun21
| 2023-11-07T16:21:59Z | 31 | 4 |
[
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"information retrieval",
"patient similarity",
"clinical decision support",
"arxiv:2202.13876",
"region:us"
] | null | 2023-04-23T14:54:45Z |
---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- information retrieval
- patient similarity
- clinical decision support
size_categories:
- 100K<n<1M
---
# Dataset Card for PMC-Patients-ReCDS
## Dataset Description
- **Homepage:** https://github.com/pmc-patients/pmc-patients
- **Repository:** https://github.com/pmc-patients/pmc-patients
- **Paper:** https://arxiv.org/pdf/2202.13876.pdf
- **Leaderboard:** https://pmc-patients.github.io/
- **Point of Contact:** [email protected]
### Dataset Summary
**PMC-Patients** is a first-of-its-kind dataset consisting of 167k patient summaries extracted from case reports in PubMed Central (PMC), 3.1M patient-article relevance and 293k patient-patient similarity annotations defined by PubMed citation graph.
### Supported Tasks and Leaderboards
Based on PMC-Patients, we define two tasks to benchmark Retrieval-based Clinical Decision Support (ReCDS) systems: Patient-to-Article Retrieval (PAR) and Patient-to-Patient Retrieval (PPR).
For details, please refer to [our paper](https://arxiv.org/pdf/2202.13876.pdf) and [leaderboard](https://pmc-patients.github.io/).
### Languages
English (en).
## Dataset Structure
The PMC-Patients ReCDS benchmark is presented as retrieval tasks and the data format is the same as [BEIR](https://github.com/beir-cellar/beir) benchmark.
To be specific, there are queries, corpus, and qrels (annotations).
### Queries
ReCDS-PAR and ReCDS-PPR tasks share the same query patient set and dataset split.
For each split (train, dev, and test), queries are stored a `jsonl` file that contains a list of dictionaries, each with two fields:
- `_id`: unique query identifier represented by patient_uid.
- `text`: query text represented by patient summary text.
### Corpus
Corpus is shared by different splits. For ReCDS-PAR, the corpus contains 11.7M PubMed articles, and for ReCDS-PPR, the corpus contains 155.2k reference patients from PMC-Patients. The corpus is also presented by a `jsonl` file that contains a list of dictionaries with three fields:
- `_id`: unique document identifier represented by PMID of the PubMed article in ReCDS-PAR, and patient_uid of the candidate patient in ReCDS-PPR.
- `title`: : title of the article in ReCDS-PAR, and empty string in ReCDS-PPR.
- `text`: abstract of the article in ReCDS-PAR, and patient summary text in ReCDS-PPR.
**PAR corpus note**
Due to its large size, we fail to upload the full PAR corpus on Huggingface. Instead, we provide PMIDs of the articles we include in PAR corpus, but we recommend you to download the dataset from [Figshare](https://figshare.com/collections/PMC-Patients/6723465) which contains the full PAR corpus file.
### Qrels
Qrels are TREC-style retrieval annotation files in `tsv` format.
A qrels file contains three tab-separated columns, i.e. the query identifier, corpus identifier, and score in this order. The scores (2 or 1) indicate the relevance level in ReCDS-PAR or similarity level in ReCDS-PPR.
Note that the qrels may not be the same as `relevant_articles` and `similar_patients` in `PMC-Patients.json` due to dataset split (see our manuscript for details).
### Data Instances
**A sample of query**
{"_id": "8699387-1", "text": "A 60-year-old female patient with a medical history of hypertension came to our attention because of several neurological deficits that had developed over the last few years, significantly impairing her daily life. Four years earlier, she developed sudden weakness and hypoesthesia of the right hand. The symptoms resolved in a few days and no specific diagnostic tests were performed. Two months later, she developed hypoesthesia and weakness of the right lower limb. On neurological examination at the time, she had spastic gait, ataxia, slight pronation of the right upper limb and bilateral Babinski sign. Brain MRI showed extensive white matter hyperintensities (WMHs), so leukodystrophy was suspected. However, these WMHs were located bilaterally in the corona radiata, basal ganglia, the anterior part of the temporal lobes and the medium cerebellar peduncle (A–D), and were highly suggestive of CADASIL. Genetic testing was performed, showing heterozygous mutation of the NOTCH3 gene (c.994 C<T; exon 6). The diagnosis of CADASIL was confirmed and antiplatelet prevention therapy was started. Since then, her clinical conditions remained stable, and the lesion load was unchanged at follow-up brain MRIs for 4 years until November 2020, when the patient was diagnosed with COVID-19 after a PCR nasal swab. The patient developed only mild respiratory symptoms, not requiring hospitalization or any specific treatment. Fifteen days after the COVID-19 diagnosis, she suddenly developed aphasia, agraphia and worsened right upper limb motor deficit, but she did not seek medical attention. Some days later, she reported these symptoms to her family medical doctor, and a new brain MRI was performed, showing a subacute ischemic area in the left corona radiata (E,F). Therapy with acetylsalicylic acid was switched to clopidogrel as secondary prevention, while her symptoms improved in the next few weeks. The patient underwent a carotid doppler ultrasound and an echocardiogram, which did not reveal any pathological changes. The review of the blood pressure log, both in-hospital and the personal one the patient had kept, excluded uncontrolled hypertension."}
**A sample of qrels**
query-id corpus-id score
8647806-1 6437752-1 1
8647806-1 6946242-1 1
### Data Splits
Refer to our paper.
## Dataset Creation
If you are interested in the collection of PMC-Patients and reproducing our baselines, please refer to [this reporsitory](https://github.com/zhao-zy15/PMC-Patients).
### Citation Information
If you find PMC-Patients helpful in your research, please cite our work by:
```
@misc{zhao2023pmcpatients,
title={PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems},
author={Zhengyun Zhao and Qiao Jin and Fangyuan Chen and Tuorui Peng and Sheng Yu},
year={2023},
eprint={2202.13876},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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kaiokendev/SuperCOT-dataset
|
kaiokendev
| 2023-05-26T23:45:43Z | 31 | 33 |
[
"license:mit",
"region:us"
] | null | 2023-04-23T21:42:15Z |
---
license: mit
---
- epochs: 3
- learning rate: 3e-4
- lora rank: 8
- lora alpha: 16
- lora dropout: 0.05 for cutoff 1024 13B, otherwise no dropout due to gradient checkpointing
- masking: none
- mbatch size: 4 (1 for 30B)
- batch size: 8 (2 for 30B)
- val set size: 0.2
- sdp implementation: xformers
- optimizer: AdamW
- eval strategy: none
Cleaned combination of:
[https://huggingface.co/datasets/QingyiSi/Alpaca-CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
- Chain of thought QED
- Chain of thought Aqua
- CodeAlpaca
[https://huggingface.co/datasets/neulab/conala](https://huggingface.co/datasets/neulab/conala)
- Code snippets
[https://huggingface.co/datasets/yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned)
- Alpaca GPT4
Used in https://huggingface.co/kaiokendev/SuperCOT-LoRA
|
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] |
Thaweewat/gpteacher-20k-th
|
Thaweewat
| 2023-05-09T17:54:22Z | 31 | 1 |
[
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] |
[
"question-answering",
"summarization"
] | 2023-05-09T17:34:31Z |
---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
language:
- th
tags:
- instruction-finetuning
size_categories:
- 10K<n<100K
---
# Summary
This is a 🇹🇭 Thai-instructed dataset translated using Google Cloud Translation from [GPTeacher](https://github.com/teknium1/GPTeacher), A collection of modular datasets generated by GPT-4, General-Instruct & Roleplay-Instruct
and is comprised of around 20,000 examples with deduplication. The dataset was asked to include reasoning and thought steps in the example responses where appropriate.
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
---
|
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] |
RossVermouth/chensu_test_dataset
|
RossVermouth
| 2023-05-19T08:23:29Z | 31 | 0 |
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:aa",
"language:ae",
"license:apache-2.0",
"not-for-all-audiences",
"region:us"
] |
[
"image-classification"
] | 2023-05-19T07:58:00Z |
---
license: apache-2.0
task_categories:
- image-classification
language:
- aa
- ae
tags:
- not-for-all-audiences
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
just for test
### 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
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andersonbcdefg/redteaming_eval_pairwise
|
andersonbcdefg
| 2023-06-08T05:51:12Z | 31 | 0 |
[
"region:us"
] | null | 2023-06-08T05:48:52Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: explanation
dtype: string
- name: preferred
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 79844
num_examples: 105
download_size: 0
dataset_size: 79844
---
# Dataset Card for "redteaming_eval_pairwise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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lca0503/GPTspeech_encodec_v2
|
lca0503
| 2023-06-15T06:54:51Z | 31 | 0 |
[
"region:us"
] | null | 2023-06-14T16:48:10Z |
---
dataset_info:
features:
- name: file_id
dtype: string
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
sequence: int64
- name: src_encodec_2
sequence: int64
- name: src_encodec_3
sequence: int64
- name: src_encodec_4
sequence: int64
- name: src_encodec_5
sequence: int64
- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
sequence: int64
- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
sequence: int64
- name: tgt_encodec_3
sequence: int64
- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
sequence: int64
splits:
- name: train
num_bytes: 42732349968
num_examples: 704563
- name: validation
num_bytes: 706650258
num_examples: 12855
- name: test
num_bytes: 700741253
num_examples: 12463
download_size: 4503561741
dataset_size: 44139741479
---
# Dataset Card for "GPTspeech_encodec_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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v2run/invoices-donut-data-v1
|
v2run
| 2023-06-15T08:31:03Z | 31 | 1 |
[
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] |
[
"feature-extraction"
] | 2023-06-15T08:26:29Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 234024421
num_examples: 425
- name: test
num_bytes: 14512665
num_examples: 26
- name: validation
num_bytes: 27661738
num_examples: 50
download_size: 197512750
dataset_size: 276198824
license: mit
task_categories:
- feature-extraction
language:
- en
pretty_name: Sparrow Invoice Dataset
size_categories:
- n<1K
---
# Dataset Card for Invoices (Sparrow)
This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.
Annotation and data preparation task was done by [Katana ML](https://www.katanaml.io) team.
[Sparrow](https://github.com/katanaml/sparrow/tree/main) - open-source data extraction solution by Katana ML.
Original dataset [info](https://data.mendeley.com/datasets/tnj49gpmtz): Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2
|
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jaimevera1107/similarity-sentences-spanish
|
jaimevera1107
| 2023-07-24T14:11:43Z | 31 | 1 |
[
"task_categories:sentence-similarity",
"size_categories:10K<n<100K",
"language:es",
"license:mit",
"region:us"
] |
[
"sentence-similarity"
] | 2023-07-16T05:35:49Z |
---
license: mit
task_categories:
- sentence-similarity
language:
- es
size_categories:
- 10K<n<100K
pretty_name: SimilaritySpanishDataset
---
# similarity-sentences-spanish (SSS)
### Dataset Summary
This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics.
The dataset also includes sentences from two existing datasets, STS-ES and STSB-Multi-MT, as well as SICK, which were used as additional sources.
The sentences in this dataset were generated to exhibit varying levels of similarity based on randomly divided prompts.
| **Source** | **Share (rows)** | **Count (rows)** | **Score (avg)** |
|-----------|-----------------|------------------|----------------|
| GPT | 22.71 % | 3982 | 0.50 |
| STBS | 49.21 % | 8628 | 0.53 |
| STS | 17.69 % | 3102 | 0.42 |
| SICK | 10.38 % | 1820 | 0.51 |
| **Total** | 100% | 17532 | 0.49 |
### Objective
The purpose of creating this dataset using Chat GPT-3 was to generate diverse text samples covering various topics and to ensure a balanced distribution of scores both overall and across different themes. By leveraging Chat GPT-3, the dataset aims to provide a wide range of sentence pairs with varying degrees of similarity for further analysis and research purposes.
### Languages
Spanish
## Dataset Structure
### Data Fields
- Sentence 1: The first sentence to be compared.
- Sentence 2: The second sentence to be compared.
- Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
- Source: The source of the information, represented by its abbreviation.
## Dataset Biases
This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3.
### Source Data
The dataset was created using the following sources:
1. Already existing datasets:
- STS-ES ([STSB](https://huggingface.co/datasets/stsb_multi_mt))
- STSB-Multi-MT ([STS](https://huggingface.co/datasets/PlanTL-GOB-ES/sts-es))
2. Newly generated data:
- Chat GPT-3: The sentences were generated using Chat GPT-3 for various general topics.
The dataset includes sentences from various themes, such as:
- Alimentación y Cocina (Food and Cooking)
- Arte y Cultura (Art and Culture)
- Ciencia y Tecnología (Science and Technology)
- Cine y Televisión (Film and Television)
- Deportes (Sports)
- Economía (Economy)
- Educación (Education)
- Estadística (Statistics)
- Filosofía (Philosophy)
- Finanzas (Finance)
- Historia (History)
- Literatura (Literature)
- Medicina (Medicine)
- Medio Ambiente y Sostenibilidad (Environment and Sustainability)
- Moda y Estilo (Fashion and Style)
- Música (Music)
- Organizacional (Organizational)
- Política y Gobierno (Politics and Government)
- Psicología (Psychology)
- Religión y Espiritualidad (Religion and Spirituality)
- Salud y Bienestar (Health and Wellness)
Please note that these themes are not exhaustive.
The prompts for each label (score) are as follows:
```python
descripciones_similaridad = {
"0.0": "Rewrite the following sentence in a new sentence about a completely different topic, without any apparent connection to the original sentence. The two sentences must be completely distinct and should not share any thematic similarity.",
"0.1": "Rewrite the following sentence in a new sentence about a topic completely different from the original sentence. Make sure the two sentences are entirely different and do not share any thematic similarity. At least 90% of the information level should change.",
"0.2": "Rewrite the following sentence in a new sentence about the same topic as the original sentence, but not an exact copy. You can express different ideas, but the general theme should be similar. Ensure at least 80% of the information level is different.",
"0.3": "Rewrite the following sentence in a new sentence about a topic related to the original sentence, though not equivalent. Both sentences must share a common theme or general idea, but they can express different viewpoints. At least 70% of the information level should change.",
"0.4": "Rewrite the following sentence in a new sentence that is not equivalent to the original, but has some similar details or elements. Ensure at least 60% of the information level is different.",
"0.5": "Rewrite the following sentence in a new sentence that is not equivalent to the original, but is related to some extent. Both sentences should have some details in common and be thematically related at least 50% of the information level.",
"0.6": "Rewrite the following sentence in a new sentence that is approximately equivalent to the original, but may differ in important information or have certain missing elements. The changes should slightly affect the meaning, and at least 60% of the information level should be preserved.",
"0.7": "Rewrite the following sentence in a new sentence that is approximately equivalent to the original, but may differ in important information or have some missing elements. Ensure at least 70% of the information level remains the same.",
"0.8": "Rewrite the following sentence in a new sentence that is mostly equivalent to the original, but may differ in some unimportant details. The changes should affect a maximum of 20% of the information level.",
"0.9": "Rewrite the following sentence in a new sentence that is nearly equivalent to the original, but may have some differences in minor details that do not significantly impact its meaning. The changes should affect a maximum of 10% of the information level.",
"1.0": "Rewrite the following sentence in a new sentence that is completely equivalent to the original, as they express exactly the same idea or meaning. The two sentences must share 100% of the information level.",
}
```
- SICK ([SICK Dataset](https://huggingface.co/datasets/sick))
The dataset also includes translated and sampled sentences from the SICK dataset using Helsinki ([helsinki - EN -ES](https://huggingface.co/datasets/sick)) as the translation tool to achieve an average score close to 0.5 with the entire dataset.
To maintain a balanced representation and avoid excessive prominence of translated data that was not originally written in Spanish and has not been reviewed in Spanish, the intention is to have scores generally centered around 0.5.
|
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foduucom/table-detection-yolo
|
foduucom
| 2023-08-05T14:42:23Z | 31 | 5 |
[
"task_categories:object-detection",
"size_categories:1K<n<10K",
"language:en",
"foduuai",
"table",
"Documents",
"bordered table",
"borderless table",
"unstructured document",
"region:us"
] |
[
"object-detection"
] | 2023-08-05T11:43:51Z |
---
task_categories:
- object-detection
tags:
- foduuai
- table
- Documents
- bordered table
- borderless table
- unstructured document
language:
- en
pretty_name: TableBorderNet
size_categories:
- 1K<n<10K
---
<div align="center">
<img width="640" alt="foduucom/table-detection-yolo" src="https://huggingface.co/datasets/foduucom/table-detection-yolo/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['bordered', 'borderless']
```
### Number of Images
```json
{'test': 34, 'train': 238, 'valid': 70}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("foduucom/table-detection-yolo", name="full")
example = ds['train'][0]
```
### Dataset Summary
Certainly! Here's a dataset summary for your dataset of images containing tables that are categorized as border and borderless, provided in YOLO format:
## Dataset Summary
The **Table Detection Dataset** is a curated collection of images, each depicting tables that are classified as either 'bordered' or 'borderless'. The dataset is provided in YOLO format, featuring annotations for accurate object detection and classification. It serves as a valuable resource for researchers, developers, and practitioners working on table detection tasks, with a specific focus on distinguishing between tables with distinct visual characteristics.
**Key Features:**
- **Image Variety:** The dataset encompasses a diverse range of images, capturing tables from various real-world scenarios and environments.
- **Annotation Precision:** Each image is meticulously annotated with bounding box coordinates and class labels, indicating whether the table is 'bordered' or 'borderless'.
- **YOLO Format:** Annotations follow the YOLO format, making it suitable for training and evaluating object detection models.
- **Research and Development:** The dataset is designed to facilitate advancements in table detection algorithms and technologies, enabling the development of models capable of accurately identifying and classifying different types of tables.
Whether you are working on document analysis, data extraction, or image-based content recognition, the Table Detection Dataset provides an essential foundation for enhancing the capabilities of object detection models in identifying tables with varying visual attributes. By offering a comprehensive collection of border and borderless tables, this dataset empowers the AI community to tackle challenges in table detection across a wide range of applications.
For more details and access to the dataset, please refer to [email protected] .
|
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deep-plants/AGM_HS
|
deep-plants
| 2023-10-04T11:07:25Z | 31 | 3 |
[
"license:cc",
"region:us"
] | null | 2023-08-16T10:04:19Z |
---
license: cc
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: crop_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 22900031.321
num_examples: 6127
download_size: 22010079
dataset_size: 22900031.321
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for AGM_HS Dataset
## Dataset Summary
The AGM<sub>HS</sub> (AGricolaModerna Healthy-Stress) Dataset is an extension of the AGM Dataset, specifically curated to address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset comprises 6,127 high-resolution RGB images, each with a resolution of 120x120 pixels, selected from the original AGM Dataset. The images in AGM<sub>HS</sub> are divided into two categories: healthy samples (3,798 images) and stressed samples (2,329 images) representing 14 of the 18 classes present in AGM. Alongside the healthy/stressed classification labels, the dataset also provides segmentation masks for the stressed areas.
## Supported Tasks
Image classification: Healthy-stressed classification
Image segmentation: detection and localization of plant stress in top-view images.
## Languages
The dataset primarily consists of image data and does not involve language content. Therefore, the primary language is English, but it is not relevant to the dataset's core content.
## Dataset Structure
### Data Instances
A typical data instance from the AGM<sub>HS</sub> Dataset consists of the following:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=120x120 at 0x29CEAD71780>,
'labels': 'stressed',
'crop_type': 'by'
'mask': <PIL.PngImagePlugin.PngImageFile image mode=L size=120x120 at 0x29CEAD71780>
}
```
### Data Fields
The dataset's data instances have the following fields:
- `image`: A PIL.Image.Image object representing the image.
- `labels`: A string representation indicating whether the image is "healthy" or "stressed."
- `crop_type`: An string representation of the crop type in the image
- `mask`: A PIL.Image.Image object representing the segmentation mask of stressed areas in the image, stored as a PNG image.
### Data Splits
- **Training Set**:
- Number of Examples: 6,127
- Healthy Samples: 3,798
- Stressed Samples: 2,329
## Dataset Creation
### Curation Rationale
The AGM<sub>HS</sub> Dataset was created as an extension of the AGM Dataset to specifically address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset is essential for the development and evaluation of advanced segmentation models tailored for this task.
### Source Data
#### Initial Data Collection and Normalization
The images in AGM<sub>HS</sub> were extracted from the original AGM Dataset. During the extraction process, labelers selected images showing clear signs of either good health or high stress. These sub-images were resized to 120x120 pixels to create AGM<sub>HS</sub>.
### Annotations
#### Annotation Process
The AGM<sub>HS</sub> Dataset underwent a secondary stage of annotation. Labelers manually collected images by clicking on points corresponding to stressed areas on the leaves. These clicked points served as prompts for the semi-automatic generation of segmentation masks using the "Segment Anything" technique \cite{kirillov2023segment}. Each image is annotated with a classification label ("healthy" or "stressed") and a corresponding segmentation mask.
### Who Are the Annotators?
The annotators for AGM<sub>HS</sub> are domain experts with knowledge of plant health and stress detection.
## Personal and Sensitive Information
The dataset does not contain personal or sensitive information about individuals. It exclusively consists of images of plants.
## Considerations for Using the Data
### Social Impact of Dataset
The AGM<sub>HS</sub> Dataset plays a crucial role in advancing research and technologies for plant stress detection and localization in the context of modern agriculture. By providing a diverse set of top-view crop images with associated segmentation masks, this dataset can facilitate the development of innovative solutions for sustainable agriculture, contributing to increased crop health, yield prediction, and overall food security.
### Discussion of Biases and Known Limitations
While AGM<sub>HS</sub> is a valuable dataset, it inherits some limitations from the original AGM Dataset. It primarily involves images from a single vertical farm setting, potentially limiting the representativeness of broader agricultural scenarios. Additionally, the dataset's composition may reflect regional agricultural practices and business-driven crop preferences specific to vertical farming. Researchers should be aware of these potential biases when utilizing AGM<sub>HS</sub> for their work.
## Additional Information
### Dataset Curators
The AGM<sub>HS</sub> Dataset is curated by DeepPlants and AgricolaModerna. For further information, please contact us at:
- [email protected]
- [email protected]
### Licensing Information
### Citation Information
If you use the AGM<sub>HS</sub> dataset in your work, please consider citing the following publication:
```bibtex
@InProceedings{Sama_2023_ICCV,
author = {Sama, Nico and David, Etienne and Rossetti, Simone and Antona, Alessandro and Franchetti, Benjamin and Pirri, Fiora},
title = {A new Large Dataset and a Transfer Learning Methodology for Plant Phenotyping in Vertical Farms},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {October},
year = {2023},
pages = {540-551}
}
```
|
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pig4431/HeQ_v1
|
pig4431
| 2023-08-16T13:13:16Z | 31 | 2 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:he",
"license:cc-by-4.0",
"region:us"
] |
[
"question-answering"
] | 2023-08-16T12:59:03Z |
---
license: cc-by-4.0
task_categories:
- question-answering
language:
- he
size_categories:
- 1K<n<10K
---
# Dataset Card for HeQ_v1
## Dataset Description
- **Homepage:** [HeQ - Hebrew Question Answering Dataset](https://github.com/NNLP-IL/Hebrew-Question-Answering-Dataset)
- **Repository:** [GitHub Repository](https://github.com/NNLP-IL/Hebrew-Question-Answering-Dataset)
- **Paper:** [HeQ: A Dataset for Hebrew Question Answering](https://u.cs.biu.ac.il/~yogo/heq.pdf)
- **Leaderboard:** N/A
### Dataset Summary
HeQ is a question answering dataset in Modern Hebrew, consisting of 30,147 questions. It follows the format and crowdsourcing methodology of SQuAD and ParaShoot, with paragraphs sourced from Hebrew Wikipedia and Geektime.
### Supported Tasks and Leaderboards
- **Task:** Question Answering
### Languages
- Hebrew (he)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- **ID:** `string`
- **Title:** `string`
- **Source:** `string`
- **Context:** `string`
- **Question:** `string`
- **Answers:** `string`
- **Is_Impossible:** `bool`
- **WH_Question:** `string`
- **Question_Quality:** `string`
### Data Splits
- **Train:** 27,142 examples
- **Test:** 1,504 examples
- **Validation:** 1,501 examples
## Dataset Creation
### Curation Rationale
The dataset was created to provide a resource for question answering research in Hebrew.
### Source Data
#### Initial Data Collection and Normalization
Paragraphs were sourced from Hebrew Wikipedia and Geektime.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
A team of crowdworkers formulated and answered reading comprehension questions.
#### Who are the annotators?
crowdsourced
### 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
License: cc-by-4.0
### Citation Information
[More Information Needed]
### Contributions
Contributions and additional information are welcome.
|
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] |
ziq/RSNA-ATD2023
|
ziq
| 2023-08-31T14:31:16Z | 31 | 0 |
[
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"annotations_creators:other",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:mit",
"region:us"
] |
[
"image-segmentation"
] | 2023-08-20T09:28:18Z |
---
annotations_creators:
- other
language:
- en
language_creators:
- found
- expert-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: RSNA-ATD2023
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
tags: []
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
---
# 📁 Dataset
This dataset only comprised of 205 series of CT scans in `.png` file with raw images and raw mask.
Data source: [Kaggle RSNA 2023 Abdominal Trauma Detection](https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/data)
# 🚀 Setup
```bash
pip install datasets
```
# 🤩 Feel the Magic
### Load Dataset
```python
from datasets import load_dataset
data = load_dataset('ziq/RSNA-ATD2023')
print(data)
```
```bash
DatasetDict({
train: Dataset({
features: ['patient_id', 'series_id', 'frame_id', 'image', 'mask'],
num_rows: 70291
})
})
```
### Set Labels
```python
labels = ["background", "liver", "spleen", "right_kidney", "left_kidney", "bowel"]
```
### Train Test Split
```python
data = data['train'].train_test_split(test_size=0.2)
```
```python
train, test = data['train'], data['test']
# train[0]['patient_id']
# train[0]['image'] -> PIL Image
# train[0]['mask'] -> PIL Image
```
### Get Image & Segmentation Mask
```python
ids = 3
image, mask = train[ids]['image'], \ # shape: (512, 512)
train[ids]['mask'] # shape: (512, 512)
```
### Convert mask into np.ndarray
```python
mask = np.array(mask)
```
### Visualize Image & Mask
```python
fig = plt.figure(figsize=(16,16))
ax1 = fig.add_subplot(131)
plt.axis('off')
ax1.imshow(image, cmap='gray')
ax2 = fig.add_subplot(132)
plt.axis('off')
ax2.imshow(mask, cmap='gray')
ax3 = fig.add_subplot(133)
ax3.imshow(image*np.where(mask>0,1,0), cmap='gray')
plt.axis('off')
plt.show()
```
### Write Custom Plotting Function
```python
from matplotlib.colors import ListedColormap, BoundaryNorm
colors = ['#02020e', '#520e6d', '#c13a50', '#f57d15', '#fac62c', '#f4f88e'] # inferno
bounds = range(0, len(colors) + 1)
# Define the boundaries for each class in the colormap
cmap, norm = ListedColormap(colors), BoundaryNorm(bounds, len(colors))
# Plot the segmentation mask with the custom colormap
def plot_mask(mask, alpha=1.0):
_, ax = plt.subplots()
cax = ax.imshow(mask, cmap=cmap, norm=norm, alpha=alpha)
cbar = plt.colorbar(cax, cmap=cmap, norm=norm, boundaries=bounds, ticks=bounds)
cbar.set_ticks([])
_labels = [""] + labels
for i in range(1, len(_labels)):
cbar.ax.text(2, -0.5 + i, _labels[i], ha='left', color=colors[i - 1], fontsize=8)
plt.axis('off')
plt.show()
```
### Custom Color
```python
plot_mask(mask)
```
### Plot only one class (e.g. liver)
```python
liver, spleen, right_kidney, left_kidney, bowel = [(mask == i,1,0)[0] * i for i in range(1, len(labels))]
plot_mask(liver)
```
|
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seungheondoh/audioset-music
|
seungheondoh
| 2023-08-23T03:09:25Z | 31 | 1 |
[
"language:en",
"license:mit",
"music",
"audioset",
"arxiv:2302.03917",
"region:us"
] | null | 2023-08-23T02:20:43Z |
---
license: mit
language:
- en
tags:
- music
- audioset
pretty_name: audioset-music
---
# Dataset Card for "audioset-music"
audioset-subset using 130 music mid from [noise2music](https://arxiv.org/abs/2302.03917)
```
[
'/m/0z9c','/m/0mkg','/m/042v_gx','/m/0fd3y','/t/dd00036','/m/025td0t','/m/0192l','/m/018j2','/m/0bm02','/m/018vs','/m/02cz_7','/m/0395lw','/m/0gg8l','/m/0155w','/m/0l14_3',
'/m/01kcd','/m/015vgc','/m/01xqw','/m/02bk07','/m/0l14jd','/m/02mscn','/m/0140xf','/m/01wy6','/m/0ggq0m','/m/01lyv','/m/0239kh','/m/01qbl','/m/0ggx5q','/m/02bxd','/m/026z9',
'/m/02fsn','/m/0283d','/m/02hnl','/m/02k_mr','/m/026t6','/m/07s72n','/m/02sgy','/m/08cyft','/m/02lkt','/m/03xq_f','/m/0m0jc','/t/dd00035','/m/0326g','/m/0l14j_','/m/02w4v',
'/m/0319l','/m/02x8m','/t/dd00032','/m/0dwtp','/m/0mbct','/m/0dls3','/m/0342h','/m/03gvt','/t/dd00031','/m/03qjg','/m/03m5k','/m/03q5t','/m/03lty','/m/0glt670','/m/03mb9',
'/m/05rwpb','/m/03_d0','/m/03r5q_','/m/05148p4','/m/07pkxdp','/m/0j45pbj','/m/04rzd','/m/0dwsp','/m/06j64v','/m/05fw6t','/m/0164x2','/m/028sqc','/m/0dq0md','/m/0g293',
'/m/02v2lh','/m/05pd6','/m/013y1f','/m/0l14md','/m/05r5c','/m/0fx80y','/m/064t9','/m/0dl5d','/m/05w3f','/m/05r6t','/m/05r5wn','/m/06cqb','/m/06j6l','/m/03t3fj','/m/07sbbz2',
'/m/06by7','/t/dd00033','/m/0ln16','/m/06ncr','/t/dd00037','/m/01hgjl','/m/0l14l2','/m/0l14t7','/m/0jtg0','/m/06rqw','/m/06rvn','/m/0gywn','/m/0l14gg','/m/06w87','/m/0l156b',
'/m/02qmj0d','/m/07s0s5r','/m/015y_n','/m/0l14qv','/m/01p970','/m/07brj','/m/01glhc','/m/07gxw','/t/dd00034','/m/02cjck','/m/07kc_','/m/011k_j','/m/02p0sh1','/m/07lnk',
'/m/07c6l','/m/07gql','/m/016622','/m/07xzm','/m/0dwt5','/m/01z7dr','/m/07y_7','/m/0y4f8','/m/04wptg','/m/085jw','/m/01sm1g','/m/01bns_'
]
```
```
[
'A capella','Accordion','Acoustic guitar','Ambient music','Angry music',
'Background music','Bagpipes','Banjo','Bass drum','Bass guitar','Beatboxing','Bell','Bluegrass','Blues','Bowed string instrument','Brass instrument',
'Carnatic music','Cello','Chant','Choir','Christian music','Christmas music','Clarinet','Classical music','Country','Cowbell','Cymbal',
'Dance music','Didgeridoo','Disco','Double bass','Drum and bass','Drum kit','Drum roll','Drum','Dubstep',
'Electric guitar','Electronic dance music','Electronic music','Electronic organ','Electronica','Exciting music',
'Flamenco','Flute','Folk music','French horn','Funk','Funny music',
'Glockenspiel','Gong','Grunge','Guitar',
'Hammond organ','Happy music','Harmonica','Harp','Harpsichord','Heavy metal','Hip hop music','House music',
'Independent music',
'Jazz','Jingle (music)',
'Keyboard (musical)',
'Lullaby',
'Mallet percussion','Mandolin','Marimba, xylophone','Middle Eastern music','Music for children','Music of Africa','Music of Asia','Music of Bollywood','Music of Latin America',
'New-age music',
'Orchestra','Organ',
'Percussion','Piano','Plucked string instrument','Pop music','Progressive rock','Psychedelic rock','Punk rock',
'Rattle (instrument)','Reggae','Rhythm and blues','Rimshot','Rock and roll','Rock music',
'Sad music','Salsa music','Saxophone','Scary music','Scratching (performance technique)','Shofar','Singing bowl','Sitar','Ska','Snare drum','Soul music','Soundtrack music','Steel guitar, slide guitar','Steelpan','String section','Strum','Swing music','Synthesizer',
'Tabla','Tambourine','Tapping (guitar technique)','Techno','Tender music','Theme music','Theremin','Timpani','Traditional music','Trance music','Trombone','Trumpet','Tubular bells',
'Ukulele',
'Vibraphone','Video game music','Violin, fiddle','Vocal music',
'Wedding music','Wind instrument, woodwind instrument','Wood block',
'Zither'
]
```
|
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] |
philschmid/meta-shepherd-human-data
|
philschmid
| 2023-08-23T14:38:43Z | 31 | 3 |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-08-23T14:36:13Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: dataset
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: feedback
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2588873
num_examples: 1317
download_size: 1319368
dataset_size: 2588873
license: cc-by-nc-4.0
---
# Dataset Card for "meta-shepherd-human-data"
Original Dataset: https://github.com/facebookresearch/Shepherd
## Example
```bash
### Question: Where on the planet would you expect a bald eagle to live?
Here are the options:
Option 1: colorado
Option 2: outside
Option 3: protection
Option 4: zoo exhibit
Option 5: world
Please choose the correct option and justify your choice:
### Answer: Bald eagles are found throughout most of North America, from Alaska and Canada south to Mexico. They are also found in some parts of Europe and Asia. In the United States, bald eagles are usually seen near large bodies of water where they hunt for fish. They build their nests high up in tall trees near rivers, lakes, marshes, and coastal areas. Because of hunting and loss of habitat, bald eagle populations declined dramatically during the twentieth century. Conservation efforts have helped increase their numbers, and they were removed from the U.S. Endangered Species List in 2007.
### Feedback: The answer adds extra information, last 2 sentences are unnecessary. It does not state which options specifically are where bald eagles might live, like outside, Colorado, Zoo Exhibit, and World.
```
|
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dim/linux_man_pages_tldr_summarized
|
dim
| 2023-08-31T19:56:32Z | 31 | 0 |
[
"region:us"
] | null | 2023-08-31T19:51:37Z |
---
dataset_info:
features:
- name: Command
dtype: string
- name: Text
dtype: string
- name: Summary
dtype: string
splits:
- name: train
num_bytes: 3006835
num_examples: 481
download_size: 1308915
dataset_size: 3006835
---
# Dataset Card for "linux_man_pages_tldr_summarized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Nicolas-BZRD/Original_Songs_Lyrics_with_French_Translation
|
Nicolas-BZRD
| 2023-10-16T14:02:02Z | 31 | 6 |
[
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:fr",
"language:en",
"language:es",
"language:it",
"language:de",
"language:ko",
"language:id",
"language:pt",
"language:no",
"language:fi",
"language:sv",
"language:sw",
"language:hr",
"language:so",
"language:ca",
"language:tl",
"language:ja",
"language:nl",
"language:ru",
"language:et",
"language:tr",
"language:ro",
"language:cy",
"language:vi",
"language:af",
"language:hu",
"language:sk",
"language:sl",
"language:cs",
"language:da",
"language:pl",
"language:sq",
"language:el",
"language:he",
"language:zh",
"language:th",
"language:bg",
"language:ar",
"license:unknown",
"music",
"parallel",
"parallel data",
"region:us"
] |
[
"translation",
"text-generation"
] | 2023-09-12T21:21:44Z |
---
license: unknown
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: artist_name
dtype: string
- name: album_name
dtype: string
- name: year
dtype: int64
- name: title
dtype: string
- name: number
dtype: int64
- name: original_version
dtype: string
- name: french_version
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 250317845
num_examples: 99289
download_size: 122323323
dataset_size: 250317845
task_categories:
- translation
- text-generation
language:
- fr
- en
- es
- it
- de
- ko
- id
- pt
- 'no'
- fi
- sv
- sw
- hr
- so
- ca
- tl
- ja
- nl
- ru
- et
- tr
- ro
- cy
- vi
- af
- hu
- sk
- sl
- cs
- da
- pl
- sq
- el
- he
- zh
- th
- bg
- ar
tags:
- music
- parallel
- parallel data
pretty_name: SYFT
size_categories:
- 10K<n<100K
---
# Original Songs Lyrics with French Translation
### Dataset Summary
Dataset of 99289 songs containing their metadata (author, album, release date, song number), original lyrics and lyrics translated into French.
Details of the number of songs by language of origin can be found in the table below:
| Original language | Number of songs |
|---|:---|
| en | 75786 |
| fr | 18486 |
| es | 1743 |
| it | 803 |
| de | 691 |
| sw | 529 |
| ko | 193 |
| id | 169 |
| pt | 142 |
| no | 122 |
| fi | 113 |
| sv | 70 |
| hr | 53 |
| so | 43 |
| ca | 41 |
| tl | 36 |
| ja | 35 |
| nl | 32 |
| ru | 29 |
| et | 27 |
| tr | 22 |
| ro | 19 |
| cy | 14 |
| vi | 14 |
| af | 13 |
| hu | 10 |
| sk | 10 |
| sl | 10 |
| cs | 7 |
| da | 6 |
| pl | 5 |
| sq | 4 |
| el | 4 |
| he | 3 |
| zh-cn | 2 |
| th | 1 |
| bg | 1 |
| ar | 1 |
|
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BAAI/COIG-PC-core
|
BAAI
| 2023-09-25T10:33:33Z | 31 | 11 |
[
"language:zh",
"license:unknown",
"region:us"
] | null | 2023-09-19T06:24:01Z |
---
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: |
北京智源人工智能研究院（以下简称“我们”或“研究院”）通过BAAI DataHub（data.baai.ac.cn）和COIG-PC HuggingFace仓库（https://huggingface.co/datasets/BAAI/COIG-PC）向您提供开源数据集（以下或称“数据集”），您可通过下载的方式获取您所需的开源数据集，并在遵守各原始数据集使用规则前提下，基于学习、研究、商业等目的使用相关数据集。
在您获取（包括但不限于访问、下载、复制、传播、使用等处理数据集的行为）开源数据集前，您应认真阅读并理解本《COIG-PC开源数据集使用须知与免责声明》（以下简称“本声明”）。一旦您获取开源数据集，无论您的获取方式为何，您的获取行为均将被视为对本声明全部内容的认可。
1. 平台的所有权与运营权
您应充分了解并知悉，BAAI DataHub和COIG-PC HuggingFace仓库（包括当前版本及全部历史版本）的所有权与运营权归智源人工智能研究院所有，智源人工智能研究院对本平台/本工具及开源数据集开放计划拥有最终解释权和决定权。
您知悉并理解，基于相关法律法规更新和完善以及我们需履行法律合规义务的客观变化，我们保留对本平台/本工具进行不定时更新、维护，或者中止乃至永久终止提供本平台/本工具服务的权利。我们将在合理时间内将可能发生前述情形通过公告或邮件等合理方式告知您，您应当及时做好相应的调整和安排，但我们不因发生前述任何情形对您造成的任何损失承担任何责任。
2. 开源数据集的权利主张
为了便于您基于学习、研究、商业的目的开展数据集获取、使用等活动，我们对第三方原始数据集进行了必要的格式整合、数据清洗、标注、分类、注释等相关处理环节，形成可供本平台/本工具用户使用的开源数据集。
您知悉并理解，我们不对开源数据集主张知识产权中的相关财产性权利，因此我们亦无相应义务对开源数据集可能存在的知识产权进行主动识别和保护，但这不意味着我们放弃开源数据集主张署名权、发表权、修改权和保护作品完整权（如有）等人身性权利。而原始数据集可能存在的知识产权及相应合法权益由原权利人享有。
此外，向您开放和使用经合理编排、加工和处理后的开源数据集，并不意味着我们对原始数据集知识产权、信息内容等真实、准确或无争议的认可，您应当自行筛选、仔细甄别，使用经您选择的开源数据集。您知悉并同意，研究院对您自行选择使用的原始数据集不负有任何无缺陷或无瑕疵的承诺义务或担保责任。
3. 开源数据集的使用限制
您使用数据集不得侵害我们或任何第三方的合法权益（包括但不限于著作权、专利权、商标权等知识产权与其他权益）。
获取开源数据集后，您应确保对开源数据集的使用不超过原始数据集的权利人以公示或协议等形式明确规定的使用规则，包括原始数据的使用范围、目的和合法用途等。我们在此善意地提请您留意，如您对开源数据集的使用超出原始数据集的原定使用范围及用途，您可能面临侵犯原始数据集权利人的合法权益例如知识产权的风险，并可能承担相应的法律责任。
4. 个人信息保护
基于技术限制及开源数据集的公益性质等客观原因，我们无法保证开源数据集中不包含任何个人信息，我们不对开源数据集中可能涉及的个人信息承担任何法律责任。
如开源数据集涉及个人信息，我们不对您使用开源数据集可能涉及的任何个人信息处理行为承担法律责任。我们在此善意地提请您留意，您应依据《个人信息保护法》等相关法律法规的规定处理个人信息。
为了维护信息主体的合法权益、履行可能适用的法律、行政法规的规定，如您在使用开源数据集的过程中发现涉及或者可能涉及个人信息的内容，应立即停止对数据集中涉及个人信息部分的使用，并及时通过“6. 投诉与通知”中载明的联系我们。
5. 信息内容管理
我们不对开源数据集可能涉及的违法与不良信息承担任何法律责任。
如您在使用开源数据集的过程中发现开源数据集涉及或者可能涉及任何违法与不良信息，您应立即停止对数据集中涉及违法与不良信息部分的使用，并及时通过“6. 投诉与通知”中载明的联系我们。
6. 投诉与通知
如您认为开源数据集侵犯了您的合法权益，您可通过010-50955974联系我们，我们会及时依法处理您的主张与投诉。
为了处理您的主张和投诉，我们可能需要您提供联系方式、侵权证明材料以及身份证明等材料。请注意，如果您恶意投诉或陈述失实，您将承担由此造成的全部法律责任（包括但不限于合理的费用赔偿等）。
7. 责任声明
您理解并同意，基于开源数据集的性质，数据集中可能包含来自不同来源和贡献者的数据，其真实性、准确性、客观性等可能会有所差异，我们无法对任何数据集的可用性、可靠性等做出任何承诺。
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extra_gated_fields:
Name: text
Affiliation: text
Country: text
I agree to use this model for non-commercial use ONLY: checkbox
extra_gated_button_content: "Acknowledge license"
license: unknown
language:
- zh
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: task_type
struct:
- name: major
sequence: string
- name: minor
sequence: string
- name: domain
sequence: string
- name: other
dtype: string
- name: task_name_in_eng
dtype: string
- name: index
dtype: string
splits:
- name: train
num_bytes: 1053129000
num_examples: 744592
download_size: 416315627
dataset_size: 1053129000
---
# COIG Prompt Collection
## License
**Default Licensing for Sub-Datasets Without Specific License Declaration**: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declaration, the Apache License 2.0 (Apache-2.0) will be the applicable licensing terms by default.
**Precedence of Declared Licensing for Sub-Datasets**: For any sub-dataset within the COIG-PC Dataset that has an explicitly declared license, the terms and conditions of the declared license shall take precedence and govern the usage of that particular sub-dataset.
Users and developers utilizing the COIG-PC Dataset must ensure compliance with the licensing terms as outlined above. It is imperative to review and adhere to the specified licensing conditions of each sub-dataset, as they may vary.
## What is COIG-PC?
The COIG-PC Dataset is a meticulously curated and comprehensive collection of Chinese tasks and data, designed to facilitate the fine-tuning and optimization of language models for Chinese natural language processing (NLP). The dataset aims to provide researchers and developers with a rich set of resources to improve the capabilities of language models in handling Chinese text, which can be utilized in various fields such as text generation, information extraction, sentiment analysis, machine translation, among others.
If you think COIG-PC is too huge, please refer to [COIG-PC-Lite](https://huggingface.co/datasets/BAAI/COIG-PC-Lite) which is a subset of COIG-PC with only 200 samples from each task file.
## Why COIG-PC?
The COIG-PC Dataset is an invaluable resource for the domain of natural language processing (NLP) for various compelling reasons:
**Addressing Language Complexity**: Chinese is known for its intricacy, with a vast array of characters and diverse grammatical structures. A specialized dataset like COIG-PC, which is tailored for the Chinese language, is essential to adequately address these complexities during model training.
**Comprehensive Data Aggregation**: The COIG-PC Dataset is a result of an extensive effort in integrating almost all available Chinese datasets in the market. This comprehensive aggregation makes it one of the most exhaustive collections for Chinese NLP.
**Data Deduplication and Normalization**: The COIG-PC Dataset underwent rigorous manual processing to eliminate duplicate data and perform normalization. This ensures that the dataset is free from redundancy, and the data is consistent and well-structured, making it more user-friendly and efficient for model training.
**Fine-tuning and Optimization**: The dataset’s instruction-based phrasing facilitates better fine-tuning and optimization of language models. This structure allows models to better understand and execute tasks, which is particularly beneficial in improving performance on unseen or novel tasks.
The COIG-PC Dataset, with its comprehensive aggregation, meticulous selection, deduplication, and normalization of data, stands as an unmatched resource for training and optimizing language models tailored for the Chinese language and culture. It addresses the unique challenges of Chinese language processing and serves as a catalyst for advancements in Chinese NLP.
## Who builds COIG-PC?
The bedrock of COIG-PC is anchored in the dataset furnished by stardust.ai, which comprises an aggregation of data collected from the Internet.
And COIG-PC is the result of a collaborative effort involving engineers and experts from over twenty distinguished universities both domestically and internationally. Due to space constraints, it is not feasible to list all of them; however, the following are a few notable institutions among the collaborators:
- Beijing Academy of Artificial Intelligence, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/baai.png" alt= “BAAI” height="100" width="150">
- Peking University, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/pku.png" alt= “PKU” height="100" width="200">
- The Hong Kong University of Science and Technology (HKUST), China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/hkust.png" alt= “HKUST” height="100" width="200">
- The University of Waterloo, Canada
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/waterloo.png" alt= “Waterloo” height="100" width="150">
- The University of Sheffield, United Kingdom
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/sheffield.png" alt= “Sheffield” height="100" width="200">
- Beijing University of Posts and Telecommunications, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/bupt.png" alt= “BUPT” height="100" width="200">
- [Multimodal Art Projection](https://huggingface.co/m-a-p)
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/map.png" alt= “M.A.P” height="100" width="200">
- stardust.ai, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/stardust.png" alt= “stardust.ai” height="100" width="200">
- LinkSoul.AI, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/linksoul.png" alt= “linksoul.ai” height="100" width="200">
For the detailed list of engineers involved in the creation and refinement of COIG-PC, please refer to the paper that will be published subsequently. This paper will provide in-depth information regarding the contributions and the specifics of the dataset’s development process.
## How to use COIG-PC?
COIG-PC is structured in a **.jsonl** file format. Each line in the file represents a single data record and is structured in JSON (JavaScript Object Notation) format. Below is a breakdown of the elements within each line:
**instruction**: This is a text string that provides the instruction for the task. For example, it might tell the model what to do with the input data.
**input**: This is the input data that the model needs to process. In the context of translation, it would be the text that needs to be translated.
**output**: This contains the expected output data after processing the input. In the context of translation, it would be the translated text.
**split**: Indicates the official split of the original dataset, which is used to categorize data for different phases of model training and evaluation. It can be 'train', 'test', 'valid', etc.
**task_type**: Contains major and minor categories for the dataset. Major categories are broader, while minor categories can be more specific subcategories.
**domain**: Indicates the domain or field to which the data belongs.
**other**: This field can contain additional information or metadata regarding the data record. If there is no additional information, it may be set to null.
### Example
Here is an example of how a line in the COIG-PC dataset might be structured:
```
{
"instruction": "请把下面的中文句子翻译成英文",
"input": "我爱你。",
"output": "I love you.",
"split": "train",
"task_type": {
"major": ["翻译"],
"minor": ["翻译", "中译英"]
},
"domain": ["通用"],
"other": null
}
```
In this example:
**instruction** tells the model to translate the following Chinese sentence into English.
**input** contains the Chinese text "我爱你" which means "I love you".
**output** contains the expected translation in English: "I love you".
**split** indicates that this data record is part of the training set.
**task_type** specifies that the major category is "Translation" and the minor categories are "Translation" and "Chinese to English".
**domain** specifies that this data record belongs to the general domain.
**other** is set to null as there is no additional information for this data record.
## Update: Aug. 30, 2023
- v1.0: First version of COIG-PC-core.
## COIG-PC Citation
If you want to cite COIG-PC-core dataset, you could use this:
```
```
## Contact Us
To contact us feel free to create an Issue in this repository.
|
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codymlewis/nbaiot
|
codymlewis
| 2023-10-13T04:02:56Z | 31 | 0 |
[
"license:cc-by-4.0",
"arxiv:1805.03409",
"region:us"
] | null | 2023-09-20T02:24:15Z |
---
dataset_info:
features:
- name: features
sequence: float32
length: 115
- name: attack
dtype:
class_label:
names:
'0': benign_traffic
'1': combo
'2': junk
'3': mirai-ack
'4': mirai-scan
'5': mirai-syn
'6': mirai-udp
'7': mirai-udpplain
'8': scan
'9': tcp
'10': udp
- name: device
dtype:
class_label:
names:
'0': Danmini_Doorbell
'1': Ecobee_Thermostat
'2': Ennio_Doorbell
'3': Philips_B120N10_Baby_Monitor
'4': Provision_PT_737E_Security_Camera
'5': Provision_PT_838_Security_Camera
'6': Samsung_SNH_1011_N_Webcam
'7': SimpleHome_XCS7_1002_WHT_Security_Camera
'8': SimpleHome_XCS7_1003_WHT_Security_Camera
splits:
- name: train
num_bytes: 2857231888
num_examples: 6002588
- name: test
num_bytes: 504568568
num_examples: 1060018
download_size: 1772922927
dataset_size: 3361800456
license: cc-by-4.0
pretty_name: nbaiot
---
# Dataset Card for N-BAIoT
*From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot:* This dataset addresses the lack of public botnet datasets, especially for the IoT. It suggests *real* traffic data, gathered from 9 commercial IoT devices authentically infected by Mirai and BASHLITE.
## Dataset Details
### Dataset Description
*From https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot:*
(a) Attribute being predicted:
-- Originally we aimed at distinguishing between benign and Malicious traffic data by means of anomaly detection techniques.
-- However, as the malicious data can be divided into 10 attacks carried by 2 botnets, the dataset can also be used for multi-class classification: 10 classes of attacks, plus 1 class of 'benign'.
(b) The study's results:
-- For each of the 9 IoT devices we trained and optimized a deep autoencoder on 2/3 of its benign data (i.e., the training set of each device). This was done to capture normal network traffic patterns.
-- The test data of each device comprised of the remaining 1/3 of benign data plus all the malicious data. On each test set we applied the respective trained (deep) autoencoder as an anomaly detector. The detection of anomalies (i.e., the cyberattacks launched from each of the above IoT devices) concluded with 100% TPR.
- **Curated by:** Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, , Asaf, and Shabtai, Asaf
- **License:** [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/legalcode)
### Dataset Sources
- **Repository:** https://archive.ics.uci.edu/dataset/442/detection+of+iot+botnet+attacks+n+baiot
- **Paper:** https://arxiv.org/abs/1805.03409
## Citation
**BibTeX:**
@misc{misc_detection_of_iot_botnet_attacks_n_baiot_442,
author = {Meidan,Yair, Bohadana,Michael, Mathov,Yael, Mirsky,Yisroel, Breitenbacher,Dominik, ,Asaf, and Shabtai,Asaf},
title = {{N-BaIoT Dataset to Detect IoT Botnet Attacks}},
year = {2018},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5RC8J}
}
**APA:**
Meidan, Yair, Bohadana, Michael, Mathov, Yael, Mirsky, Yisroel, Breitenbacher, Dominik, ,Asaf, and Shabtai, Asaf. (2018). N-BaIoT Dataset to Detect IoT Botnet Attacks. UCI Machine Learning Repository. https://doi.org/10.24432/C5RC8J.
## Glossary [optional]
- **IoT**: Internet of Things
- **Botnet**: A collection of devices that are maliciously controlled via malware
|
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cnut1648/ScienceQA-LLAVA
|
cnut1648
| 2023-10-22T00:49:42Z | 31 | 0 |
[
"region:us"
] | null | 2023-09-24T04:07:31Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: choice
dtype: string
- name: answer
dtype: string
- name: lecture
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 425066440.932
num_examples: 12726
- name: validation
num_bytes: 141104381.824
num_examples: 4241
- name: test
num_bytes: 139230285.176
num_examples: 4241
download_size: 681887955
dataset_size: 705401107.932
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "ScienceQA-LLAVA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Dahoas/hf_cot_gsm8k
|
Dahoas
| 2023-10-01T14:40:46Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-01T09:45:46Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 8663589
num_examples: 7217
- name: val
num_bytes: 301562
num_examples: 256
- name: test
num_bytes: 1610805
num_examples: 1319
download_size: 5575205
dataset_size: 10575956
---
# Dataset Card for "hf_cot_gsm8k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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SinclairSchneider/time_magazine
|
SinclairSchneider
| 2023-10-18T14:32:37Z | 31 | 0 |
[
"size_categories:10K<n<100K",
"language:en",
"license:unknown",
"news",
"newspaper",
"time magazine",
"politics",
"Business",
"Entertainment",
"Health",
"History",
"Ideas",
"Politics",
"Press-room",
"Tech",
"US",
"World",
"region:us"
] | null | 2023-10-18T14:28:11Z |
---
license: unknown
dataset_info:
features:
- name: category
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 117207060
num_examples: 38189
download_size: 72886361
dataset_size: 117207060
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- en
tags:
- news
- newspaper
- time magazine
- politics
- Business
- Entertainment
- Health
- History
- Ideas
- Politics
- Press-room
- Tech
- US
- World
size_categories:
- 10K<n<100K
---
|
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] |
davidfant/natural-questions-chunk-0
|
davidfant
| 2023-10-22T22:48:50Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-22T22:45:10Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4705302627
num_examples: 10000
download_size: 1826111395
dataset_size: 4705302627
---
# Dataset Card for "natural-questions-chunk-0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
davidfant/natural-questions-chunk-1
|
davidfant
| 2023-10-22T22:52:24Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-22T22:48:50Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4690314797
num_examples: 10000
download_size: 1819108926
dataset_size: 4690314797
---
# Dataset Card for "natural-questions-chunk-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
davidfant/natural-questions-chunk-2
|
davidfant
| 2023-10-22T22:56:01Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-22T22:52:24Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4672087643
num_examples: 10000
download_size: 1816142719
dataset_size: 4672087643
---
# Dataset Card for "natural-questions-chunk-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
davidfant/natural-questions-chunk-3
|
davidfant
| 2023-10-22T22:59:30Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-22T22:56:01Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4591162424
num_examples: 10000
download_size: 1782588663
dataset_size: 4591162424
---
# Dataset Card for "natural-questions-chunk-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
davidfant/natural-questions-chunk-4
|
davidfant
| 2023-10-22T23:03:02Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-22T22:59:31Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4529920148
num_examples: 10000
download_size: 1759288585
dataset_size: 4529920148
---
# Dataset Card for "natural-questions-chunk-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nlplabtdtu/multi-choices-text
|
nlplabtdtu
| 2023-11-09T10:07:38Z | 31 | 0 |
[
"region:us"
] | null | 2023-10-27T06:35:45Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: options
list:
- name: answer
dtype: string
- name: key
dtype: string
- name: answer
struct:
- name: answer
dtype: string
- name: key
dtype: string
- name: solution
dtype: string
- name: type
dtype: string
- name: alnum_start
dtype: bool
- name: prompt
dtype: string
- name: response
dtype: string
- name: grade
dtype: string
- name: subject
dtype: string
- name: prompt_type
dtype: string
splits:
- name: train
num_bytes: 93596608
num_examples: 58286
download_size: 48223987
dataset_size: 93596608
---
# Dataset Card for "multi-choices-text"
Bộ dữ liệu trắc nghiệm gồm 58,290 dòng từ vungoi. Bộ này có một số đặc điểm sau:
```
- Các câu hỏi đều là câu hỏi hoàn chỉnh với "?" cuối câu
- Các câu hỏi tiếng Anh đều đã bị bỏ qua
- Các phần "Đáp án.*[ABCD]" của field "solution" bị thay bằng ""
- Đã bỏ "." ở từng "answer" của "options" và cả "solution". Chủ yếu để dễ làm prompt.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
YousufEjaz/phishing_legitimate
|
YousufEjaz
| 2023-11-03T09:11:27Z | 31 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-03T06:03:36Z |
---
license: mit
---
|
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feedback-to-code/Corona-Warn-App-1
|
feedback-to-code
| 2023-11-06T12:12:23Z | 31 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-06T08:34:10Z |
---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/tasks/cwa-app-ios-tasks-everything.jsonl
- split: test
path: data/tasks/cwa-app-ios-tasks.jsonl
---
|
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HaiderSultanArc/MT-Urdu-English
|
HaiderSultanArc
| 2023-11-07T23:29:35Z | 31 | 1 |
[
"task_categories:translation",
"task_categories:text2text-generation",
"size_categories:1M<n<10M",
"language:en",
"language:ur",
"license:mit",
"region:us"
] |
[
"translation",
"text2text-generation"
] | 2023-11-07T22:22:00Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: en
dtype: string
- name: ur
dtype: string
splits:
- name: train
num_bytes: 1121540748.544574
num_examples: 5646138
- name: test
num_bytes: 280385286.4554259
num_examples: 1411535
download_size: 873598433
dataset_size: 1401926035
task_categories:
- translation
- text2text-generation
language:
- en
- ur
pretty_name: English Urdu Pairs for Machine Translation
size_categories:
- 1M<n<10M
---
|
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] |
thangvip/orca-processes
|
thangvip
| 2023-11-08T03:11:09Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-08T03:11:05Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 31950704.438731268
num_examples: 32860
download_size: 11256640
dataset_size: 31950704.438731268
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "orca-processes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kowndinya23/flan2022
|
kowndinya23
| 2023-11-09T07:10:33Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-09T06:49:11Z |
---
configs:
- config_name: default
data_files:
- split: flan2021
path: data/flan2021-*
- split: t0
path: data/t0-*
- split: cot
path: data/cot-*
- split: niv2
path: data/niv2-*
- split: dialog
path: data/dialog-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: template_type
dtype: string
splits:
- name: flan2021
num_bytes: 8988026240
num_examples: 5362361
- name: t0
num_bytes: 4602180562
num_examples: 1650308
- name: cot
num_bytes: 209004809
num_examples: 183848
- name: niv2
num_bytes: 13104211362
num_examples: 10066896
- name: dialog
num_bytes: 1024507265
num_examples: 553869
download_size: 16511300644
dataset_size: 27927930238
---
# Dataset Card for "flan2022"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
celsowm/ambito_juridico_artigos
|
celsowm
| 2023-12-03T15:57:14Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-12T12:24:47Z |
---
dataset_info:
features:
- name: titulo
dtype: string
- name: resumo
dtype: string
- name: categoria
dtype: string
- name: texto
dtype: string
- name: link
dtype: string
splits:
- name: train
num_bytes: 390296460
num_examples: 11373
download_size: 198397380
dataset_size: 390296460
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ambito_juridico_artigos"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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anumafzal94/pubmed-2shot-4096
|
anumafzal94
| 2023-11-17T10:45:39Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-15T13:47:10Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: few-shot
dtype: bool
splits:
- name: test
num_bytes: 8149116.593446602
num_examples: 426
- name: train
num_bytes: 139802654.7469022
num_examples: 7242
download_size: 20828412
dataset_size: 147951771.3403488
---
# Dataset Card for "pubmed-2shot-4096"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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