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 |
|---|---|---|---|---|---|---|---|---|---|
jetaudio/tieuthuyet
|
jetaudio
| 2023-11-25T06:11:03Z | 31 | 1 |
[
"language:vi",
"region:us"
] | null | 2023-11-16T06:49:22Z |
---
language:
- vi
dataset_info:
features:
- name: name
dtype: string
- name: genres
sequence: string
- name: chapter
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 20727425500
num_examples: 1588802
download_size: 10580460566
dataset_size: 20727425500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tieuthuyet"
A raw dataset scraped from Internet, contain 1.5m novel chapters in Vietnamese
|
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empbetty/dog-similar-to-tangyuan-dataset
|
empbetty
| 2023-11-16T11:08:55Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-16T11:08:53Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2926472.0
num_examples: 105
download_size: 2926339
dataset_size: 2926472.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dog-similar-to-tangyuan-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Xnhyacinth/NQ-Image
|
Xnhyacinth
| 2023-11-22T15:41:35Z | 31 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T09:42:49Z |
---
license: mit
dataset_info:
- config_name: ctxs1
features:
- name: id
dtype: int64
- name: answers
sequence: string
- name: question
dtype: string
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struct:
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dtype: int64
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dtype: int64
- name: ratio
dtype: string
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list:
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splits:
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num_bytes: 5212377086
num_examples: 79168
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num_bytes: 576466670
num_examples: 8757
- name: test
num_bytes: 238448436
num_examples: 3610
download_size: 3334114023
dataset_size: 6027292192
- config_name: ctxs100
features:
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dtype: string
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struct:
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list:
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splits:
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num_examples: 79168
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num_examples: 3610
download_size: 3413758169
dataset_size: 6147292667
- config_name: ctxs5
features:
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sequence: string
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struct:
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dtype: int64
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dtype: int64
- name: ratio
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list:
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splits:
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num_examples: 79168
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num_bytes: 594986589
num_examples: 8757
- name: test
num_bytes: 246104192
num_examples: 3610
download_size: 3408308518
dataset_size: 6220570567
configs:
- config_name: ctxs1
data_files:
- split: train
path: ctxs1/train-*
- split: eval
path: ctxs1/eval-*
- split: test
path: ctxs1/test-*
- config_name: ctxs100
data_files:
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path: ctxs100/train-*
- split: eval
path: ctxs100/eval-*
- split: test
path: ctxs100/test-*
- config_name: ctxs5
data_files:
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path: ctxs5/train-*
- split: eval
path: ctxs5/eval-*
- split: test
path: ctxs5/test-*
---
|
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Anwaarma/MySentimentAnwarBig
|
Anwaarma
| 2023-11-21T10:32:07Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-21T10:32:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Positive
splits:
- name: train
num_bytes: 3402656.0
num_examples: 14666
- name: test
num_bytes: 271618.95179553545
num_examples: 1080
download_size: 1986961
dataset_size: 3674274.9517955356
---
# Dataset Card for "MySentimentAnwarBig"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Siki-77/amazon6_5core_polarity
|
Siki-77
| 2023-11-23T14:44:42Z | 31 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T12:29:07Z |
---
license: apache-2.0
---
data source：https://cseweb.ucsd.edu/~jmcauley/datasets/amazon_v2/
we construct a new dataset Amazon reviews (Ni et al., 2019) on data aggregated over six genres 5core: beauty, fashion, appliances, giftcards, magazines, and software.
cite:
Jianmo Ni, Jiacheng Li, and Julian McAuley. Justifying recommendations using distantly-labeled
reviews and fine-grained aspects. In Empirical Methods in Natural Language Processing and
International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019. URL
https://www.aclweb.org/anthology/D19-1018.
|
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plaguss/go_emotions_raw
|
plaguss
| 2023-11-24T08:44:41Z | 31 | 0 |
[
"size_categories:10K<n<100K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-11-23T18:18:33Z |
---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for go_emotions_raw
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("plaguss/go_emotions_raw")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("plaguss/go_emotions_raw")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | text | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| label | Label | multi_label_selection | True | Classify the text by selecting the correct label from the given list of labels. | ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'] |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
},
"metadata": {},
"responses": [
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000001",
"values": {
"label": {
"value": [
"neutral"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000016",
"values": {
"label": {
"value": [
"anger",
"annoyance",
"optimism"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000028",
"values": {
"label": {
"value": [
"approval"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000039",
"values": {
"label": {
"value": [
"neutral"
]
}
}
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000048",
"values": {
"label": {
"value": [
"annoyance"
]
}
}
}
],
"suggestions": [
{
"agent": null,
"question_name": "label",
"score": null,
"type": "human",
"value": [
"annoyance",
"neutral"
]
}
],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"label": [
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000001",
"value": [
"neutral"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000016",
"value": [
"anger",
"annoyance",
"optimism"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000028",
"value": [
"approval"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000039",
"value": [
"neutral"
]
},
{
"status": "submitted",
"user_id": "00000000-0000-0000-0000-000000000048",
"value": [
"annoyance"
]
}
],
"label-suggestion": [
"annoyance",
"neutral"
],
"label-suggestion-metadata": {
"agent": null,
"score": null,
"type": "human"
},
"metadata": "{}",
"text": " \"If you don\u0027t wear BROWN AND ORANGE...YOU DON\u0027T MATTER!\" We need a tshirt with that on it asap! "
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **label** is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'], and description "Classify the text by selecting the correct label from the given list of labels.".
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **label-suggestion** is of type `multi_label_selection` with the following allowed values ['admiration', 'amusement', 'anger', 'annoyance', 'approval', 'caring', 'confusion', 'curiosity', 'desire', 'disappointment', 'disapproval', 'disgust', 'embarrassment', 'excitement', 'fear', 'gratitude', 'grief', 'joy', 'love', 'nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'].
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Script used for the generation
```python
import argilla as rg
from datasets import load_dataset
import uuid
from datasets import concatenate_datasets
ds = load_dataset("go_emotions", "raw", split="train")
ds_prepared = load_dataset("go_emotions")
_CLASS_NAMES = [
"admiration",
"amusement",
"anger",
"annoyance",
"approval",
"caring",
"confusion",
"curiosity",
"desire",
"disappointment",
"disapproval",
"disgust",
"embarrassment",
"excitement",
"fear",
"gratitude",
"grief",
"joy",
"love",
"nervousness",
"optimism",
"pride",
"realization",
"relief",
"remorse",
"sadness",
"surprise",
"neutral",
]
label_to_id = {label: i for i, label in enumerate(_CLASS_NAMES)}
id_to_label = {i: label for i, label in enumerate(_CLASS_NAMES)}
# Concatenate the datasets and transform to pd.DataFrame
ds_prepared = concatenate_datasets([ds_prepared["train"], ds_prepared["validation"], ds_prepared["test"]])
df_prepared = ds_prepared.to_pandas()
# Obtain the final labels as a dict, to later include these as suggestions
labels_prepared = {}
for idx in df_prepared.index:
labels = [id_to_label[label_id] for label_id in df_prepared['labels'][idx]]
labels_prepared[df_prepared['id'][idx]] = labels
# Add labels to the dataset and keep only the relevant columns
def add_labels(ex):
labels = []
for label in _CLASS_NAMES:
if ex[label] == 1:
labels.append(label)
ex["labels"] = labels
return ex
ds = ds.map(add_labels)
df = ds.select_columns(["text", "labels", "rater_id", "id"]).to_pandas()
# Create a FeedbackDataset for text classification
feedback_dataset = rg.FeedbackDataset.for_text_classification(labels=_CLASS_NAMES, multi_label=True)
# Create the records with the original responses, and use as suggestions
# the final labels in the "simplified" go_emotions dataset.
records = []
for text, df_text in df.groupby("text"):
responses = []
for rater_id, df_raters in df_text.groupby("rater_id"):
responses.append(
{
"values": {"label": {"value": df_raters["labels"].iloc[0].tolist()}},
"status": "submitted",
"user_id": uuid.UUID(int=rater_id),
}
)
suggested_labels = labels_prepared.get(df_raters["id"].iloc[0], None)
if not suggested_labels:
continue
suggestion = [
{
"question_name": "label",
"value": suggested_labels,
"type": "human",
}
]
records.append(
rg.FeedbackRecord(
fields={"text": df_raters["text"].iloc[0]},
responses=responses,
suggestions=suggestion
)
)
feedback_dataset.add_records(records)
# Push to the hub
feedback_dataset.push_to_huggingface("plaguss/go_emotions_raw")
```
### 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 guidelines
This is a text classification dataset that contains texts and labels. Given a set of texts and a predefined set of labels, the goal of text classification is to assign one or more labels to each text based on its content. Please classify the texts by making the correct selection.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
OrdalieTech/Ordalie-FR-STS-benchmark
|
OrdalieTech
| 2023-11-27T17:32:55Z | 31 | 0 |
[
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"language:fr",
"license:apache-2.0",
"region:us"
] |
[
"feature-extraction"
] | 2023-11-25T15:01:49Z |
---
language:
- fr
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- feature-extraction
pretty_name: ordalie-fr-sts-benchmark
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 14934570
num_examples: 10000
download_size: 9328832
dataset_size: 14934570
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Ordalie - French STS Benchmark
- 30k sentence pairs
- Score either 0 or 1
|
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] |
arieg/fma_small_images
|
arieg
| 2023-11-25T19:07:58Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-25T19:07:26Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '000002'
'1': '000005'
'2': '000010'
'3': '000140'
'4': '000141'
'5': 000148
'6': 000182
'7': 000190
'8': 000193
'9': 000194
'10': 000197
'11': '000200'
'12': '000203'
'13': '000204'
'14': '000207'
'15': '000210'
'16': '000211'
'17': '000212'
'18': '000213'
'19': '000255'
'20': '000256'
'21': 000368
'22': '000424'
'23': 000459
'24': '000534'
'25': '000540'
'26': '000546'
'27': '000574'
'28': '000602'
'29': '000615'
'30': '000620'
'31': '000621'
'32': '000625'
'33': '000666'
'34': '000667'
'35': '000676'
'36': 000690
'37': 000694
'38': 000695
'39': '000704'
'40': '000705'
'41': '000706'
'42': '000707'
'43': 000708
'44': 000709
'45': '000714'
'46': '000715'
'47': '000716'
'48': 000718
'49': '000777'
'50': 000814
'51': 000821
'52': 000822
'53': 000825
'54': 000853
'55': 000890
'56': 000892
'57': 000897
'58': 000993
'59': 000995
'60': 000997
'61': 000998
'62': 001039
'63': '001040'
'64': '001066'
'65': 001069
'66': '001073'
'67': '001075'
'68': 001082
'69': 001083
'70': 001087
'71': '001102'
'72': 001193
'73': 001195
'74': 001196
'75': 001197
'76': 001249
'77': 001259
'78': '001270'
'79': '001276'
'80': '001277'
'81': 001278
'82': '001417'
'83': '001427'
'84': '001443'
'85': 001482
'86': '001510'
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'88': '001642'
'89': '001644'
'90': 001649
'91': '001661'
'92': '001663'
'93': '001666'
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'95': 001680
'96': 001681
'97': 001682
'98': 001683
'99': 001684
'100': 001685
'101': 001686
'102': 001687
'103': 001688
'104': 001689
'105': '001701'
'106': '001702'
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'108': '001704'
'109': '001706'
'110': '001720'
'111': '001732'
'112': '001733'
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'115': 001883
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'120': 001929
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'122': '002012'
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'125': 002099
'126': '003263'
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splits:
- name: train
num_bytes: 440849403.831
num_examples: 7997
download_size: 432687499
dataset_size: 440849403.831
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
zinc75/Vibravox_dummy
|
zinc75
| 2023-11-29T16:14:19Z | 31 | 0 |
[
"task_categories:audio-to-audio",
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:text-to-speech",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:fr",
"license:cc-by-4.0",
"region:us"
] |
[
"audio-to-audio",
"automatic-speech-recognition",
"audio-classification",
"text-to-speech"
] | 2023-11-28T12:33:43Z |
---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- fr
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- audio-to-audio
- automatic-speech-recognition
- audio-classification
- text-to-speech
task_ids:
- speaker-identification
pretty_name: VibraVox
configs:
- config_name: ASR_Headworn_mouth_microphone
data_files:
- split: train
path: ASR_Headworn_mouth_microphone/train-*
- split: validation
path: ASR_Headworn_mouth_microphone/validation-*
- split: test
path: ASR_Headworn_mouth_microphone/test-*
- config_name: ASR_Larynx_microphone
data_files:
- split: train
path: ASR_Larynx_microphone/train-*
- split: validation
path: ASR_Larynx_microphone/validation-*
- split: test
path: ASR_Larynx_microphone/test-*
- config_name: BWE_Larynx_microphone
data_files:
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path: BWE_Larynx_microphone/train-*
- split: validation
path: BWE_Larynx_microphone/validation-*
- split: test
path: BWE_Larynx_microphone/test-*
dataset_info:
- config_name: ASR_Headworn_mouth_microphone
features:
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dtype: audio
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dtype: string
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download_size: 0
dataset_size: 6734942.0
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features:
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features:
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mono: false
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---
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BAAI/CCI-Data
|
BAAI
| 2023-11-29T08:23:42Z | 31 | 37 |
[
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:zh",
"region:us"
] |
[
"text-generation"
] | 2023-11-29T08:10:39Z |
---
task_categories:
- text-generation
language:
- zh
size_categories:
- 10M<n<100M
---
## Data Description
With the rapid development of large language models, the demand for high-quality datasets in both the industry and academia is growing. These datasets not only need to contain a vast amount of information but also require rigorous screening and cleaning to ensure their accuracy and the safety of downstream models and applications. However, the currently popular public datasets in the industry have certain quality and security risks, especially in the Chinese domain where high-quality datasets are particularly lacking. Moreover, constructing a safe Chinese dataset also faces many challenges. Therefore, building a dataset that has undergone strict screening and standardized processing is particularly important for the innovation and development of LLMs.
Our CCI (Chinese Corpora Internet) dataset consists of high-quality, trustworthy sources from internet sites within mainland China. It has undergone rigorous data cleaning and deduplication, with targeted detection and filtering in aspects of content quality. The rules for data processing include:
- Rule-based filtering: density-based extraction, keyword filtering, spam information filtering, conversion between simplified and traditional Chinese, etc.
- Model-based filtering: filtering of low-quality content by training a classification model
- Deduplication: within and between datasets dedup
Additionally, in response to the issue of pre-training data being large in scale and prone to causing leaks of evaluation data, we specifically conduct rigorous screening and filtering of several current mainstream Chinese evaluation datasets during the data processing phase.
The CCI corpus released (CCI v1.0.0) is 104GB in size. The overall timespan of the dataset ranges from January 2001 to November 2023.
## Update
- November 29, 2023, CCI v1.0.0 released!
## Data Format
| Field | Type | Meaning |
| :-: | :-: | :-: |
| id | String | Document ID, globally unique |
| title | String | Document title |
| content | String | Content of the document |
## Sample
```json
{
"id": "a262c26c915762ae107019f2797fda03",
"title": "深圳人工智能企业闪耀东京展会",
"content": "拳头产品叫好又叫座 深圳人工智能企业闪耀东京展会 近日在东京举行的日本人工智能展上,由深圳市贸促委组织的深圳人工智能企业展团集中亮相,引起热烈关注。该展会是日本规模最大的人工智能展会,云鲸智能、思谋科技、魔耳智能、格瑞普电池、云译科技等近20家深圳人工智能代表性企业的最新人工智能产品吸引了众多当地专业观众的目光,成为展会上的一抹亮色。企业现场“揽单”,参展成果丰硕深圳市大象机器人科技有限公司是一家由海外留学人才来深创建的专注于机器人研发生产的专精特新企业,本次在东京,该公司重点展示了myCobot协作机器人和仿真宠物猫metacat等公司拳头产品。“参展期间我们接待客户数达到500位以上,有意愿成为分销伙伴、集成商或终端客户的有效意向客户近70人,成效相当不错。……"
}
```
## Download
The CCI dataset is simultaneously open-sourced on the [BAAI DataHub](https://data.baai.ac.cn/data) and Huggingface.
### BAAI DataHub
Users can click the link [CCI Dataset](https://data.baai.ac.cn/details/BAAI-CCI) to view the data files, and click to download.
Note that users need to register on BAAI DataHub to use the data, and filling out a survey questionnaire is required before their first download.
### Huggingface
To use the data, you can load it using the following code:
```python
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("BAAI/CCI-Data")
```
## User Agreement
Users need to comply with the usage agreement of the CCI dataset. You can view the agreement by clicking on the following link: （[View Usage Agreement](https://data.baai.ac.cn/resources/agreement/cci_usage_aggrement.pdf)）.
## Notice
If you have any questions related to this dataset, please contact [email protected].
|
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] |
Tianduo/gsm8k-llama-2-7b-sft-dpo
|
Tianduo
| 2023-11-29T08:34:45Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-29T08:34:40Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question
dtype: string
- name: gold_ans
dtype: string
- name: positives
sequence: string
- name: negatives
sequence: string
splits:
- name: train
num_bytes: 12187984
num_examples: 7473
download_size: 5539563
dataset_size: 12187984
---
# Dataset Card for "gsm8k-llama-2-7b-sft-dpo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Deojoandco/capstone_fromgpt_without_gold_v9
|
Deojoandco
| 2023-11-29T08:52:53Z | 31 | 0 |
[
"region:us"
] | null | 2023-11-29T08:52:50Z |
---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
splits:
- name: test
num_bytes: 14648
num_examples: 12
download_size: 17112
dataset_size: 14648
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ivillar/mdb-mc-mtt-spec-vocals-pix2pix
|
ivillar
| 2023-12-01T13:35:17Z | 31 | 0 |
[
"region:us"
] | null | 2023-12-01T13:29:13Z |
---
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 1711537993.909
num_examples: 22253
- name: test
num_bytes: 756125660.586
num_examples: 9871
- name: valid
num_bytes: 123962115.9
num_examples: 1601
download_size: 2606467417
dataset_size: 2591625770.395
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
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erishabh/unit-test-v1
|
erishabh
| 2023-12-03T04:59:28Z | 31 | 0 |
[
"region:us"
] | null | 2023-12-03T03:02:31Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 59084973
num_examples: 2470
download_size: 9425669
dataset_size: 59084973
---
# Dataset Card for "unit-test-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ncduy/mt-en-vi
|
ncduy
| 2022-10-22T15:08:45Z | 30 | 4 |
[
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:own",
"source_datasets:open_subtitles",
"source_datasets:tatoeba",
"source_datasets:opus_tedtalks",
"source_datasets:qed_amara",
"source_datasets:opus_wikipedia",
"language:en",
"language:vi",
"license:mit",
"region:us"
] |
[
"conditional-text-generation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
- vi
license:
- mit
multilinguality:
- translation
pretty_name: "Machine Translation Paired English-Vietnamese Sentences"
size_categories:
- 1M<n<10M
source_datasets:
- own
- open_subtitles
- tatoeba
- opus_tedtalks
- qed_amara
- opus_wikipedia
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
---
# Dataset Card for Machine Translation Paired English-Vietnamese Sentences
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language of the dataset text sentence is English ('en') and Vietnamese (`vi`).
## Dataset Structure
### Data Instances
An instance example:
```
{
'en': 'And what I think the world needs now is more connections.',
'vi': 'Và tôi nghĩ điều thế giới đang cần bây giờ là nhiều sự kết nối hơn.',
'source': 'TED2020 v1'
}
```
### Data Fields
- `en` (str): English sentence
- `vi` (str): Vietnamese sentence
- `source` (str): Source.
### Data Splits
The dataset is split in train, validation and test.
| | Tain | Validation | Test |
|--------------------|------:|-----------:|-----:|
| Number of examples |2884451| 11316| 11225|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@ncduy0303](https://github.com/ncduy0303) for adding this dataset.
|
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] |
pragnakalp/squad_v2_french_translated
|
pragnakalp
| 2022-08-29T07:49:15Z | 30 | 1 |
[
"multilinguality:monolingual",
"multilinguality:translation",
"language:fr",
"license:apache-2.0",
"region:us"
] | null | 2022-04-04T05:44:07Z |
---
language: fr
license: apache-2.0
multilinguality:
- monolingual
- translation
---
Using Google Translation, we have translated SQuAD 2.0 dataset into multiple languages.
Here is the translated dataset of SQuAD 2.0 in French language.
Shared by [Pragnakalp Techlabs](https://www.pragnakalp.com)
|
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] |
bigscience-data/roots_ar_wikipedia
|
bigscience-data
| 2022-12-12T11:00:43Z | 30 | 1 |
[
"language:ar",
"license:cc-by-sa-3.0",
"region:us"
] | null | 2022-05-18T09:06:35Z |
---
language: ar
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ar_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
|
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] |
malteos/wechsel_de
|
malteos
| 2022-07-30T18:57:02Z | 30 | 0 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"size_categories:100k<n<1M",
"language:de",
"region:us"
] |
[
"text-generation"
] | 2022-06-24T08:13:38Z |
---
language:
- de
task_categories:
- text-generation
size_categories:
- 100k<n<1M
task_ids:
- language-modeling
- masked-language-modeling
---
German validation dataset from WECHSEL () to evaluate LLM perplexity.
JSON-line files (on JSON object per line):
- `valid.json.gz`: Gzipped validation set as generated by the paper (163,698 docs)
- `valid.random_1636.json.gz`: Random 1% (1636 docs) of the validation set
|
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] |
SocialGrep/reddit-r-bitcoin-data-for-jun-2022
|
SocialGrep
| 2022-07-25T18:22:16Z | 30 | 2 |
[
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-25T18:11:58Z |
---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
---
# Dataset Card for reddit-r-bitcoin-data-for-jun-2022
## 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)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/reddit-r-bitcoin-data-for-jun-2022?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Reddit downloader used:** [https://socialgrep.com/exports](https://socialgrep.com/exports?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022)
### Dataset Summary
Lite version of our premium [Reddit /r/Bitcoin dataset](https://socialgrep.com/datasets/the-reddit-r-bitcoin-dataset?utm_source=huggingface&utm_medium=link&utm_campaign=redditrbitcoindataforjun2022) - CSV of all posts & comments to the /r/Bitcoin subreddit over Jun 2022.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## Additional Information
### Licensing Information
CC-BY v4.0
|
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] |
alex-apostolo/filtered-cuad
|
alex-apostolo
| 2022-08-04T06:24:04Z | 30 | 1 |
[
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:cuad",
"language:en",
"license:cc-by-4.0",
"arxiv:2103.06268",
"region:us"
] |
[
"question-answering"
] | 2022-08-03T15:59:24Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- cuad
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: cuad
pretty_name: CUAD
train-eval-index:
- config: default
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: test
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: cuad
name: CUAD
---
# Dataset Card for filtered_cuad
## 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:** [Contract Understanding Atticus Dataset](https://www.atticusprojectai.org/cuad)
- **Repository:** [Contract Understanding Atticus Dataset](https://github.com/TheAtticusProject/cuad/)
- **Paper:** [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://arxiv.org/abs/2103.06268)
- **Point of Contact:** [Atticus Project Team]([email protected])
### Dataset Summary
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 commercial legal contracts that have been manually labeled to identify 41 categories of important clauses that lawyers look for when reviewing contracts in connection with corporate transactions. This dataset is a filtered version of CUAD. It excludes legal contracts with an Agreement date prior to 2002 and contracts which are not Business to Business. From the 41 categories we filtered them down to 12 which we considered the most crucial.
We wanted a small dataset to quickly fine-tune different models without sacrificing the categories which we deemed as important. The need to remove most questions was due to them not having an answer which is problematic since it can scue the resulting metrics such as the F1 score and the AUPR curve.
CUAD is curated and maintained by The Atticus Project, Inc. to support NLP research and development in legal contract review. Analysis of CUAD can be found at https://arxiv.org/abs/2103.06268. Code for replicating the results and the trained model can be found at https://github.com/TheAtticusProject/cuad.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in English only.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [44],
"text": ['DISTRIBUTOR AGREEMENT']
},
"context": 'EXHIBIT 10.6\n\n DISTRIBUTOR AGREEMENT\n\n THIS DISTRIBUTOR AGREEMENT (the "Agreement") is made by and between Electric City Corp., a Delaware corporation ("Company") and Electric City of Illinois LLC ("Distributor") this 7th day of September, 1999...',
"id": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT__Document Name_0",
"question": "Highlight the parts (if any) of this contract related to "Document Name" that should be reviewed by a lawyer. Details: The name of the contract",
"title": "LIMEENERGYCO_09_09_1999-EX-10-DISTRIBUTOR AGREEMENT"
}
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
This dataset is split into train/test set. Number of samples in each set is given below:
| | Train | Test |
| ----- | ------ | ---- |
| CUAD | 5442 | 936 |
## Dataset Creation
### Curation Rationale
A highly valuable specialized task without a public large-scale dataset is contract review, which costs humans substantial time, money, and attention. Many law firms spend approximately 50% of their time reviewing contracts (CEB, 2017). Due to the specialized training necessary to understand and interpret contracts, the billing rates for lawyers at large law firms are typically around $500-$900 per hour in the US. As a result, many transactions cost companies hundreds of thousands of dollars just so that lawyers can verify that there are no problematic obligations or requirements included in the contracts. Contract review can be a source of drudgery and, in comparison to other legal tasks, is widely considered to be especially boring.
Contract review costs also affect consumers. Since contract review costs are so prohibitive, contract review is not often performed outside corporate transactions. Small companies and individuals consequently often sign contracts without even reading them, which can result in predatory behavior that harms consumers. Automating contract review by openly releasing high-quality data and fine-tuned models can increase access to legal support for small businesses and individuals, so that legal support is not exclusively available to wealthy companies.
To reduce the disparate societal costs of contract review, and to study how well NLP models generalize to specialized domains, the authors introduced a new large-scale dataset for contract review. As part of The Atticus Project, a non-profit organization of legal experts, CUAD is introduced, the Contract Understanding Atticus Dataset. This dataset was created with a year-long effort pushed forward by dozens of law student annotators, lawyers, and machine learning researchers. The dataset includes more than 500 contracts and more than 13,000 expert annotations that span 41 label categories. For each of 41 different labels, models must learn to highlight the portions of a contract most salient to that label. This makes the task a matter of finding needles in a haystack.
### Source Data
#### Initial Data Collection and Normalization
The CUAD includes commercial contracts selected from 25 different types of contracts based on the contract names as shown below. Within each type, the creators randomly selected contracts based on the names of the filing companies across the alphabet.
Type of Contracts: # of Docs
Affiliate Agreement: 8
Agency Agreement: 8
Collaboration/Cooperation Agreement: 26
Co-Branding Agreement: 6
Consulting Agreement: 11
Development Agreement: 28
Distributor Agreement: 23
Endorsement Agreement: 10
Franchise Agreement: 14
Hosting Agreement: 12
IP Agreement: 16
Joint Venture Agreemen: 22
License Agreement: 32
Maintenance Agreement: 24
Manufacturing Agreement: 6
Marketing Agreement: 16
Non-Compete/No-Solicit/Non-Disparagement Agreement: 3
Outsourcing Agreement: 12
Promotion Agreement: 9
Reseller Agreement: 12
Service Agreement: 24
Sponsorship Agreement: 17
Supply Agreement: 13
Strategic Alliance Agreement: 32
Transportation Agreement: 1
TOTAL: 385
Categories
Document Name
Parties
Agreement Date
Effective Date
Expiration Date
Renewal Term
Notice Period To Terminate Renewal
Governing Law
Non-Compete
Exclusivity
Change Of Control
Anti-Assignment
#### Who are the source language producers?
The contracts were sourced from EDGAR, the Electronic Data Gathering, Analysis, and Retrieval system used at the U.S. Securities and Exchange Commission (SEC). Publicly traded companies in the United States are required to file certain contracts under the SEC rules. Access to these contracts is available to the public for free at https://www.sec.gov/edgar. Please read the Datasheet at https://www.atticusprojectai.org/ for information on the intended use and limitations of the CUAD.
### Annotations
#### Annotation process
The labeling process included multiple steps to ensure accuracy:
1. Law Student Training: law students attended training sessions on each of the categories that included a summary, video instructions by experienced attorneys, multiple quizzes and workshops. Students were then required to label sample contracts in eBrevia, an online contract review tool. The initial training took approximately 70-100 hours.
2. Law Student Label: law students conducted manual contract review and labeling in eBrevia.
3. Key Word Search: law students conducted keyword search in eBrevia to capture additional categories that have been missed during the “Student Label” step.
4. Category-by-Category Report Review: law students exported the labeled clauses into reports, review each clause category-by-category and highlight clauses that they believe are mislabeled.
5. Attorney Review: experienced attorneys reviewed the category-by-category report with students comments, provided comments and addressed student questions. When applicable, attorneys discussed such results with the students and reached consensus. Students made changes in eBrevia accordingly.
6. eBrevia Extras Review. Attorneys and students used eBrevia to generate a list of “extras”, which are clauses that eBrevia AI tool identified as responsive to a category but not labeled by human annotators. Attorneys and students reviewed all of the “extras” and added the correct ones. The process is repeated until all or substantially all of the “extras” are incorrect labels.
7. Final Report: The final report was exported into a CSV file. Volunteers manually added the “Yes/No” answer column to categories that do not contain an answer.
#### Who are the annotators?
Answered in above section.
### Personal and Sensitive Information
Some clauses in the files are redacted because the party submitting these contracts redacted them to protect confidentiality. Such redaction may show up as asterisks (\*\*\*) or underscores (\_\_\_) or blank spaces. The dataset and the answers reflect such redactions. For example, the answer for “January \_\_ 2020” would be “1/[]/2020”).
For any categories that require an answer of “Yes/No”, annotators include full sentences as text context in a contract. To maintain consistency and minimize inter-annotator disagreement, annotators select text for the full sentence, under the instruction of “from period to period”.
For the other categories, annotators selected segments of the text in the contract that are responsive to each such category. One category in a contract may include multiple labels. For example, “Parties” may include 4-10 separate text strings that are not continuous in a contract. The answer is presented in the unified format separated by semicolons of “Party A Inc. (“Party A”); Party B Corp. (“Party B”)”.
Some sentences in the files include confidential legends that are not part of the contracts. An example of such confidential legend is as follows:
THIS EXHIBIT HAS BEEN REDACTED AND IS THE SUBJECT OF A CONFIDENTIAL TREATMENT REQUEST. REDACTED MATERIAL IS MARKED WITH [* * *] AND HAS BEEN FILED SEPARATELY WITH THE SECURITIES AND EXCHANGE COMMISSION.
Some sentences in the files contain irrelevant information such as footers or page numbers. Some sentences may not be relevant to the corresponding category. Some sentences may correspond to a different category. Because many legal clauses are very long and contain various sub-parts, sometimes only a sub-part of a sentence is responsive to a category.
To address the foregoing limitations, annotators manually deleted the portion that is not responsive, replacing it with the symbol "<omitted>" to indicate that the two text segments do not appear immediately next to each other in the contracts. For example, if a “Termination for Convenience” clause starts with “Each Party may terminate this Agreement if” followed by three subparts “(a), (b) and (c)”, but only subpart (c) is responsive to this category, the authors manually deleted subparts (a) and (b) and replaced them with the symbol "<omitted>”. Another example is for “Effective Date”, the contract includes a sentence “This Agreement is effective as of the date written above” that appears after the date “January 1, 2010”. The annotation is as follows: “January 1, 2010 <omitted> This Agreement is effective as of the date written above.”
Because the contracts were converted from PDF into TXT files, the converted TXT files may not stay true to the format of the original PDF files. For example, some contracts contain inconsistent spacing between words, sentences and paragraphs. Table format is not maintained in the TXT files.
## 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
Attorney Advisors
Wei Chen, John Brockland, Kevin Chen, Jacky Fink, Spencer P. Goodson, Justin Haan, Alex Haskell, Kari Krusmark, Jenny Lin, Jonas Marson, Benjamin Petersen, Alexander Kwonji Rosenberg, William R. Sawyers, Brittany Schmeltz, Max Scott, Zhu Zhu
Law Student Leaders
John Batoha, Daisy Beckner, Lovina Consunji, Gina Diaz, Chris Gronseth, Calvin Hannagan, Joseph Kroon, Sheetal Sharma Saran
Law Student Contributors
Scott Aronin, Bryan Burgoon, Jigar Desai, Imani Haynes, Jeongsoo Kim, Margaret Lynch, Allison Melville, Felix Mendez-Burgos, Nicole Mirkazemi, David Myers, Emily Rissberger, Behrang Seraj, Sarahginy Valcin
Technical Advisors & Contributors
Dan Hendrycks, Collin Burns, Spencer Ball, Anya Chen
### Licensing Information
CUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license and free to the public for commercial and non-commercial use.
The creators make no representations or warranties regarding the license status of the underlying contracts, which are publicly available and downloadable from EDGAR.
Privacy Policy & Disclaimers
The categories or the contracts included in the dataset are not comprehensive or representative. The authors encourage the public to help improve them by sending them your comments and suggestions to [email protected]. Comments and suggestions will be reviewed by The Atticus Project at its discretion and will be included in future versions of Atticus categories once approved.
The use of CUAD is subject to their privacy policy https://www.atticusprojectai.org/privacy-policy and disclaimer https://www.atticusprojectai.org/disclaimer.
### Citation Information
```
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset.
|
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projecte-aina/WikiCAT_ca
|
projecte-aina
| 2023-11-25T06:02:26Z | 30 | 1 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:auromatically-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"text-classification"
] | 2022-08-18T14:29:02Z |
---
YAML tags:
annotations_creators:
- auromatically-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: wikicat_ca
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# WikiCAT_ca: Catalan Text Classification dataset
## Dataset Description
- **Paper:**
- **Point of Contact:** [email protected]
**Repository**
https://github.com/TeMU-BSC/WikiCAT
### Dataset Summary
WikiCAT_ca is a Catalan corpus for thematic Text Classification tasks. It is created automagically from Wikipedia and Wikidata sources, and contains 13201 articles from the Viquipedia classified under 13 different categories.
This dataset was developed by BSC TeMU as part of the AINA project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (ca-ES).
## Dataset Structure
### Data Instances
Two json files, one for each split.
### Data Fields
We used a simple model with the article text and associated labels, without further metadata.
#### Example:
<pre>
{"version": "1.1.0",
"data":
[
{
'sentence': ' Celsius és conegut com l\'inventor de l\'escala centesimal del termòmetre. Encara que aquest instrument és un invent molt antic, la història de la seva gradació és molt més capritxosa. Durant el segle xvi era graduat com "fred" col·locant-lo (...)',
'label': 'Ciència'
},
.
.
.
]
}
</pre>
#### Labels
'Ciència_i_Tecnologia', 'Dret', 'Economia', 'Enginyeria', 'Entreteniment', 'Esport', 'Filosofia', 'Història', 'Humanitats', 'Matemàtiques', 'Música', 'Política', 'Religió'
### Data Splits
* dev_ca.json: 2484 label-document pairs
* train_ca.json: 9907 label-document pairs
## Dataset Creation
### Methodology
“Category” starting pages are chosen to represent the topics in each language.
We extract, for each category, the main pages, as well as the subcategories ones, and the individual pages under this first level.
For each page, the "summary" provided by Wikipedia is also extracted as the representative text.
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The source data are thematic categories in the different Wikipedias
#### Who are the source language producers?
### Annotations
#### Annotation process
Automatic annotation
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
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
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Contributions
[N/A]
|
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] |
GuiGel/meddocan
|
GuiGel
| 2022-10-07T08:58:07Z | 30 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"clinical",
"protected health information",
"health records",
"region:us"
] |
[
"token-classification"
] | 2022-10-07T06:31:03Z |
---
annotations_creators:
- expert-generated
language:
- es
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: MEDDOCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- clinical
- protected health information
- health records
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for "meddocan"
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://temu.bsc.es/meddocan/index.php/datasets/](https://temu.bsc.es/meddocan/index.php/datasets/)
- **Repository:** [https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN](https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN)
- **Paper:** [http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf](http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A personal upload of the SPACC_MEDDOCAN corpus. The tokenization is made with the help of a custom [spaCy](https://spacy.io/) pipeline.
### Supported Tasks and Leaderboards
Name Entity Recognition
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Data Fields
The data fields are the same among all splits.
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|meddocan|10312|5268|5155|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
From the [SPACCC_MEDDOCAN: Spanish Clinical Case Corpus - Medical Document Anonymization](https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN) page:
> This work is licensed under a Creative Commons Attribution 4.0 International License.
>
> You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
>
> For more information, please see https://creativecommons.org/licenses/by/4.0/
### Citation Information
```
@inproceedings{Marimon2019AutomaticDO,
title={Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results},
author={Montserrat Marimon and Aitor Gonzalez-Agirre and Ander Intxaurrondo and Heidy Rodriguez and Jose Lopez Martin and Marta Villegas and Martin Krallinger},
booktitle={IberLEF@SEPLN},
year={2019}
}
```
### Contributions
Thanks to [@GuiGel](https://github.com/GuiGel) for adding this dataset.
|
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] |
joey234/nan-nli
|
joey234
| 2022-10-13T23:18:18Z | 30 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"negation",
"region:us"
] |
[
"text-classification"
] | 2022-10-13T23:16:18Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: nan-nli
size_categories:
- n<1K
source_datasets:
- original
tags:
- negation
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for [Dataset Name]
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
Natural Language Inference
Text Classification
### Languages
en
## Dataset Structure
### Data Instances
### Data Fields
premise:
hypothesis:
label:
### Data Splits
Evaluation: 258 samples
## Dataset Creation
### Curation Rationale
Extracting samples corresponding to different linguistics constructions of negation.
### Source Data
Geoffrey K. Pullum and Rodney Huddleston. 2002. Negation, chapter 9. Cambridge University Press.
#### 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?
The annotators are the authors of the papers, one of whom holds a graduate degree in linguistics.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@joey234](https://github.com/joey234) for adding this dataset.
|
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bond005/sberdevices_golos_100h_farfield
|
bond005
| 2022-10-27T04:23:04Z | 30 | 0 |
[
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"source_datasets:extended",
"language:ru",
"license:other",
"arxiv:2106.10161",
"region:us"
] |
[
"automatic-speech-recognition",
"audio-classification"
] | 2022-10-26T05:04:50Z |
---
pretty_name: Golos
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- ru
license:
- other
multilinguality:
- monolingual
paperswithcode_id: golos
size_categories:
- 10K<n<100k
source_datasets:
- extended
task_categories:
- automatic-speech-recognition
- audio-classification
---
# Dataset Card for sberdevices_golos_100h_farfield
## 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:** [Golos ASR corpus](https://www.openslr.org/114)
- **Repository:** [Golos dataset](https://github.com/sberdevices/golos)
- **Paper:** [Golos: Russian Dataset for Speech Research](https://arxiv.org/pdf/2106.10161.pdf)
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
- **Point of Contact:** [Nikolay Karpov](mailto:[email protected])
### Dataset Summary
Sberdevices Golos is a corpus of approximately 1200 hours of 16kHz Russian speech from crowd (reading speech) and farfield (communication with smart devices) domains, prepared by SberDevices Team (Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov). The data is derived from the crowd-sourcing platform, and has been manually annotated.
Authors divide all dataset into train and test subsets. The training subset includes approximately 1000 hours. For experiments with a limited number of records, authors identified training subsets of shorter length: 100 hours, 10 hours, 1 hour, 10 minutes.
This dataset is a simpler version of the above mentioned Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
### Languages
The audio is in Russian.
## Dataset Structure
### Data Instances
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
```
{'audio': {'path': None,
'array': array([ 1.22070312e-04, 1.22070312e-04, 9.15527344e-05, ...,
6.10351562e-05, 6.10351562e-05, 3.05175781e-05]), dtype=float64),
'sampling_rate': 16000},
'transcription': 'джой источники истории турции'}
```
### 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]`.
- transcription: the transcription of the audio file.
### Data Splits
This dataset is a simpler version of the original Golos:
- it includes the farfield domain only (without any sound from the crowd domain);
- validation split is built on the 10-hour training subset;
- training split corresponds to the 100-hour training subset without sounds from the 10-hour training subset;
- test split is a full original test split.
| | Train | Validation | Test |
| ----- | ------ | ---------- | ----- |
| examples | 9570 | 933 | 1916 |
| hours | 10.3h | 1.0h | 1.4h |
## 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
All recorded audio files were manually annotated on the crowd-sourcing platform.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially created by Alexander Denisenko, Angelina Kovalenko, Fedor Minkin, and Nikolay Karpov.
### Licensing Information
[Public license with attribution and conditions reserved](https://github.com/sberdevices/golos/blob/master/license/en_us.pdf)
### Citation Information
```
@misc{karpov2021golos,
author = {Karpov, Nikolay and Denisenko, Alexander and Minkin, Fedor},
title = {Golos: Russian Dataset for Speech Research},
publisher = {arXiv},
year = {2021},
url = {https://arxiv.org/abs/2106.10161}
}
```
### Contributions
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
|
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bigbio/bionlp_st_2011_ge
|
bigbio
| 2022-12-22T15:43:51Z | 30 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | 2022-11-13T22:06:52Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_3p0
pretty_name: BioNLP 2011 GE
homepage: https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2011 GE
## Dataset Description
- **Homepage:** https://sites.google.com/site/bionlpst/bionlp-shared-task-2011/genia-event-extraction-genia
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
The BioNLP-ST GE task has been promoting development of fine-grained information extraction (IE) from biomedical
documents, since 2009. Particularly, it has focused on the domain of NFkB as a model domain of Biomedical IE.
The GENIA task aims at extracting events occurring upon genes or gene products, which are typed as "Protein"
without differentiating genes from gene products. Other types of physical entities, e.g. cells, cell components,
are not differentiated from each other, and their type is given as "Entity".
## Citation Information
```
@inproceedings{10.5555/2107691.2107693,
author = {Kim, Jin-Dong and Wang, Yue and Takagi, Toshihisa and Yonezawa, Akinori},
title = {Overview of Genia Event Task in BioNLP Shared Task 2011},
year = {2011},
isbn = {9781937284091},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {The Genia event task, a bio-molecular event extraction task,
is arranged as one of the main tasks of BioNLP Shared Task 2011.
As its second time to be arranged for community-wide focused
efforts, it aimed to measure the advance of the community since 2009,
and to evaluate generalization of the technology to full text papers.
After a 3-month system development period, 15 teams submitted their
performance results on test cases. The results show the community has
made a significant advancement in terms of both performance improvement
and generalization.},
booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop},
pages = {7–15},
numpages = {9},
location = {Portland, Oregon},
series = {BioNLP Shared Task '11}
}
```
|
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bigbio/bionlp_st_2011_rel
|
bigbio
| 2022-12-22T15:43:54Z | 30 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | null | 2022-11-13T22:06:59Z |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2011 REL
homepage: https://github.com/openbiocorpora/bionlp-st-2011-rel
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2011 REL
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2011-rel
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE,COREF
The Entity Relations (REL) task is a supporting task of the BioNLP Shared Task 2011.
The task concerns the extraction of two types of part-of relations between a
gene/protein and an associated entity.
## Citation Information
```
@inproceedings{10.5555/2107691.2107703,
author = {Pyysalo, Sampo and Ohta, Tomoko and Tsujii, Jun'ichi},
title = {Overview of the Entity Relations (REL) Supporting Task of BioNLP Shared Task 2011},
year = {2011},
isbn = {9781937284091},
publisher = {Association for Computational Linguistics},
address = {USA},
abstract = {This paper presents the Entity Relations (REL) task,
a supporting task of the BioNLP Shared Task 2011. The task concerns
the extraction of two types of part-of relations between a gene/protein
and an associated entity. Four teams submitted final results for
the REL task, with the highest-performing system achieving 57.7%
F-score. While experiments suggest use of the data can help improve
event extraction performance, the task data has so far received only
limited use in support of event extraction. The REL task continues
as an open challenge, with all resources available from the shared
task website.},
booktitle = {Proceedings of the BioNLP Shared Task 2011 Workshop},
pages = {83–88},
numpages = {6},
location = {Portland, Oregon},
series = {BioNLP Shared Task '11}
}
```
|
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osanseviero/twitter-airline-sentiment
|
osanseviero
| 2022-11-16T22:31:48Z | 30 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-11-16T22:31:43Z |
---
license:
- cc-by-nc-sa-4.0
converted_from: kaggle
kaggle_id: crowdflower/twitter-airline-sentiment
---
# Dataset Card for Twitter US Airline Sentiment
## 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/crowdflower/twitter-airline-sentiment
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
*This data originally came from [Crowdflower's Data for Everyone library](http://www.crowdflower.com/data-for-everyone).*
As the original source says,
> A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").
The data we're providing on Kaggle is a slightly reformatted version of the original source. It includes both a CSV file and SQLite database. The code that does these transformations is [available on GitHub](https://github.com/benhamner/crowdflower-airline-twitter-sentiment)
For example, it contains whether the sentiment of the tweets in this set was positive, neutral, or negative for six US airlines:
[](https://www.kaggle.com/benhamner/d/crowdflower/twitter-airline-sentiment/exploring-airline-twitter-sentiment-data)
### 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 [@crowdflower](https://kaggle.com/crowdflower)
### Licensing Information
The license for this dataset is cc-by-nc-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed]
|
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] |
Javtor/rock-paper-scissors
|
Javtor
| 2022-11-25T01:55:34Z | 30 | 0 |
[
"region:us"
] | null | 2022-11-25T01:45:06Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: paper
1: rock
2: scissors
splits:
- name: test
num_bytes: 29457688.0
num_examples: 372
- name: train
num_bytes: 196585089.6
num_examples: 2520
download_size: 229783612
dataset_size: 226042777.6
---
# Dataset Card for "rock-paper-scissors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mlxen/squad_v1
|
mlxen
| 2022-11-28T18:37:58Z | 30 | 0 |
[
"region:us"
] | null | 2022-11-28T18:37:54Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346108
num_examples: 87599
download_size: 14457366
dataset_size: 79346108
---
# Dataset Card for "squad_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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dlproject/nsc_batches
|
dlproject
| 2022-12-14T17:51:02Z | 30 | 0 |
[
"region:us"
] | null | 2022-12-14T17:48:01Z |
---
dataset_info:
features:
- name: input_values
sequence:
sequence:
sequence: float32
- name: labels
dtype: string
splits:
- name: train
num_bytes: 2000882211
num_examples: 10000
download_size: 1765862751
dataset_size: 2000882211
---
# Dataset Card for "nsc_batches"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ruanchaves/hatebr
|
ruanchaves
| 2023-04-13T13:39:40Z | 30 | 7 |
[
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"instagram",
"doi:10.57967/hf/0274",
"region:us"
] |
[
"text-classification"
] | 2023-01-15T11:11:33Z |
---
annotations_creators:
- expert-generated
language:
- pt
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: HateBR - Offensive Language and Hate Speech Dataset in Brazilian Portuguese
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- instagram
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for HateBR - Offensive Language and Hate Speech Dataset in Brazilian Portuguese
## Dataset Description
- **Homepage:** http://143.107.183.175:14581/
- **Repository:** https://github.com/franciellevargas/HateBR
- **Paper:** https://aclanthology.org/2022.lrec-1.777/
- **Leaderboard:**
- **Point of Contact:** https://franciellevargas.github.io/
### Dataset Summary
HateBR is the first large-scale expert annotated corpus of Brazilian Instagram comments for hate speech and offensive language detection on the web and social media. The HateBR corpus was collected from Brazilian Instagram comments of politicians and manually annotated by specialists. It is composed of 7,000 documents annotated according to three different layers: a binary classification (offensive versus non-offensive comments), offensiveness-level (highly, moderately, and slightly offensive messages), and nine hate speech groups (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology for the dictatorship, antisemitism, and fatphobia). Each comment was annotated by three different annotators and achieved high inter-annotator agreement. Furthermore, baseline experiments were implemented reaching 85% of F1-score outperforming the current literature models for the Portuguese language. Accordingly, we hope that the proposed expertly annotated corpus may foster research on hate speech and offensive language detection in the Natural Language Processing area.
**Relevant Links:**
* [**Demo: Brasil Sem Ódio**](http://143.107.183.175:14581/)
* [**MOL - Multilingual Offensive Lexicon Annotated with Contextual Information**](https://github.com/franciellevargas/MOL)
### Supported Tasks and Leaderboards
Hate Speech Detection
### Languages
Portuguese
## Dataset Structure
### Data Instances
```
{'instagram_comments': 'Hipocrita!!',
'offensive_language': True,
'offensiveness_levels': 2,
'antisemitism': False,
'apology_for_the_dictatorship': False,
'fatphobia': False,
'homophobia': False,
'partyism': False,
'racism': False,
'religious_intolerance': False,
'sexism': False,
'xenophobia': False,
'offensive_&_non-hate_speech': True,
'non-offensive': False,
'specialist_1_hate_speech': False,
'specialist_2_hate_speech': False,
'specialist_3_hate_speech': False
}
```
### Data Fields
* **instagram_comments**: Instagram comments.
* **offensive_language**: A classification of comments as either offensive (True) or non-offensive (False).
* **offensiveness_levels**: A classification of comments based on their level of offensiveness, including highly offensive (3), moderately offensive (2), slightly offensive (1) and non-offensive (0).
* **antisemitism**: A classification of whether or not the comment contains antisemitic language.
* **apology_for_the_dictatorship**: A classification of whether or not the comment praises the military dictatorship period in Brazil.
* **fatphobia**: A classification of whether or not the comment contains language that promotes fatphobia.
* **homophobia**: A classification of whether or not the comment contains language that promotes homophobia.
* **partyism**: A classification of whether or not the comment contains language that promotes partyism.
* **racism**: A classification of whether or not the comment contains racist language.
* **religious_intolerance**: A classification of whether or not the comment contains language that promotes religious intolerance.
* **sexism**: A classification of whether or not the comment contains sexist language.
* **xenophobia**: A classification of whether or not the comment contains language that promotes xenophobia.
* **offensive_&_no-hate_speech**: A classification of whether or not the comment is offensive but does not contain hate speech.
* **specialist_1_hate_speech**: A classification of whether or not the comment was annotated by the first specialist as hate speech.
* **specialist_2_hate_speech**: A classification of whether or not the comment was annotated by the second specialist as hate speech.
* **specialist_3_hate_speech**: A classification of whether or not the comment was annotated by the third specialist as hate speech.
### Data Splits
The original authors of the dataset did not propose a standard data split. To address this, we use the [multi-label data stratification technique](http://scikit.ml/stratification.html) implemented at the scikit-multilearn library to propose a train-validation-test split. This method considers all classes for hate speech in the data and attempts to balance the representation of each class in the split.
| name |train|validation|test|
|---------|----:|----:|----:|
|hatebr|4480|1120|1400|
## Considerations for Using the Data
### Discussion of Biases
Please refer to [the HateBR paper](https://aclanthology.org/2022.lrec-1.777/) for a discussion of biases.
### Licensing Information
The HateBR dataset, including all its components, is provided strictly for academic and research purposes. The use of the dataset for any commercial or non-academic purpose is expressly prohibited without the prior written consent of [SINCH](https://www.sinch.com/).
### Citation Information
```
@inproceedings{vargas2022hatebr,
title={HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection},
author={Vargas, Francielle and Carvalho, Isabelle and de G{\'o}es, Fabiana Rodrigues and Pardo, Thiago and Benevenuto, Fabr{\'\i}cio},
booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
pages={7174--7183},
year={2022}
}
```
### Contributions
Thanks to [@ruanchaves](https://github.com/ruanchaves) for adding this dataset.
|
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] |
jonathan-roberts1/Canadian_Cropland
|
jonathan-roberts1
| 2023-03-31T14:45:40Z | 30 | 1 |
[
"region:us"
] | null | 2023-01-19T11:18:15Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': BARLEY
'1': CANOLA
'2': CORN
'3': MIXEDWOOD
'4': OAT
'5': ORCHARD
'6': PASTURE
'7': POTATO
'8': SOYBEAN
'9': SPRING_WHEAT
splits:
- name: train
num_bytes: 68287123.977
num_examples: 14111
download_size: 66338711
dataset_size: 68287123.977
viewer: true
---
# Dataset Card for "Canadian_Cropland_Dataset"
## Dataset Description
- **Paper** [Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification](https://openreview.net/pdf/3b9f82b0ce8f1e195c4c20df9637afd8ed9ea339.pdf)
- **Split** 2017, RGB
- **GitHub** [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset)
## Split Information
This HuggingFace dataset repository contains just the 2017, RGB split.
### Licensing Information
[Montreal Data License](https://github.com/bioinfoUQAM/Canadian-cropland-dataset/blob/main/DATA_LICENSE)
## Citation Information
[Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification](https://openreview.net/pdf/3b9f82b0ce8f1e195c4c20df9637afd8ed9ea339.pdf)
```
@inproceedings{jacques2021towards,
title = {Towards the Creation of a Canadian Land-Use Dataset for Agricultural Land Classification},
author = {Jacques, Amanda A Boatswain and Diallo, Abdoulaye Banir{\'e} and Lord, Etienne},
year = 2021,
booktitle = {42nd Canadian Symposium on Remote Sensing: Understanding Our World: Remote Sensing for a Sustainable Future}
}
```
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] |
IlyaGusev/rulm
|
IlyaGusev
| 2023-03-20T23:53:53Z | 30 | 13 |
[
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:ru",
"region:us"
] |
[
"text-generation"
] | 2023-01-25T18:14:38Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 78609111353
num_examples: 14811026
- name: test
num_bytes: 397130292
num_examples: 74794
- name: validation
num_bytes: 395354867
num_examples: 74691
download_size: 24170140196
dataset_size: 79401596512
task_categories:
- text-generation
language:
- ru
size_categories:
- 10M<n<100M
---
# Dataset for training Russian language models
Overall: 75G
Scripts: https://github.com/IlyaGusev/rulm/tree/master/data_processing
| Website | Char count (M) | Word count (M) |
|-----------------|---------------|---------------|
| pikabu | 14938 | 2161 |
| lenta | 1008 | 135 |
| stihi | 2994 | 393 |
| stackoverflow | 1073 | 228 |
| habr | 5112 | 753 |
| taiga_fontanka | 419 | 55 |
| librusec | 10149 | 1573 |
| buriy | 2646 | 352 |
| ods_tass | 1908 | 255 |
| wiki | 3473 | 469 |
| math | 987 | 177 |
|
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jonathan-roberts1/Brazilian_Cerrado-Savanna_Scenes
|
jonathan-roberts1
| 2023-03-31T15:28:58Z | 30 | 0 |
[
"task_categories:zero-shot-image-classification",
"task_categories:image-classification",
"license:other",
"region:us"
] |
[
"zero-shot-image-classification",
"image-classification"
] | 2023-02-14T18:28:02Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': agriculture
'1': arboreal vegetation
'2': herbaceous vegetation
'3': shrubby vegetation
splits:
- name: train
num_bytes: 16933385.557
num_examples: 1311
download_size: 14574976
dataset_size: 16933385.557
license: other
task_categories:
- zero-shot-image-classification
- image-classification
---
# Dataset Card for "Brazilian_Cerrado-Savanna_Scenes"
## Dataset Description
- **Paper** [Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
-
### Licensing Information
[CC BY-NC]
## Citation Information
[Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
```
@inproceedings{nogueira2016towards,
title = {Towards vegetation species discrimination by using data-driven descriptors},
author = {Nogueira, Keiller and Dos Santos, Jefersson A and Fornazari, Tamires and Silva, Thiago Sanna Freire and Morellato, Leonor Patricia and Torres, Ricardo da S},
year = 2016,
booktitle = {2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)},
pages = {1--6},
organization = {Ieee}
}
```
|
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Aniemore/resd_annotated
|
Aniemore
| 2023-07-14T07:59:51Z | 30 | 3 |
[
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:ru",
"license:mit",
"voice",
"emotions",
"annotated",
"classification",
"doi:10.57967/hf/1272",
"region:us"
] |
[
"audio-classification"
] | 2023-02-15T20:00:40Z |
---
language: ru
dataset_info:
features:
- name: name
dtype: string
- name: path
dtype: string
- name: speech
dtype: audio
- name: text
dtype: string
- name: emotion
dtype: string
splits:
- name: train
num_bytes: 398878916.336
num_examples: 1116
- name: test
num_bytes: 96643276
num_examples: 280
download_size: 485513605
dataset_size: 495522192.336
license: mit
task_categories:
- audio-classification
tags:
- voice
- emotions
- annotated
- classification
pretty_name: RESD
size_categories:
- 1K<n<10K
---
# Dataset Card for "resd_annotated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
VLyb/FB15k
|
VLyb
| 2023-02-16T07:44:47Z | 30 | 1 |
[
"size_categories:10K<n<100K",
"language:en",
"license:unlicense",
"link-prediction",
"region:us"
] | null | 2023-02-16T07:30:38Z |
---
license: unlicense
language:
- en
tags:
- link-prediction
pretty_name: FB15k
size_categories:
- 10K<n<100K
---
# FB15k Dataset
The details of it can be got by this paper titled:
+ [Translating Embeddings for Modeling Multi-relational Data](http://dl.acm.org/doi/10.5555/2999792.2999923)
|
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jonathan-roberts1/RSD46-WHU
|
jonathan-roberts1
| 2023-03-31T14:43:55Z | 30 | 0 |
[
"license:other",
"region:us"
] | null | 2023-02-17T15:41:45Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': airplane
'1': airport
'2': artificial dense forest land
'3': artificial sparse forest land
'4': bare land
'5': basketball court
'6': blue structured factory building
'7': building
'8': construction site
'9': cross river bridge
'10': crossroads
'11': dense tall building
'12': dock
'13': fish pond
'14': footbridge
'15': graff
'16': grassland
'17': irregular farmland
'18': low scattered building
'19': medium density scattered building
'20': medium density structured building
'21': natural dense forest land
'22': natural sparse forest land
'23': oil tank
'24': overpass
'25': parking lot
'26': plastic greenhouse
'27': playground
'28': railway
'29': red structured factory building
'30': refinery
'31': regular farmland
'32': scattered blue roof factory building
'33': scattered red roof factory building
'34': sewage plant-type-one
'35': sewage plant-type-two
'36': ship
'37': solar power station
'38': sparse residential area
'39': square
'40': steelworks
'41': storage land
'42': tennis court
'43': thermal power plant
'44': vegetable plot
'45': water
splits:
- name: train
num_bytes: 1650045051.96
num_examples: 17516
download_size: 2184490825
dataset_size: 1650045051.96
license: other
---
# Dataset Card for "RSD46-WHU"
## Dataset Description
- **Paper** [Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks](https://ieeexplore.ieee.org/iel7/36/7880748/07827088.pdf)
- **Paper** [High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective](https://www.mdpi.com/209338)
- **Split** Validation
## Split Information
This HuggingFace dataset repository contains just the Validation split.
### Licensing Information
[Free for education, research and commercial use.](https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU)
## Citation Information
[Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks](https://ieeexplore.ieee.org/iel7/36/7880748/07827088.pdf)
[High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective](https://www.mdpi.com/209338)
```
@article{long2017accurate,
title = {Accurate object localization in remote sensing images based on convolutional neural networks},
author = {Long, Yang and Gong, Yiping and Xiao, Zhifeng and Liu, Qing},
year = 2017,
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
volume = 55,
number = 5,
pages = {2486--2498}
}
@article{xiao2017high,
title = {High-resolution remote sensing image retrieval based on CNNs from a dimensional perspective},
author = {Xiao, Zhifeng and Long, Yang and Li, Deren and Wei, Chunshan and Tang, Gefu and Liu, Junyi},
year = 2017,
journal = {Remote Sensing},
publisher = {MDPI},
volume = 9,
number = 7,
pages = 725
}
```
|
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] |
nanaaaa/emotion_chinese_english
|
nanaaaa
| 2023-03-05T10:36:14Z | 30 | 6 |
[
"task_categories:text-classification",
"language:zh",
"language:en",
"doi:10.57967/hf/1019",
"region:us"
] |
[
"text-classification"
] | 2023-02-20T13:24:36Z |
---
task_categories:
- text-classification
language:
- zh
- en
---
|
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dctanner/oa_recipes
|
dctanner
| 2023-02-24T13:42:50Z | 30 | 4 |
[
"region:us"
] | null | 2023-02-24T11:52:38Z |
---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
splits:
- name: train
num_bytes: 7600684
num_examples: 4747
download_size: 3325663
dataset_size: 7600684
---
# Dataset Card for Recipes dialogue
Derrived from the Kaggle dataset [Recipes from Tasty](https://www.kaggle.com/datasets/zeeenb/recipes-from-tasty), we turn the recipe ingredients and instructions into chat dialogue using a preset list of user prompt templates.
Dataset license: CC0: Public Domain.
|
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] |
vicclab/fairy_tales
|
vicclab
| 2023-02-27T10:35:24Z | 30 | 2 |
[
"task_categories:text-generation",
"language:en",
"region:us"
] |
[
"text-generation"
] | 2023-02-26T01:18:41Z |
---
language:
- en
task_categories:
- text-generation
---
Concatenated and edited collection of fairy tales taken from Project Gutenberg.
Texts:
https://www.gutenberg.org/files/2591/2591-0.txt
https://www.gutenberg.org/files/503/503-0.txt
https://www.gutenberg.org/files/7277/7277-0.txt
https://www.gutenberg.org/cache/epub/35862/pg35862.txt
https://www.gutenberg.org/cache/epub/69739/pg69739.txt
https://www.gutenberg.org/files/2435/2435-0.txt
https://www.gutenberg.org/cache/epub/7871/pg7871.txt
https://www.gutenberg.org/files/8933/8933-0.txt
gutenberg.org/cache/epub/30834/pg30834.txt
https://www.gutenberg.org/cache/epub/68589/pg68589.txt
https://www.gutenberg.org/cache/epub/34453/pg34453.txt
gutenberg.org/cache/epub/8653/pg8653.txt
|
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] |
Hamid-reza/small-persian-QA
|
Hamid-reza
| 2023-03-02T20:06:30Z | 30 | 0 |
[
"region:us"
] | null | 2023-03-02T20:02:45Z |
---
dataset_info:
features:
- name: id
dtype: int32
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 2525696.3482460035
num_examples: 1261
- name: validation
num_bytes: 260467.5806451613
num_examples: 130
download_size: 1158438
dataset_size: 2786163.9288911647
---
# Dataset Card for "small-persian-QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Joanne/Metaphors_and_Analogies
|
Joanne
| 2023-05-30T20:40:56Z | 30 | 0 |
[
"task_categories:question-answering",
"task_categories:token-classification",
"language:en",
"region:us"
] |
[
"question-answering",
"token-classification"
] | 2023-03-10T19:57:35Z |
---
task_categories:
- question-answering
- token-classification
language:
- en
---
# Metaphors and analogies datasets
These datasets contain word pairs and quadruples forming analogies, metaphoric mapping or sematically unacceptable compositions.
- Pair instances are pairs of nouns A and B in a sentence of the form "A is a B".
- Quadruple instances are of the form : < (A,B),(C,D) >
There is an analogy when A is to B what C is to D.
The analogy is also a metaphor when the (A,B) and (C,D) form a metaphoric mapping, usually when they come from different domains.
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
Language : English
### Datasets and paper links
| Name | Size | Labels | Description |
| ---------: | :----- |:-------- | :-------------------------------------------------------------------------- |
| `Cardillo` | 260 *2 | 1, 2 | Pairs of "A is-a B" sentences composed of one metaphoric and one literal sentence. The two sentences of a given pair share the same B term. |
| `Jankowiak`| 120*3 | 0, 1, 2 | Triples of "A is-a/is-like-a B" sentences with exactly one literal, one semantic abnormal and one metaphoric sentence. |
| `Green` | 40*3 | 0, 1, 2 | Triples of proportional analogies, made of 4 terms <A, B, Ci, Di> each. One stem <A,B> is composed with 3 different <Ci,Di> pairs, to form exaclty one near analogy, one far analogy and one non analogic quadruple|
| `Kmiecik` | 720 | 0, 1, 2 | Quadruples <A,B,C,D> labelled as analogy:True/False and far_analogy: True/False|
| `SAT-met` | 160?*5 | 0, 1, 2, 12 | One pair stem <A,B> to combine with 5 different pairs <Ci,Di> and attempt to form proportional analogies. Only one <Ci,Di> forms an analogy with <A,B> We additionally labelled the analogies as **metaphoric**:True/False|
| Name | Paper Citation | Paper link | Dataset link |
| ---------: | :------- | :------------------------------ |-----------------------------------------: |
| `Cardillo` | | [Cardillo (2010)](https://link.springer.com/article/10.3758/s13428-016-0717-1) [Cardillo (2017)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2952404/ ) | |
| `Jankowiak`| | [Jankowiak (2020)]( https://link-springer-com.abc.cardiff.ac.uk/article/10.1007/s10936-020-09695-7) | |
| `Green` | Green, A. E., Kraemer, D. J. M., Fugelsang, J., Gray, J. R., & Dunbar, K. (2010). Connecting Long Distance: Semantic Distance in Analogical Reasoning Modulates Frontopolar Cortex Activity. Cerebral Cortex, 10, 70-76. | [Green (20)]() ||
| `Kmiecik` |Kmiecik, M. J., Brisson, R. J., & Morrison, R. G. (2019). The time course of semantic and relational processing during verbal analogical reasoning. Brain and Cognition, 129, 25-34. | [Kmiecik (20)]() ||
| `SAT-met` | | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf) | |
### Labels :
- Pairs
- **0** : anomaly
- **1** : literal
- **2** : metaphor
- Quadruples :
- **0** : not an analogy
- **1** : an analogy but not a metaphor
- **2** : an analogy and a metaphor or a far analogy
- **12** : maybe a metaphor, somewhere between 1 and 2
### Dataset Splits
- Both lexical and random splits are available for classification experiments.
- Size of the splits :
- **train** : 50 %
- **validation** : 10 %
- **test** : 40 %
- Additionally, for all datasets, the `5-folds` field gives frozen splits for a five-folds cross validation experiment with train/val/test = 70/10/20% of the sets.
# Datasets for Classification
- Task : binary classification or 3-classes classification of pairs or quadruples. Each pair or quadruple is to classify between anomaly, non-metaphoric and metaphoric.
## Pairs
### Datasets names & splits :
| Original set | Dataset name | Split |
|-------------:| :------------ | :------ |
| Cardillo | Pairs\_Cardillo\_random_split | random |
| | Pairs\_Cardillo\_lexical_split | lexical |
| Jankowiac | Pairs\_Jankowiac\_random_split | random |
| | Pairs\_Jankowiac\_lexical_split | lexical |
### Data fields :
| Field | Description | Type |
| -------------:| :------------ | ---- |
| corpus | name of the orgiginal dataset | str |
| id | instance id | str |
| set_id | id of the set containing the given instance in the multiple choice task | int |
| label | 0, 1, 2 | int |
| sentence | A is-a B sentence. | str |
| A | A expression in the sentence | str |
| B | B expression in the sentence | str |
| A\_position | position of A in the sentence | list(int) |
| B\_position | position of B in the sentence | list(int) |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
| -------: | :------------------------------------- | :-------- |
|Cardillo | | |
|Jankowiac | | |
## Quadruples
### Datasets names & splits
| Original set | dataset name | Split |
| -------: | :------------------------------------- | :-------- |
|Green | Quadruples\_Green\_random_split | random |
| | Quadruples\_Green\_lexical_split | lexical |
|Kmiecik | Quadruples\_Kmiecik\_random_split | random |
| | Quadruples\_Kmiecik\_lexical\_split\_on\_AB | lexical AB |
| | Quadruples\_Kmiecik\_lexical_split\_on\_CD | lexical CD |
|SAT | Quadruples\_SAT\_random\_split | random | random |
| | Quadruples\_SAT\_lexical\_split | lexical | lexical |
### Data fields :
| Field| Description | Type |
| -------------: | :------------ | :------------ |
| corpus | Name of the orgiginal dataset | str |
| id | Element id | str |
| set\_id | Id of the set containing the given instance in the multiple-choice task datasets | int |
| label | 0, 1, 2, 12 | int |
| AB | pair of terms | list(str) |
| CD | pair of terms | list(str) |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
|-------: | :------------------------------------- | :-------- |
|Green | | |
|Kmiecik | | |
| SAT | | |
# Datasets for multiple choice questions or permutation
- Task : One stem and multiple choices. The stem and its possible combinations are to be combined to form a sentence. The resulting sentence has a label <0,1,2>.
## Pairs
### Datasets names & splits :
| Original set | dataset name | Split |
| -----------|------| :---- |
| Cardillo | Pairs\_Cardillo\_set | test only |
| Jankowiac | Pairs\_Jankowiac\_set |test only |
### Data fields :
| Field | Description | Type |
| -------------: | :------------ | :------------ |
| corpus | Name of the orgiginal dataset | str |
| id | Element id | str |
| pair_ids | Ids of each pair as appearing in the classification datasets. | list(str) |
| labels | 0, 1, 2 | list(int) |
| sentences | List of the sentences composing the set | list(str) |
| A\_positions | Positions of the A's in each sentence | list(list(int)) |
| B\_positions | Positions of the B's in each sentence | list(list(int)) |
| answer | Index of the metaphor | int |
| stem | Term shared between the sentences of the set. | str |
| 5-folds | frozen splits for cross validation | list(str) |
### Examples :
| Name | Stem | Sentences |Label|
|-------: |-------: | :------------------------------------- | :-------- |
|Cardillo | comet | The astronomer's obssession was a comet. | 1 |
| | | The politician's career was a comet. | 2 |
| Jankoviac | harbour | This banana is like a harbour | 0 |
| | | A house is a harbour | 2|
| | | This area is a harbour | 1 |
## Quadruples
### Datasets names & splits :
| Original set | dataset name | Split |
| ----------: | :------| :---- |
| Green | Quadruples\_Green\_set | test only |
| SAT | Quadruples\_SAT\_met_set | test only |
### Data fields :
| Field | Description | Type |
|-------------: | :------------ | :------------ |
| corpus | name of the orgiginal dataset | str |
| id | Element id | str |
| pair\_ids | Ids of the instances as appearing in the clasification datasets | list(str) |
| labels | 0, 1, 2, 12 | list(int) |
| answer | temp | int |
| stem | Word pair to compose with all the other pairs of the set | list(str) |
| pairs | List of word pairs | list(list(str)) |
| 5-folds | Frozen splits for cross validation | list(str) |
### Examples :
| Name | Example | Label|
|-------: | :------------------------------------- | :-------- |
|Green | | |
| | | |
| SAT | | |
|
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] |
grammarly/detexd-benchmark
|
grammarly
| 2023-07-10T17:36:37Z | 30 | 1 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-classification"
] | 2023-03-21T18:44:32Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- en
size_categories:
- 1K<n<10K
pretty_name: 'DeTexD: A Benchmark Dataset for Delicate Text Detection'
dataset_info:
features:
- name: text
dtype: string
- name: annotator_1
dtype: int32
- name: annotator_2
dtype: int32
- name: annotator_3
dtype: int32
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: test
num_examples: 1023
---
# Dataset Card for DeTexD: A Benchmark Dataset for Delicate Text Detection
## Dataset Description
- **Repository:** [DeTexD repository](https://github.com/grammarly/detexd)
- **Paper:** [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO)
### Dataset Summary
We define *delicate text* as any text that is emotionally charged or potentially triggering such that engaging with it has the potential to result in harm. This broad term covers a range of sensitive texts that vary across four major dimensions: 1) riskiness, 2) explicitness, 3) topic, and 4) target.
This dataset contains texts with fine-grained individual annotator labels from 0 to 5 (where 0 indicates no risk and 5 indicates high risk) and averaged binary labels. See paper for more details.
**Repository:** [DeTexD repository](https://github.com/grammarly/detexd) <br>
**Paper:** [DeTexD: A Benchmark Dataset for Delicate Text Detection](TODO)
## Dataset Structure
### Data Instances
```
{'text': '"He asked me and the club if we could give him a couple of days off just to clear up his mind and he will be back in the group, I suppose, next Monday, back for training and then be a regular part of the whole squad again," Rangnick said.',
'annotator_1': 0,
'annotator_2': 0,
'annotator_3': 0,
'label': 0}
```
### Data Fields
- `text`: Text to be classified
- `annotator_1`: Annotator 1 score (0-5)
- `annotator_2`: Annotator 2 score (0-5)
- `annotator_3`: Annotator 3 score (0-5)
- `label`: Averaged binary score (>=3), either "negative" (0) or positive (1)
### Data Splits
| | test |
|--------------------|-----:|
| Number of examples | 1023 |
### Citation Information
```
@inproceedings{chernodub-etal-2023-detexd,
title = "{D}e{T}ex{D}: A Benchmark Dataset for Delicate Text Detection",
author = "Yavnyi, Serhii and Sliusarenko, Oleksii and Razzaghi, Jade and Mo, Yichen and Hovakimyan, Knar and Chernodub, Artem",
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.2",
pages = "14--28",
abstract = "Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called {``}delicate text.{''} We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.",
}
```
|
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pythainlp/thainer-corpus-v2
|
pythainlp
| 2023-03-23T05:23:46Z | 30 | 0 |
[
"task_categories:token-classification",
"language:th",
"license:cc-by-3.0",
"region:us"
] |
[
"token-classification"
] | 2023-03-22T16:12:10Z |
---
dataset_info:
features:
- name: words
sequence: string
- name: ner
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': O
'3': B-ORGANIZATION
'4': B-LOCATION
'5': I-ORGANIZATION
'6': I-LOCATION
'7': B-DATE
'8': I-DATE
'9': B-TIME
'10': I-TIME
'11': B-MONEY
'12': I-MONEY
'13': B-FACILITY
'14': I-FACILITY
'15': B-URL
'16': I-URL
'17': B-PERCENT
'18': I-PERCENT
'19': B-LEN
'20': I-LEN
'21': B-AGO
'22': I-AGO
'23': B-LAW
'24': I-LAW
'25': B-PHONE
'26': I-PHONE
'27': B-EMAIL
'28': I-EMAIL
'29': B-ZIP
'30': B-TEMPERATURE
'31': I-TEMPERATURE
'32': B-DTAE
'33': I-DTAE
'34': B-DATA
'35': I-DATA
splits:
- name: train
num_bytes: 3736419
num_examples: 3938
- name: validation
num_bytes: 1214580
num_examples: 1313
- name: test
num_bytes: 1242609
num_examples: 1313
download_size: 974230
dataset_size: 6193608
license: cc-by-3.0
task_categories:
- token-classification
language:
- th
---
# Dataset Card for "thainer-corpus-v2"
Thai Named Entity Recognition Corpus
Home Page: [https://pythainlp.github.io/Thai-NER/version/2](https://pythainlp.github.io/Thai-NER/version/2)
Training script and split data: [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354)
**You can download .conll to train named entity model in [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354).**
**Size**
- Train: 3,938 docs
- Validation: 1,313 docs
- Test: 1,313 Docs
Some data come from crowdsourcing between Dec 2018 - Nov 2019. [https://github.com/wannaphong/thai-ner](https://github.com/wannaphong/thai-ner)
**Domain**
- News (It, politics, economy, social)
- PR (KKU news)
- general
**Source**
- I use sone data from Nutcha’s theses (http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) and improve data by rechecking and adding more tagging.
- Twitter
- Blognone.com - It news
- thaigov.go.th
- kku.ac.th
And more (the lists are lost.)
**Tag**
- DATA - date
- TIME - time
- EMAIL - email
- LEN - length
- LOCATION - Location
- ORGANIZATION - Company / Organization
- PERSON - Person name
- PHONE - phone number
- TEMPERATURE - temperature
- URL - URL
- ZIP - Zip code
- MONEY - the amount
- LAW - legislation
- PERCENT - PERCENT
Download: [HuggingFace Hub](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
## Cite
> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7761354
or BibTeX
```
@dataset{wannaphong_phatthiyaphaibun_2022_7761354,
author = {Wannaphong Phatthiyaphaibun},
title = {Thai NER 2.0},
month = sep,
year = 2022,
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.7761354},
url = {https://doi.org/10.5281/zenodo.7761354}
}
```
|
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mstz/contraceptive
|
mstz
| 2023-04-16T17:03:10Z | 30 | 0 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc",
"contraceptive",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-12T08:32:09Z |
---
language:
- en
tags:
- contraceptive
- tabular_classification
- binary_classification
- UCI
pretty_name: Contraceptive evaluation
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- contraceptive
license: cc
---
# Contraceptive
The [Contraceptive dataset](https://archive-beta.ics.uci.edu/dataset/30/contraceptive+method+choice) from the [UCI repository](https://archive-beta.ics.uci.edu).
Does the couple use contraceptives?
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| contraceptive | Binary classification | Does the couple use contraceptives?|
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/contraceptive", "contraceptive")["train"]
```
|
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EdwardLin2023/ASVP_ESD
|
EdwardLin2023
| 2023-04-19T02:51:35Z | 30 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-04-18T08:46:32Z |
---
license: cc-by-4.0
---
# The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)
## ABOUT
The Audio, Speech, and Vision Processing Lab - Emotional Sound Database (ASVP - ESD)
was created by School of Electronic and Information Engineering, South China University of Technology.
## CHOSEN EMOTIONS
13 emotions were chosen:
1. boredom,sigh
2. neutral,calm
3. happy,laugh,gaggle
4. sad,cry
5. angry,grunt,frustration
6. fearful,scream,panic
7. disgust,dislike,contempt
8. surprised,gasp,amazed
9. excited
10. pleasure
11. pain,groan
12. disappointment,disapproval
13. breath
## ORGANISING THE DATABASE
### Speech Statistic
| Duration Statisitc | Duration |
| -------------------------- |:-------------------------------------------------:|
| Num. of Clips | 2,150 |
| Total Duration | 13347.835 seconds = 222.464 minutes = 3.708 hours |
| Max Dur | 32.235 seconds |
| Min Dur | 0.287 seconds |
| Mean Dur | 6.208 seconds |
| Std. Dur | 3.839 seconds |
| Num. of Clips > 30 seconds | 1 |
| Emotion | Num. of Clips |
| ------------------------------- |:-------------:|
| 01: boredom, sigh | 81 |
| 02: neutral, calm | 657 |
| 03: happy, laugh, gaggle | 154 |
| 04: sad, cry | 268 |
| 05: angry, grunt, frustration | 385 |
| 06: fearful, scream, panic | 63 |
| 07: disgust, dislike, contempt | 90 |
| 08: surprised, hasp, amazed | 144 |
| 09: excited | 136 |
| 10: pleasure | 15 |
| 11: pain, groan | 25 |
| 12: disappointmrnt, disapproval | 132 |
| 13: breath | 0 |
| Emotion Intensity | Num. of Clips |
| ----------------- |:-------------:|
| 01: normal | 1,783 |
| 02: high | 367 |
| Gender | Num. of Clips |
| ----------- |:-------------:|
| 01: male | 1,224 |
| 02: female | 926 |
| Age Range | Num. of Clips |
| ---------- |:-------------:|
| 01: >65 | 65 |
| 02: 20~65 | 1,914 |
| 03: 3<20 | 80 |
| 04: <3 | 91 |
| Language | Num. of Clips |
| ------------- |:-------------:|
| 01: Mandarin | 937 |
| 02: English | 621 |
| 03: French | 175 |
| 04: Others | 417 |
### Non-Speech Statistic
| Duration Statisitc | Duration |
| -------------------------- |:-------------------------------------------------:|
| Num. of Clips | 5,484 |
| Total Duration | 14438.117 seconds = 240.635 minutes = 4.011 hours |
| Max Dur | 25.810 seconds |
| Min Dur | 0.141 seconds |
| Mean Dur | 2.633 seconds |
| Std. Dur | 2.720 seconds |
| Num. of Clips > 30 seconds | 0 |
| Emotion | Num. of Clips |
| ------------------------------- |:-------------:|
| 01: boredom, sigh | 392 |
| 02: neutral, calm | 253 |
| 03: happy, laugh, gaggle | 878 |
| 04: sad, cry | 383 |
| 05: angry, grunt, frustration | 339 |
| 06: fearful, scream, panic | 799 |
| 07: disgust, dislike, contempt | 473 |
| 08: surprised, hasp, amazed | 808 |
| 09: excited | 109 |
| 10: pleasure | 273 |
| 11: pain, groan | 706 |
| 12: disappointmrnt, disapproval | 70 |
| 13: breath | 1 |
| Emotion Intensity | Num. of Clips |
| ----------------- |:-------------:|
| 01: normal | 4,693 |
| 02: high | 791 |
| Gender | Num. of Clips |
| ----------- |:-------------:|
| 01: male | 2,919 |
| 02: female | 2,565 |
| Age Range | Num. of Clips |
| ---------- |:-------------:|
| 01: >65 | 73 |
| 02: 20~65 | 5,224 |
| 03: 3<20 | 100 |
| 04: <3 | 87 |
| Language | Num. of Clips |
| ------------- |:-------------:|
| 01: Mandarin | 512 |
| 02: English | 3,258 |
| 03: French | 109 |
| 04: Others | 1,605 |
## References
1. Dejoli Tientcheu Touko Landry, Qianhua He, Haikang Yan and Yanxiong Li. (2020). ASVP-ESD:A dataset and its benchmark for emotion recognition using both speech and non-speech utterances. Global Scientific Journals, 8(6), 1793-1798.
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michaelwzhu/ChatMed_Consult_Dataset
|
michaelwzhu
| 2023-05-05T13:41:10Z | 30 | 73 |
[
"task_categories:text-generation",
"language:zh",
"license:cc-by-4.0",
"medical consultation",
"finetuning",
"region:us"
] |
[
"text-generation"
] | 2023-04-20T04:53:33Z |
---
license: cc-by-4.0
language:
- zh
tags:
- medical consultation
- finetuning
pretty_name: ChatMed-Dataset
task_categories:
- text-generation
---
# Dataset Card for ChatMed
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/michaelwzhu/ChatMed-Datasets
- **Repository:** https://github.com/michael-wzhu/ChatMed
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Wei Zhu
### Dataset Summary
ChatMed-Dataset is a dataset of 110,113 medical query-response pairs (in Chinese) generated by OpenAI's `GPT-3.5` engine. The queries are crawled from several online medical consultation sites, reflecting the medical needs in the real world. The responses are generated by the OpenAI engine. This dataset is designated to to inject medical knowledge into Chinese large language models.
The dataset size growing rapidly. Stay tuned for updates!
在大模型时代，模型都是趋同的，比如开源社区由于LlaMa的"开源"，出现了各种stanford-alpaca等一系列的开源模型。但是中文垂直领域上的AGI数据集相对匮乏。
本数据集, ChatMed-Dataset, 中的query(或者是prompt)来自于互联网上的医疗问诊问题(110,113)，反映了真实世界的不同用户/患者的医疗问诊需求。目前response都是由OpenAI `GPT-3.5`引擎回答的。我们后续会对互联网上的医生回答与患者回答进行筛选甄别，择优选择，构建质量更优的数据集。
### Supported Tasks and Leaderboards
The ChatMed-Dataset designed for fine-tuning pretrained language models to perform better in automatic medical consultations.
### Languages
The data in ChatMed-Dataset are in Chinese (zh).
## Dataset Structure
### Data Instances
The dataset is in json-line format and is very convenient to use! An example in the ChatMed-Dataset looks as follows:
```json
{
"query": "你好，视神经萎缩，黄斑变性。怎样缓解，及日常注意事项。谢谢。",
"response": "视神经萎缩和黄斑变性是两种不同的疾病，需要分别进行治疗。\n\n视神经萎缩是指视神经逐渐受损，导致视力逐渐减退，常见的治疗方法包括：\n\n1.药物治疗：如促进视神经细胞再生的药物，改善视网膜供血的药物等。\n\n2.手术治疗：如视神经萎缩的手术治疗可以通过神经移植、修复和再生等手术方式来缓解症状。\n\n3.康复训练：通过视力训练等康复训练可以帮助患者恢复视力。\n\n黄斑变性则是指黄斑区域受损，导致中央视力减退，常见的治疗方法包括：\n\n1.药物治疗：如抗氧化剂、维生素等可以缓解症状。\n\n2.手术治疗：如黄斑脱离手术等可以恢复视力。\n\n3.日常注意事项：保持良好的生活习惯，如戒烟、控制血压、血糖等，避免暴晒和过度用眼，保持充足的睡眠和均衡的饮食等。\n\n需要根据具体情况选择合适的治疗方案，建议您咨询专业医生进行诊断和治疗。"
}
```
### Data Fields
The data fields are as follows:
* `query`: real-life medical query from the web.
* `response`: response generated by the OpenAI `GPT-3.5` engine.
### Data Splits
| | train |
|---------------|------:|
| ChatMed-Dataset | 110,113 |
## 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
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on how large language models responses to real-life medical query.
> More social impacts will be discussed soon!
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `ChatMed-Dataset` data is generated by a language model (`GPT-3.5`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Wei Zhu },
title = {ChatMed-Dataset: An GPT generated medical query-response datasets for medcial large language models},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/michael-wzhu/ChatMed}},
}
```
### Contributions
[More Information Needed]
|
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] |
RossVermouth/chensu_test_dataset1
|
RossVermouth
| 2023-05-19T08:26:12Z | 30 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-19T08:25:25Z |
---
license: apache-2.0
---
|
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AyoubChLin/CompanyDocuments
|
AyoubChLin
| 2023-05-30T18:30:10Z | 30 | 2 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-29T10:39:30Z |
---
license: apache-2.0
---
|
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mirfan899/usummary
|
mirfan899
| 2023-05-30T09:12:15Z | 30 | 0 |
[
"region:us"
] | null | 2023-05-30T09:12:01Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 37261391
num_examples: 8458
- name: train
num_bytes: 335094323
num_examples: 67665
- name: validation
num_bytes: 37296120
num_examples: 8458
download_size: 191022704
dataset_size: 409651834
---
# Dataset Card for "usummary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
RafaelMPereira/HealthCareMagic-100k-Chat-Format-en
|
RafaelMPereira
| 2023-06-15T14:44:18Z | 30 | 2 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-06-15T14:42:57Z |
---
license: apache-2.0
---
|
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ManthanKulakarni/Text2JQL
|
ManthanKulakarni
| 2023-06-15T18:13:35Z | 30 | 0 |
[
"license:bsd",
"region:us"
] | null | 2023-06-15T18:12:46Z |
---
license: bsd
---
|
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] |
AdiOO7/Bank_Complaints
|
AdiOO7
| 2023-07-12T07:37:36Z | 30 | 1 |
[
"task_categories:table-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"finance",
"region:us"
] |
[
"table-question-answering"
] | 2023-07-12T07:04:50Z |
---
license: apache-2.0
task_categories:
- table-question-answering
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
---
|
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sayakpaul/hf-codegen-v2
|
sayakpaul
| 2023-08-03T10:28:58Z | 30 | 6 |
[
"region:us"
] | null | 2023-07-20T05:07:42Z |
---
dataset_info:
features:
- name: index
dtype: int64
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 51358178715
num_examples: 370000
download_size: 11423577734
dataset_size: 51358178715
---
# Dataset Card for "hf-codegen-v2"
Dataset generated with the code from: https://github.com/sayakpaul/hf-codegen.
|
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lamini/bts
|
lamini
| 2023-07-24T03:50:41Z | 30 | 1 |
[
"region:us"
] | null | 2023-07-24T03:49:06Z |
---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 129862.8
num_examples: 126
- name: test
num_bytes: 14429.2
num_examples: 14
download_size: 50390
dataset_size: 144292.0
---
# Dataset Card for "bts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
RikoteMaster/isear_for_llama2
|
RikoteMaster
| 2023-08-03T13:01:30Z | 30 | 0 |
[
"region:us"
] | null | 2023-08-02T14:24:47Z |
---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
- name: text
dtype: string
splits:
- name: train
num_bytes: 3715314
num_examples: 7499
- name: validation
num_bytes: 645323
num_examples: 1324
- name: test
num_bytes: 854222
num_examples: 1879
download_size: 567800
dataset_size: 5214859
---
# Dataset Card for "isear_for_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
larryvrh/ShareGPT-Zh_Only
|
larryvrh
| 2023-08-22T08:25:50Z | 30 | 6 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:zh",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-08-21T09:57:50Z |
---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 69835231
num_examples: 8631
download_size: 32862465
dataset_size: 69835231
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- 1K<n<10K
---
# Dataset Card for "sharegpt"
Combined and filtered from [shibing624/sharegpt_gpt4](https://huggingface.co/datasets/shibing624/sharegpt_gpt4) and [zetavg/ShareGPT-Processed](https://huggingface.co/datasets/zetavg/ShareGPT-Processed).
|
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seara/ru_go_emotions
|
seara
| 2023-08-25T19:13:08Z | 30 | 1 |
[
"task_categories:text-classification",
"task_categories:translation",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:sentiment-analysis",
"task_ids:sentiment-classification",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"source_datasets:go_emotions",
"language:ru",
"language:en",
"license:mit",
"emotion-classification",
"emotion",
"reddit",
"region:us"
] |
[
"text-classification",
"translation"
] | 2023-08-25T10:12:05Z |
---
dataset_info:
- config_name: raw
features:
- name: ru_text
dtype: string
- name: text
dtype: string
- name: id
dtype: string
- name: author
dtype: string
- name: subreddit
dtype: string
- name: link_id
dtype: string
- name: parent_id
dtype: string
- name: created_utc
dtype: float32
- name: rater_id
dtype: int32
- name: example_very_unclear
dtype: bool
- name: admiration
dtype: int32
- name: amusement
dtype: int32
- name: anger
dtype: int32
- name: annoyance
dtype: int32
- name: approval
dtype: int32
- name: caring
dtype: int32
- name: confusion
dtype: int32
- name: curiosity
dtype: int32
- name: desire
dtype: int32
- name: disappointment
dtype: int32
- name: disapproval
dtype: int32
- name: disgust
dtype: int32
- name: embarrassment
dtype: int32
- name: excitement
dtype: int32
- name: fear
dtype: int32
- name: gratitude
dtype: int32
- name: grief
dtype: int32
- name: joy
dtype: int32
- name: love
dtype: int32
- name: nervousness
dtype: int32
- name: optimism
dtype: int32
- name: pride
dtype: int32
- name: realization
dtype: int32
- name: relief
dtype: int32
- name: remorse
dtype: int32
- name: sadness
dtype: int32
- name: surprise
dtype: int32
- name: neutral
dtype: int32
splits:
- name: train
num_bytes: 84388976
num_examples: 211225
download_size: 41128059
dataset_size: 84388976
- config_name: simplified
features:
- name: ru_text
dtype: string
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': admiration
'1': amusement
'2': anger
'3': annoyance
'4': approval
'5': caring
'6': confusion
'7': curiosity
'8': desire
'9': disappointment
'10': disapproval
'11': disgust
'12': embarrassment
'13': excitement
'14': fear
'15': gratitude
'16': grief
'17': joy
'18': love
'19': nervousness
'20': optimism
'21': pride
'22': realization
'23': relief
'24': remorse
'25': sadness
'26': surprise
'27': neutral
- name: id
dtype: string
splits:
- name: train
num_bytes: 10118125
num_examples: 43410
- name: validation
num_bytes: 1261921
num_examples: 5426
- name: test
num_bytes: 1254989
num_examples: 5427
download_size: 7628917
dataset_size: 12635035
configs:
- config_name: raw
data_files:
- split: train
path: raw/train-*
- config_name: simplified
data_files:
- split: train
path: simplified/train-*
- split: validation
path: simplified/validation-*
- split: test
path: simplified/test-*
license: mit
task_categories:
- text-classification
- translation
task_ids:
- multi-class-classification
- multi-label-classification
- sentiment-analysis
- sentiment-classification
language:
- ru
- en
pretty_name: Ru-GoEmotions
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets:
- go_emotions
tags:
- emotion-classification
- emotion
- reddit
---
## Description
This dataset is a translation of the Google [GoEmotions](https://github.com/google-research/google-research/tree/master/goemotions) emotion classification dataset.
All features remain unchanged, except for the addition of a new `ru_text` column containing the translated text in Russian.
For the translation process, I used the [Deep translator](https://github.com/nidhaloff/deep-translator) with the Google engine.
You can find all the details about translation, raw `.csv` files and other stuff in this [Github repository](https://github.com/searayeah/ru-goemotions).
For more information also check the official original dataset [card](https://huggingface.co/datasets/go_emotions).
## Id to label
```yaml
0: admiration
1: amusement
2: anger
3: annoyance
4: approval
5: caring
6: confusion
7: curiosity
8: desire
9: disappointment
10: disapproval
11: disgust
12: embarrassment
13: excitement
14: fear
15: gratitude
16: grief
17: joy
18: love
19: nervousness
20: optimism
21: pride
22: realization
23: relief
24: remorse
25: sadness
26: surprise
27: neutral
```
## Label to Russian label
```yaml
admiration: восхищение
amusement: веселье
anger: злость
annoyance: раздражение
approval: одобрение
caring: забота
confusion: непонимание
curiosity: любопытство
desire: желание
disappointment: разочарование
disapproval: неодобрение
disgust: отвращение
embarrassment: смущение
excitement: возбуждение
fear: страх
gratitude: признательность
grief: горе
joy: радость
love: любовь
nervousness: нервозность
optimism: оптимизм
pride: гордость
realization: осознание
relief: облегчение
remorse: раскаяние
sadness: грусть
surprise: удивление
neutral: нейтральность
```
|
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Azure99/blossom-math-v2
|
Azure99
| 2023-09-05T03:28:13Z | 30 | 0 |
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:apache-2.0",
"region:us"
] |
[
"text-generation",
"text2text-generation"
] | 2023-09-05T03:19:29Z |
---
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
# BLOSSOM MATH V2
### 介绍
Blossom Math V2是基于Math23K和GSM8K衍生而来的中英双语数学对话数据集，适用于数学问题微调。
相比于blossom-math-v1，新增了2500条GSM8K数据和翻译为中文的2500条GSM8K-CN数据。此外，优化了答案的检查逻辑，还移除了<<1+1=2>>等计算步骤，以统一推理步骤的风格。
本数据集采用全量Math23K、GSM8K和翻译后的GSM8K的问题，随后调用gpt-3.5-turbo-0613生成结果，并使用原始数据集中的答案对生成的结果进行验证，过滤掉错误答案，很大程度上保证了问题和答案的准确性。
本次发布了全量数据的25%，包含10K记录。
### 语言
中文和英文
### 数据集结构
每条数据代表一个完整的题目及答案，包含id、input、output、answer、dataset四个字段。
- id：字符串，代表原始数据集中的题目id，与dataset字段结合可确定唯一题目。
- input：字符串，代表问题。
- output：字符串，代表gpt-3.5-turbo-0613生成的答案。
- answer：字符串，代表正确答案。
- dataset：字符串，代表原始数据集。
### 数据集限制
本数据集的所有响应均由gpt-3.5-turbo-0613生成，并经过初步校验，但仍可能包含不准确的回答。
|
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argilla/squad
|
argilla
| 2023-09-10T20:48:49Z | 30 | 0 |
[
"size_categories:10K<n<100K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | null | 2023-09-10T20:27:53Z |
---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for squad
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/squad")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/squad")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| question | Question | TextField | True | False |
| context | Context | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| answer | Answer | TextQuestion | True | N/A | N/A |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"context": "Architecturally, the school has a Catholic character. Atop the Main Building\u0027s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
"question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?"
},
"metadata": {
"split": "train"
},
"responses": [
{
"status": "submitted",
"values": {
"answer": {
"value": "Saint Bernadette Soubirous"
}
}
}
],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"answer": [
{
"status": "submitted",
"user_id": null,
"value": "Saint Bernadette Soubirous"
}
],
"answer-suggestion": null,
"answer-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"context": "Architecturally, the school has a Catholic character. Atop the Main Building\u0027s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
"external_id": null,
"metadata": "{\"split\": \"train\"}",
"question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **question** is of type `TextField`.
* **context** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **answer** is of type `TextQuestion`.
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **answer-suggestion** is of type `text`.
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
Vision-Flan/vision-flan_191-task_1k
|
Vision-Flan
| 2023-09-21T18:11:37Z | 30 | 8 |
[
"task_categories:visual-question-answering",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] |
[
"visual-question-answering"
] | 2023-09-20T16:54:20Z |
---
task_categories:
- visual-question-answering
language:
- en
pretty_name: Vision-Flan
size_categories:
- 100K<n<1M
---
# 🚀 Vision-Flan Dataset
vision-flan_191-task-1k is a human-labeled visual instruction tuning dataset consisting of 191 diverse tasks and 1,000 examples for each task.
It is constructed for visual instruction tuning and for building large-scale vision-language models.
## Paper or blog for more information:
https://github.com/VT-NLP/MultiInstruct/
https://vision-flan.github.io/
*Paper coming soon* 😊
## Citation
*Paper coming soon* 😊. If you use Vision-Flan, please use the following cites:
```
@misc{visionFlan2023,
title = {Vision-Flan:Scaling Visual Instruction Tuning},
url = {https://vision-flan.github.io/},
author = {Zhiyang Xu and Trevor Ashby and Chao Feng and Rulin Shao and Ying Shen and Di Jin and Qifan Wang and Lifu Huang},
month = {Sep},
year = {2023}
}
```
```
@inproceedings{DBLP:conf/acl/XuSH23,
author = {Zhiyang Xu and Ying Shen and Lifu Huang},
editor = {Anna Rogers and Jordan L. Boyd{-}Graber and Naoaki Okazaki},
title = {MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning},
booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), {ACL} 2023, Toronto, Canada, July 9-14, 2023},
pages = {11445--11465},
publisher = {Association for Computational Linguistics},
year = {2023},
url = {https://doi.org/10.18653/v1/2023.acl-long.641},
doi = {10.18653/v1/2023.acl-long.641},
timestamp = {Thu, 10 Aug 2023 12:35:59 +0200},
biburl = {https://dblp.org/rec/conf/acl/XuSH23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## License:
Please carefully check the licenses for all the datasets on this [page](https://vision-flan.github.io/tasks.html) before use.
## Contact:
If you have any questions or concerns please contact us at [email protected] .
|
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] |
Nicolas-BZRD/English_French_Webpages_Scraped_Translated
|
Nicolas-BZRD
| 2023-09-21T14:29:04Z | 30 | 0 |
[
"task_categories:translation",
"size_categories:10M<n<100M",
"language:en",
"language:fr",
"license:odbl",
"webpages",
"parallel",
"parallel data",
"region:us"
] |
[
"translation"
] | 2023-09-21T12:54:23Z |
---
language:
- en
- fr
license: odbl
size_categories:
- 10M<n<100M
task_categories:
- translation
tags:
- webpages
- parallel
- parallel data
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: en
dtype: string
- name: fr
dtype: string
splits:
- name: train
num_bytes: 6811772380
num_examples: 17161263
download_size: 640497280
dataset_size: 6811772380
---
# English French Webpages Scraped Translated
### Dataset Summary
French/English parallel texts for training translation models. Over 17.1 million sentences in French and English. Dataset created by Chris Callison-Burch, who crawled millions of web pages and then used a set of simple heuristics to transform French URLs onto English URLs, and assumed that these documents are translations of each other. This is the main dataset of Workshop on Statistical Machine Translation (WML) 2015 Dataset that can be used for Machine Translation and Language Models. Refer to the paper here: http://www.statmt.org/wmt15/pdf/WMT01.pdf
### Post-process
This dataset has been post-processed to remove all duplicates, empty fields and phrases containing less than 5 words.
### Original Dataset Citation
```
@InProceedings{bojar-EtAl:2015:WMT,
author = {Bojar, Ond\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2015 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
month = {September},
year = {2015},
address = {Lisbon, Portugal},
publisher = {Association for Computational Linguistics},
pages = {1--46},
url = {http://aclweb.org/anthology/W15-3001}
}
```
|
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] |
patched-codes/static-analysis-eval
|
patched-codes
| 2023-10-02T09:09:06Z | 30 | 1 |
[
"region:us"
] | null | 2023-09-22T08:24:16Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: source
dtype: string
- name: file_name
dtype: string
- name: cwe
dtype: string
splits:
- name: train
num_bytes: 87854
num_examples: 76
download_size: 53832
dataset_size: 87854
---
# Dataset Card for "static-analysis-eval"
A dataset of 76 Python programs taken from real Python open source projects (top 1000 on GitHub),
where each program is a file that has exactly 1 vulnerability as detected by a particular static analyzer (Semgrep).
|
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bitadin/attributes-v10
|
bitadin
| 2023-10-04T16:37:47Z | 30 | 0 |
[
"region:us"
] | null | 2023-10-03T09:50:31Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 37543937
num_examples: 42533
download_size: 20572906
dataset_size: 37543937
---
# Dataset Card for "attributes-v10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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casperhansen/longalpaca_1k_test
|
casperhansen
| 2023-10-15T11:55:55Z | 30 | 0 |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-10-15T11:48:27Z |
---
license: cc-by-nc-4.0
---
Dataset preprocessed from https://huggingface.co/datasets/Yukang/LongAlpaca-12k.
This contains 1000 samples that have a minimum length of 16k tokens and a maximum of 32k tokens.
## Script to reproduce
```python
from datasets import load_dataset
from transformers import AutoTokenizer
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
# Load the dataset and tokenizer
data = load_dataset("Yukang/LongAlpaca-12k")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True)
def filter_function(batch):
# Separate each round of conversation and concatenate them into single strings
conversation_strs = [f'{instruction}\n\n{output}' for instruction, output in zip(batch['instruction'], batch['output'])]
# Tokenize the strings without truncation
tokens = tokenizer(conversation_strs, truncation=False, return_length=True)
# Return True for examples where the token count exceeds max
return [length > 16384 and length <= 32768 for length in tokens['length']]
# Note that I've added a "keep" key to the return dictionary
filtered_data = data.filter(filter_function, batched=True, batch_size=1000)
# Convert to Pandas DataFrame
df = pd.DataFrame(filtered_data['train'])
df = df.loc[:, ["input", "instruction", "output"]]
# Sample 1k rows
sampled_df = df.sample(n=1000, random_state=1)
# Convert the Pandas DataFrame to a PyArrow Table
table = pa.table(sampled_df)
# Save the table as a Parquet file
pq.write_table(table, 'data.parquet')
```
|
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] |
NotShrirang/email-spam-filter
|
NotShrirang
| 2023-10-18T05:27:47Z | 30 | 2 |
[
"task_categories:text-classification",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-10-18T05:23:43Z |
---
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: Email Spam Filter Dataset
---
|
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davidfant/natural-questions-chunk-5
|
davidfant
| 2023-10-22T23:06:32Z | 30 | 0 |
[
"region:us"
] | null | 2023-10-22T23:03:02Z |
---
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: 4651468477
num_examples: 10000
download_size: 1807817811
dataset_size: 4651468477
---
# Dataset Card for "natural-questions-chunk-5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ayoub999/dataset_for_orange_factures
|
ayoub999
| 2023-11-09T13:59:48Z | 30 | 0 |
[
"region:us"
] | null | 2023-10-30T15:15:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': Ref
'2': NumFa
'3': Fourniss
'4': DateFa
'5': DateLim
'6': TotalHT
'7': TVA
'8': TotalTTc
'9': unitP
'10': Qt
'11': TVAP
'12': Désignation
'13': Adresse
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 2942860.8
num_examples: 12
- name: test
num_bytes: 735715.2
num_examples: 3
download_size: 2799104
dataset_size: 3678576.0
---
# Dataset Card for "dataset_for_orange_factures"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
re2panda/click_bate_1000_final
|
re2panda
| 2023-11-03T07:31:57Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-03T07:08:04Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 35358262.8
num_examples: 11400
- name: test
num_bytes: 1860961.2
num_examples: 600
download_size: 20825026
dataset_size: 37219224.0
---
# Dataset Card for "click_bate_1000_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nathanReitinger/mlcb-lite
|
nathanReitinger
| 2023-11-05T02:38:26Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-05T02:21:49Z |
---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 530086607
num_examples: 4681
- name: test
num_bytes: 60210047
num_examples: 521
download_size: 224023623
dataset_size: 590296654
---
# Dataset Card for "mlcb-lite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
thiyaneshnlp/po_layoutlm
|
thiyaneshnlp
| 2023-11-28T13:09:48Z | 30 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-07T11:21:37Z |
---
license: mit
---
|
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] |
erikaxenia/id_card_class_da
|
erikaxenia
| 2023-11-11T18:38:35Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-11T18:38:17Z |
---
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: image
dtype: image
- name: label
dtype: int64
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 250255462.45
num_examples: 2350
- name: valid
num_bytes: 21179110.0
num_examples: 59
- name: test
num_bytes: 16112586.0
num_examples: 58
download_size: 248519862
dataset_size: 287547158.45
---
# Dataset Card for "id_card_class_da"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
potatoSeop/chimsuja_dataset
|
potatoSeop
| 2023-11-13T10:14:56Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-13T01:33:28Z |
---
dataset_info:
features:
- name: audio
dtype: audio
- name: script
dtype: string
splits:
- name: train
num_bytes: 2614465503.562
num_examples: 2521
download_size: 3076362475
dataset_size: 2614465503.562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chimsuja_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jxie/year
|
jxie
| 2023-11-14T01:36:57Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-14T01:36:27Z |
---
dataset_info:
features:
- name: inputs
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 271551504
num_examples: 370972
- name: val
num_bytes: 67887876
num_examples: 92743
- name: test
num_bytes: 37793160
num_examples: 51630
download_size: 385557206
dataset_size: 377232540
---
# Dataset Card for "year"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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kuanhuggingface/tencent_tts_encodec
|
kuanhuggingface
| 2023-11-17T01:35:57Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-17T01:33:49Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: 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: 18583644220
num_examples: 266780
- name: validation
num_bytes: 527818324
num_examples: 7620
- name: test
num_bytes: 508374588
num_examples: 7620
download_size: 470732178
dataset_size: 19619837132
---
# Dataset Card for "tencent_tts_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vanesa1221/llama-two-unsaac
|
vanesa1221
| 2023-11-18T17:22:02Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-17T16:28:59Z |
---
dataset_info:
features:
- name: data
struct:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1358
num_examples: 2
download_size: 5753
dataset_size: 1358
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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rkdeva/Dermnet-Test-1
|
rkdeva
| 2023-11-20T18:32:38Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-20T18:30:25Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 376769298.178
num_examples: 3937
download_size: 370140973
dataset_size: 376769298.178
---
# Dataset Card for "Dermnet-Test-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
aladaf/homo-silicus-unboxing
|
aladaf
| 2023-11-20T19:44:05Z | 30 | 0 |
[
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"conversational"
] | 2023-11-20T19:36:18Z |
---
license: apache-2.0
task_categories:
- conversational
language:
- en
pretty_name: unboxing
size_categories:
- 10K<n<100K
---
|
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] |
darrel999/java-1000
|
darrel999
| 2023-11-23T07:00:41Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-23T07:00:28Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: content
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 576160
num_examples: 1000
download_size: 300158
dataset_size: 576160
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
mcanoglu/defect-detection
|
mcanoglu
| 2023-11-27T18:26:29Z | 30 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-26T10:21:37Z |
---
license: mit
---
A dataset containing safe and vulnerable code to fine-tune a llm for defect detection.
The data is extracted from the wonderful work in the [CVEFixes](https://github.com/secureIT-project/CVEfixes) repository.
Citation:
```
@inproceedings{bhandari2021:cvefixes,
title = {{CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software}},
booktitle = {{Proceedings of the 17th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE '21)}},
author = {Bhandari, Guru and Naseer, Amara and Moonen, Leon},
year = {2021},
pages = {10},
publisher = {{ACM}},
doi = {10.1145/3475960.3475985},
copyright = {Open Access},
isbn = {978-1-4503-8680-7},
language = {en}
}
```
|
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yangwang825/sst2-remove-stopwords-n2
|
yangwang825
| 2023-11-27T18:35:03Z | 30 | 0 |
[
"region:us"
] | null | 2023-11-27T18:34:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 884164
num_examples: 6920
- name: validation
num_bytes: 112712
num_examples: 872
- name: test
num_bytes: 218641
num_examples: 1821
download_size: 722219
dataset_size: 1215517
---
# Dataset Card for "sst2-remove-stopwords-n2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
IIC/lfqa_spanish
|
IIC
| 2022-10-23T05:17:47Z | 29 | 3 |
[
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:vblagoje/lfqa",
"source_datasets:vblagoje/lfqa_support_docs",
"language:es",
"region:us"
] |
[
"sequence-modeling"
] | 2022-03-20T01:15:30Z |
---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- es
multilinguality:
- monolingual
pretty_name: LFQA
size_categories:
- 100K<n<1M
source_datasets:
- vblagoje/lfqa
- vblagoje/lfqa_support_docs
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
This is an automatically translated version of [vblagoje/lfqa](https://huggingface.co/datasets/vblagoje/lfqa), a dataset used for long form question answering training. The model used for translating the dataset is [marianMT english-spanish](https://huggingface.co/Helsinki-NLP/opus-mt-en-es).
|
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Filippo/osdg_cd
|
Filippo
| 2023-10-08T09:57:13Z | 29 | 1 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-classification"
] | 2022-04-30T21:54:04Z |
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: OSDG Community Dataset (OSDG-CD)
dataset_info:
config_name: main_config
features:
- name: doi
dtype: string
- name: text_id
dtype: string
- name: text
dtype: string
- name: sdg
dtype: uint16
- name: label
dtype:
class_label:
names:
'0': SDG 1
'1': SDG 2
'2': SDG 3
'3': SDG 4
'4': SDG 5
'5': SDG 6
'6': SDG 7
'7': SDG 8
'8': SDG 9
'9': SDG 10
'10': SDG 11
'11': SDG 12
'12': SDG 13
'13': SDG 14
'14': SDG 15
'15': SDG 16
- name: labels_negative
dtype: uint16
- name: labels_positive
dtype: uint16
- name: agreement
dtype: float32
splits:
- name: train
num_bytes: 30151244
num_examples: 42355
download_size: 29770590
dataset_size: 30151244
---
# Dataset Card for OSDG-CD
## 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:** [OSDG-CD homepage](https://zenodo.org/record/8397907)
### Dataset Summary
The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).
> NOTES
>
> * There are currently no examples for SDGs 16 and 17. See [this GitHub issue](https://github.com/osdg-ai/osdg-data/issues/3).
> * As of July 2023, there areexamples also for SDG 16.
### Supported Tasks and Leaderboards
TBD
### Languages
The language of the dataset is English.
## Dataset Structure
### Data Instances
For each instance, there is a string for the text, a string for the SDG, and an integer for the label.
```
{'text': 'Each section states the economic principle, reviews international good practice and discusses the situation in Brazil.',
'label': 5}
```
The average token count for the premises and hypotheses are given below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Premise | 14.1 |
| Hypothesis | 8.3 |
### Data Fields
- `doi`: Digital Object Identifier of the original document
- `text_id`: unique text identifier
- `text`: text excerpt from the document
- `sdg`: the SDG the text is validated against
- `label`: an integer from `0` to `17` which corresponds to the `sdg` field
- `labels_negative`: the number of volunteers who rejected the suggested SDG label
- `labels_positive`: the number of volunteers who accepted the suggested SDG label
- `agreement`: agreement score based on the formula
### Data Splits
The OSDG-CD dataset has 1 splits: _train_.
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 32,327 |
## Dataset Creation
### Curation Rationale
The [The OSDG Community Dataset (OSDG-CD)](https://zenodo.org/record/8397907) was developed as a benchmark for ...
with the goal of producing a dataset large enough to train models using neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
TBD
#### Who are the source language producers?
TBD
### Annotations
#### Annotation process
TBD
#### Who are the annotators?
TBD
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
TBD
## Additional Information
TBD
### Dataset Curators
TBD
### Licensing Information
The OSDG Community Dataset (OSDG-CD) is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@dataset{osdg_2023_8397907,
author = {OSDG and
UNDP IICPSD SDG AI Lab and
PPMI},
title = {OSDG Community Dataset (OSDG-CD)},
month = oct,
year = 2023,
note = {{This CSV file uses UTF-8 character encoding. For
easy access on MS Excel, open the file using Data
→ From Text/CSV. Please split CSV data into
different columns by using a TAB delimiter.}},
publisher = {Zenodo},
version = {2023.10},
doi = {10.5281/zenodo.8397907},
url = {https://doi.org/10.5281/zenodo.8397907}
}
```
### Contributions
TBD
|
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] |
ncats/EpiSet4NER-v2
|
ncats
| 2022-09-20T15:25:56Z | 29 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"epidemiology",
"rare disease",
"named entity recognition",
"NER",
"NIH",
"region:us"
] |
[
"token-classification"
] | 2022-05-12T08:47:04Z |
---
annotations_creators:
- machine-generated
- expert-generated
language:
- en
language_creators:
- found
- expert-generated
license:
- other
multilinguality:
- monolingual
pretty_name: EpiSet4NER-v2
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- epidemiology
- rare disease
- named entity recognition
- NER
- NIH
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Github](https://github.com/ncats/epi4GARD/tree/master/EpiExtract4GARD#epiextract4gard)
- **Paper:** Pending
### Dataset Summary
EpiSet4NER-v2 is a gold-standard dataset for epidemiological entity recognition of location, epidemiologic types (e.g. "prevalence", "annual incidence", "estimated occurrence"), and epidemiological rates (e.g. "1.7 per 1,000,000 live births", "2.1:1.000.000", "one in five million", "0.03%") created by the [Genetic and Rare Diseases Information Center (GARD)](https://rarediseases.info.nih.gov/), a program in [the National Center for Advancing Translational Sciences](https://ncats.nih.gov/), one of the 27 [National Institutes of Health](https://www.nih.gov/). It was labeled programmatically using spaCy NER and rule-based methods, then manually validated by biomedical researchers, including a GARD curator (genetic and rare disease expert). This weakly-supervised teaching method allowed us to construct this high quality dataset in an efficient manner and achieve satisfactory performance on a multi-type token classification problem. It was used to train [EpiExtract4GARD-v2](https://huggingface.co/ncats/EpiExtract4GARD-v2), a BioBERT-based model fine-tuned for NER.
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature that indicates sentence number.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-LOC` (1), `I-LOC` (2), `B-EPI` (3), `I-EPI` (4),`B-STAT` (5),`I-STAT` (6).
### Data Splits
|name |train |validation|test|
|---------|-----:|----:|----:|
|EpiSet \# of abstracts|456|114|50|
|EpiSet \# tokens |117888|31262|13910|
## Dataset Creation
*Figure 1:* Creation of EpiSet4NER by NIH/NCATS
Comparing the programmatically labeled test set to the manually corrected test set allowed us to measure the precision, recall, and F1 of the programmatic labeling.
*Table 1:* Programmatic labeling of EpiSet4NER
| Evaluation Level | Entity | Precision | Recall | F1 |
|:----------------:|:------------------------:|:---------:|:------:|:-----:|
| Entity-Level | Overall | 0.559 | 0.662 | 0.606 |
| | Location | 0.597 | 0.661 | 0.627 |
| | Epidemiologic Type | 0.854 | 0.911 | 0.882 |
| | Epidemiologic Rate | 0.175 | 0.255 | 0.207 |
| Token-Level | Overall | 0.805 | 0.710 | 0.755 |
| | Location | 0.868 | 0.713 | 0.783 |
| | Epidemiologic Type | 0.908 | 0.908 | 0.908 |
| | Epidemiologic Rate | 0.739 | 0.645 | 0.689 |
An example of the text labeling:
*Figure 2:* Text Labeling using spaCy and rule-based labeling. Ideal labeling is bolded on the left. Actual programmatic output is on the right. [\[Figure citation\]](https://pubmed.ncbi.nlm.nih.gov/33649778/)
### Curation Rationale
To train ML/DL models that automate the process of rare disease epidemiological curation. This is crucial information to patients & families, researchers, grantors, and policy makers, primarily for funding purposes.
### Source Data
620 rare disease abstracts classified as epidemiological by a LSTM RNN rare disease epi classifier from 488 diseases. See Figure 1.
#### Initial Data Collection and Normalization
A random sample of 500 disease names were gathered from a list of ~6061 rare diseases tracked by GARD until ≥50 abstracts had been returned for each disease or the EBI RESTful API results were exhausted. Though we called ~25,000 abstracts from PubMed's db, only 7699 unique abstracts were returned for 488 diseases. Out of 7699 abstracts, only 620 were classified as epidemiological by the LSTM RNN epidemiological classifier.
### Annotations
#### Annotation process
Programmatic labeling. See [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/create_labeled_dataset_V2.ipynb) and then [here](https://github.com/ncats/epi4GARD/blob/master/EpiExtract4GARD/modify_existing_labels.ipynb). The test set was manually corrected after creation.
#### Who are the annotators?
Programmatic labeling was done by [@William Kariampuzha](https://github.com/wzkariampuzha), one of the NCATS researchers.
The test set was manually corrected by 2 more NCATS researchers and a GARD curator (genetic and rare disease expert).
### Personal and Sensitive Information
None. These are freely available abstracts from PubMed.
## Considerations for Using the Data
### Social Impact of Dataset
Assisting 25-30 millions Americans with rare diseases. Additionally can be useful for Orphanet or CDC researchers/curators.
### Discussion of Biases and Limitations
- There were errors in the source file that contained rare disease synonyms of names, which may have led to some unrelated abstracts being included in the training, validation, and test sets.
- The abstracts were gathered through the EBI API and is thus subject to any biases that the EBI API had. The NCBI API returns very different results as shown by an API analysis here.
- The [long short-term memory recurrent neural network epi classifier](https://pubmed.ncbi.nlm.nih.gov/34457147/) was used to sift the 7699 rare disease abstracts. This model had a hold-out validation F1 score of 0.886 and a test F1 (which was compared against a GARD curator who used full-text articles to determine truth-value of epidemiological abstract) of 0.701. With 620 epi abstracts filtered from 7699 original rare disease abstracts, there are likely several false positives and false negative epi abstracts.
- Tokenization was done by spaCy which may be a limitation (or not) for current and future models trained on this set.
- The programmatic labeling was very imprecise as seen by Table 1. This is likely the largest limitation of the [BioBERT-based model](https://huggingface.co/ncats/EpiExtract4GARD) trained on this set.
- The test set was difficult to validate even for general NCATS researchers, which is why we relied on a rare disease expert to verify our modifications. As this task of epidemiological information identification is quite difficult for non-expert humans to complete, this set, and especially a gold-standard dataset in the possible future, represents a challenging gauntlet for NLP systems, especially those focusing on numeracy, to compete on.
## Additional Information
### Dataset Curators
[NIH GARD](https://rarediseases.info.nih.gov/about-gard/pages/23/about-gard)
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@William Kariampuzha](https://github.com/wzkariampuzha) at NCATS/Axle Informatics for adding this dataset.
|
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strombergnlp/nlpcc-stance
|
strombergnlp
| 2022-10-25T21:47:26Z | 29 | 4 |
[
"task_categories:text-classification",
"task_ids:sentiment-analysis",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"stance-detection",
"region:us"
] |
[
"text-classification"
] | 2022-05-19T11:19:12Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- zh
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-analysis
pretty_name: NLPCC Stance
tags:
- stance-detection
---
# Dataset Card for "NLPCC 2016: Stance Detection in Chinese Microblogs"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html](http://tcci.ccf.org.cn/conference/2016/pages/page05_evadata.html)
- **Repository:**
- **Paper:** [https://link.springer.com/chapter/10.1007/978-3-319-50496-4_85](https://link.springer.com/chapter/10.1007/978-3-319-50496-4_85)
- **Point of Contact:** [Mads Kongsback](https://github.com/mkonxd)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data.
Some instances of the dataset have been removed, as they were without label.
### Supported Tasks and Leaderboards
* Stance Detection in Chinese Microblogs
### Languages
Chinese, as spoken on the Weibo website (`bcp47:zh`)
## Dataset Structure
### Data Instances
Example instance:
```
{
'id': '0',
'target': 'IphoneSE',
'text': '3月31日，苹果iPhone SE正式开卖，然而这款小屏新机并未出现人们预想的疯抢局面。根据市场分析机构Localytics周一公布的数据，iPhone SE正式上市的这个周末，销量成绩并不算太好。',
'stance': 2
}
```
### Data Fields
* id: a `string` field with a unique id for the instance
* target: a `string` representing the target of the stance
* text: a `string` of the stance-bearing text
* stance: an `int` representing class label -- `0`: AGAINST; `1`: FAVOR; `2`: NONE.
### Data Splits
The training split has 2986 instances
## Dataset Creation
### Curation Rationale
The goal was to create a dataset of microblog text annotated for stance. Six stance targets were selected and data was collected from Sina Weibo for annotation.
### Source Data
#### Initial Data Collection and Normalization
Not specified
#### Who are the source language producers?
Sina Weibo users
### Annotations
#### Annotation process
The stance of each target-microblog pair is duplicated annotated by two students
individually. If these two students provide the same annotation, the stance of this
microblog-target pair is then labeled. If the different annotation is detected, the third
student will be assigned to annotate this pair. Their annotation results will be voted to
obtain the final label.
#### Who are the annotators?
Students in China
### Personal and Sensitive Information
No reflections
## Considerations for Using the Data
### Social Impact of Dataset
The data preserves social media utterances verbatim and so has obviated any right to be forgotten, though usernames and post IDs are not explicitly included in the data.
### Discussion of Biases
There'll be at least a temporal and regional bias to this data, as well as it only representing expressions of stance on six topics.
### Other Known Limitations
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
### Citation Information
```
@incollection{xu2016overview,
title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
booktitle={Natural language understanding and intelligent applications},
pages={907--916},
year={2016},
publisher={Springer}
}
```
### Contributions
Added by [@mkonxd](https://github.com/mkonxd), [@leondz](https://github.com/leondz)
|
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embedding-data/WikiAnswers
|
embedding-data
| 2022-08-02T03:33:01Z | 29 | 1 |
[
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
] |
[
"sentence-similarity",
"paraphrase-mining"
] | 2022-07-09T00:13:25Z |
---
license: mit
language:
- en
paperswithcode_id: embedding-data/WikiAnswers
pretty_name: WikiAnswers
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "WikiAnswers"
## 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/afader/oqa#wikianswers-corpus](https://github.com/afader/oqa#wikianswers-corpus)
- **Repository:** [More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
- **Paper:** [More Information Needed](https://doi.org/10.1145/2623330.2623677)
- **Point of Contact:** [Anthony Fader](https://dl.acm.org/profile/81324489111), [Luke Zettlemoyer](https://dl.acm.org/profile/81100527621), [Oren Etzioni](https://dl.acm.org/profile/99658633129)
### Dataset Summary
The WikiAnswers corpus contains clusters of questions tagged by WikiAnswers users as paraphrases.
Each cluster optionally contains an answer provided by WikiAnswers users. There are 30,370,994 clusters containing an average of 25 questions per cluster. 3,386,256 (11%) of the clusters have an answer.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains 25 equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
```
{"set": [sentence_1, sentence_2, ..., sentence_25]}
{"set": [sentence_1, sentence_2, ..., sentence_25]}
...
{"set": [sentence_1, sentence_2, ..., sentence_25]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/WikiAnswers")
```
The dataset is loaded as a `DatasetDict` and has the format for `N` examples:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: N
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
#### Who are the source language producers?
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
#### Who are the annotators?
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Personal and Sensitive Information
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Discussion of Biases
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Other Known Limitations
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Licensing Information
[More Information Needed](https://github.com/afader/oqa#wikianswers-corpus)
### Citation Information
```
@inproceedings{Fader14,
author = {Anthony Fader and Luke Zettlemoyer and Oren Etzioni},
title = {{Open Question Answering Over Curated and Extracted
Knowledge Bases}},
booktitle = {KDD},
year = {2014}
}
```
### Contributions
|
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] |
npc-engine/light-batch-summarize-dialogue
|
npc-engine
| 2022-08-20T18:18:10Z | 29 | 3 |
[
"language:en",
"license:mit",
"region:us"
] | null | 2022-08-19T17:31:56Z |
---
license: mit
language: en
---
# [Light dataset](https://parl.ai/projects/light/) prepared for zero-shot summarization.
Dialogues are preprocessed into a form:
```
<Character name>: <character line>
...
<Character name>: <character line>
Summarize the document
```
|
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] |
graphs-datasets/AIDS
|
graphs-datasets
| 2023-02-07T16:38:52Z | 29 | 1 |
[
"task_categories:graph-ml",
"arxiv:2007.08663",
"region:us"
] |
[
"graph-ml"
] | 2022-09-02T10:51:25Z |
---
licence: unknown
task_categories:
- graph-ml
---
# Dataset Card for AIDS
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://wiki.nci.nih.gov/display/NCIDTPdata/AIDS+Antiviral+Screen+Data)**
- **Paper:**: (see citation)
- **Leaderboard:**: [Papers with code leaderboard](https://paperswithcode.com/sota/graph-classification-on-aids)
### Dataset Summary
The `AIDS` dataset is a dataset containing compounds checked for evidence of anti-HIV activity..
### Supported Tasks and Leaderboards
`AIDS` should be used for molecular classification, a binary classification task. The score used is accuracy with cross validation.
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
dataset_hf = load_dataset("graphs-datasets/<mydataset>")
# For the train set (replace by valid or test as needed)
dataset_pg_list = [Data(graph) for graph in dataset_hf["train"]]
dataset_pg = DataLoader(dataset_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | medium |
| #graphs | 1999 |
| average #nodes | 15.5875 |
| average #edges | 32.39 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data is not split, and should be used with cross validation. It comes from the PyGeometric version of the dataset.
## Additional Information
### Licensing Information
The dataset has been released under license unknown.
### Citation Information
```
@inproceedings{Morris+2020,
title={TUDataset: A collection of benchmark datasets for learning with graphs},
author={Christopher Morris and Nils M. Kriege and Franka Bause and Kristian Kersting and Petra Mutzel and Marion Neumann},
booktitle={ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+ 2020)},
archivePrefix={arXiv},
eprint={2007.08663},
url={www.graphlearning.io},
year={2020}
}
```
```
@InProceedings{10.1007/978-3-540-89689-0_33,
author="Riesen, Kaspar
and Bunke, Horst",
editor="da Vitoria Lobo, Niels
and Kasparis, Takis
and Roli, Fabio
and Kwok, James T.
and Georgiopoulos, Michael
and Anagnostopoulos, Georgios C.
and Loog, Marco",
title="IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning",
booktitle="Structural, Syntactic, and Statistical Pattern Recognition",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="287--297",
abstract="In recent years the use of graph based representation has gained popularity in pattern recognition and machine learning. As a matter of fact, object representation by means of graphs has a number of advantages over feature vectors. Therefore, various algorithms for graph based machine learning have been proposed in the literature. However, in contrast with the emerging interest in graph based representation, a lack of standardized graph data sets for benchmarking can be observed. Common practice is that researchers use their own data sets, and this behavior cumbers the objective evaluation of the proposed methods. In order to make the different approaches in graph based machine learning better comparable, the present paper aims at introducing a repository of graph data sets and corresponding benchmarks, covering a wide spectrum of different applications.",
isbn="978-3-540-89689-0"
}
```
|
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] |
cjvt/solar3
|
cjvt
| 2022-10-21T07:35:45Z | 29 | 0 |
[
"task_categories:text2text-generation",
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:sl",
"license:cc-by-nc-sa-4.0",
"grammatical-error-correction",
"other-token-classification-of-text-errors",
"region:us"
] |
[
"text2text-generation",
"other"
] | 2022-09-07T09:16:23Z |
---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- sl
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- other
task_ids: []
pretty_name: solar3
tags:
- grammatical-error-correction
- other-token-classification-of-text-errors
---
# Dataset Card for solar3
### Dataset Summary
Šolar* is a developmental corpus of 5485 school texts (e.g., essays), written by students in Slovenian secondary schools
(age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade.
Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the
document available at https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1589/Smernice-za-oznacevanje-korpusa-Solar_V1.1.pdf (in Slovenian).
\(*) pronounce "š" as "sh" in "shoe".
By default the dataset is provided at **sentence-level** (125867 instances): each instance contains a source (the original) and a target (the corrected) sentence. Note that either the source or the target sentence in an instance may be missing - this usually happens when a source sentence is marked as redundant or when a new sentence is added by the teacher. Additionally, a source or a target sentence may appear in multiple instances - for example, this happens when one sentence gets divided into multiple sentences.
There is also an option to aggregate the instances at the **document-level** or **paragraph-level**
by explicitly providing the correct config:
```
datasets.load_dataset("cjvt/solar3", "paragraph_level")`
datasets.load_dataset("cjvt/solar3", "document_level")`
```
### Supported Tasks and Leaderboards
Error correction, e.g., at token/sequence level, as token/sequence classification or text2text generation.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
A sample instance from the dataset:
```json
{
'id_doc': 'solar1',
'doc_title': 'KUS-G-slo-1-GO-E-2009-10001',
'is_manually_validated': True,
'src_tokens': ['”', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', '”', ',', 'izreče', 'Antigona', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
'src_ling_annotations': {
# truncated for conciseness
'lemma': ['”', 'ne', 'da', 'sovražiti', ...],
'ana': ['mte:U', 'mte:L', 'mte:Vd', ...],
'msd': ['UPosTag=PUNCT', 'UPosTag=PART|Polarity=Neg', 'UPosTag=SCONJ', ...],
'ne_tag': [..., 'O', 'B-PER', 'O', ...],
'space_after': [False, True, True, False, ...]
},
'tgt_tokens': ['„', 'Ne', 'da', 'sovražim', ',', 'da', 'ljubim', 'sem', 'na', 'svetu', ',', '”', 'izreče', 'Antigona', 'sebi', 'v', 'bran', 'kralju', 'Kreonu', 'za', 'svoje', 'nasprotno', 'mišljenje', 'pred', 'smrtjo', '.'],
# omitted for conciseness, the format is the same as in 'src_ling_annotations'
'tgt_ling_annotations': {...},
'corrections': [
{'idx_src': [0], 'idx_tgt': [0], 'corr_types': ['Z/LOČ/nerazvrščeno']},
{'idx_src': [10, 11], 'idx_tgt': [10, 11], 'corr_types': ['Z/LOČ/nerazvrščeno']},
{'idx_src': [], 'idx_tgt': [14], 'corr_types': ['O/KAT/povratnost']}
]
}
```
The instance represents a correction in the document 'solar1' (`id_doc`), which were manually assigned/validated (`is_manually_validated`). More concretely, the source sentence contains three errors (as indicated by three elements in `corrections`):
- a punctuation change: '”' -> '„';
- a punctuation change: ['”', ','] -> [',', '”'] (i.e. comma inside the quote, not outside);
- addition of a new word: 'sebi'.
### Data Fields
- `id_doc`: a string containing the identifying name of the document in which the sentence appears;
- `doc_title`: a string containing the assigned document title;
- `is_manually_validated`: a bool indicating whether the document in which the sentence appears was reviewed by a teacher;
- `src_tokens`: words in the source sentence (`[]` if there is no source sentence);
- `src_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the source tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token;
- `tgt_tokens`: words in the target sentence (`[]` if there is no target sentence);
- `tgt_ling_annotations`: a dict containing the lemmas (key `"lemma"`), morphosyntactic descriptions using UD (key `"msd"`) and JOS/MULTEXT-East (key `"ana"`) specification, named entity tags encoded using IOB2 (key `"ne_tag"`) for the target tokens (**automatically annotated**), and spacing information (key `"space_after"`), i.e. whether there is a whitespace after each token;
- `corrections`: a list of the corrections, with each correction represented with a dictionary, containing the indices of the source tokens involved (`idx_src`), target tokens involved (`idx_tgt`), and the categories of the corrections made (`corr_types`). Please note that there can be multiple assigned categories for one annotated correction, in which case `len(corr_types) > 1`.
## Dataset Creation
The Developmental corpus Šolar consists of 5,485 texts written by students in Slovenian secondary schools (age 15-19) and pupils in the 7th-9th grade of primary school (13-15), with a small percentage also from the 6th grade. The information on school (elementary or secondary), subject, level (grade or year), type of text, region, and date of production is provided for each text. School essays form the majority of the corpus while other material includes texts created during lessons, such as text recapitulations or descriptions, examples of formal applications, etc.
Part of the corpus (1516 texts) is annotated with teachers' corrections using a system of labels described in the attached document (in Slovenian). Teacher corrections were part of the original files and reflect real classroom situations of essay marking. Corrections were then inserted into texts by annotators and subsequently categorized. Due to the annotations being gathered in a practical (i.e. classroom) setting, only the most relevant errors may sometimes be annotated, e.g., not all incorrectly placed commas are annotated if there is a bigger issue in the text.
## Additional Information
### Dataset Curators
Špela Arhar Holdt; et al. (please see http://hdl.handle.net/11356/1589 for the full list)
### Licensing Information
CC BY-NC-SA 4.0.
### Citation Information
```
@misc{solar3,
title = {Developmental corpus {\v S}olar 3.0},
author = {Arhar Holdt, {\v S}pela and Rozman, Tadeja and Stritar Ku{\v c}uk, Mojca and Krek, Simon and Krap{\v s} Vodopivec, Irena and Stabej, Marko and Pori, Eva and Goli, Teja and Lavri{\v c}, Polona and Laskowski, Cyprian and Kocjan{\v c}i{\v c}, Polonca and Klemenc, Bojan and Krsnik, Luka and Kosem, Iztok},
url = {http://hdl.handle.net/11356/1589},
note = {Slovenian language resource repository {CLARIN}.{SI}},
year = {2022}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
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] |
biglam/gutenberg-poetry-corpus
|
biglam
| 2022-10-18T10:53:52Z | 29 | 4 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:cc0-1.0",
"poetry",
"stylistics",
"poems",
"gutenberg",
"region:us"
] |
[
"text-generation"
] | 2022-10-15T13:42:22Z |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Gutenberg Poetry Corpus
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- poetry
- stylistics
- poems
- gutenberg
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Allison Parrish's Gutenberg Poetry Corpus
This corpus was originally published under the CC0 license by [Allison Parrish](https://www.decontextualize.com/). Please visit Allison's fantastic [accompanying GitHub repository](https://github.com/aparrish/gutenberg-poetry-corpus) for usage inspiration as well as more information on how the data was mined, how to create your own version of the corpus, and examples of projects using it.
This dataset contains 3,085,117 lines of poetry from hundreds of Project Gutenberg books. Each line has a corresponding `gutenberg_id` (1191 unique values) from project Gutenberg.
```python
Dataset({
features: ['line', 'gutenberg_id'],
num_rows: 3085117
})
```
A row of data looks like this:
```python
{'line': 'And retreated, baffled, beaten,', 'gutenberg_id': 19}
```
|
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rifkiaputri/idk-mrc
|
rifkiaputri
| 2023-05-23T07:43:23Z | 29 | 2 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|tydiqa",
"language:id",
"license:cc-by-4.0",
"region:us"
] |
[
"question-answering"
] | 2022-11-11T05:56:43Z |
---
annotations_creators:
- machine-generated
- expert-generated
language:
- id
language_creators:
- machine-generated
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: IDK-MRC
size_categories:
- 1K<n<10K
source_datasets:
- extended|tydiqa
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for IDK-MRC
## Dataset Description
- **Repository:** [rifkiaputri/IDK-MRC](https://github.com/rifkiaputri/IDK-MRC)
- **Paper:** [PDF](https://aclanthology.org/2022.emnlp-main.465/)
- **Point of Contact:** [rifkiaputri](https://github.com/rifkiaputri)
### Dataset Summary
I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA, the new unanswerable question in IDK-MRC is generated using a question generation model and human-written question. Each paragraph in the dataset has a set of answerable and unanswerable questions with the corresponding answer.
### Supported Tasks
IDK-MRC is mainly intended to train Machine Reading Comprehension or extractive QA models.
### Languages
Indonesian
## Dataset Structure
### Data Instances
```
{
"context": "Para ilmuwan menduga bahwa megalodon terlihat seperti hiu putih yang lebih kekar, walaupun hiu ini juga mungkin tampak seperti hiu raksasa (Cetorhinus maximus) atau hiu harimau-pasir (Carcharias taurus). Hewan ini dianggap sebagai salah satu predator terbesar dan terkuat yang pernah ada, dan fosil-fosilnya sendiri menunjukkan bahwa panjang maksimal hiu raksasa ini mencapai 18 m, sementara rata-rata panjangnya berkisar pada angka 10,5 m. Rahangnya yang besar memiliki kekuatan gigitan antara 110.000 hingga 180.000 newton. Gigi mereka tebal dan kuat, dan telah berevolusi untuk menangkap mangsa dan meremukkan tulang.",
"qas":
[
{
"id": "indonesian--6040202845759439489-1",
"is_impossible": false,
"question": "Apakah jenis hiu terbesar di dunia ?",
"answers":
[
{
"text": "megalodon",
"answer_start": 27
}
]
},
{
"id": "indonesian-0426116372962619813-unans-h-2",
"is_impossible": true,
"question": "Apakah jenis hiu terkecil di dunia?",
"answers":
[]
},
{
"id": "indonesian-2493757035872656854-unans-h-2",
"is_impossible": true,
"question": "Apakah jenis hiu betina terbesar di dunia?",
"answers":
[]
}
]
}
```
### Data Fields
Each instance has several fields:
- `context`: context passage/paragraph as a string
- `qas`: list of questions related to the `context`
- `id`: question ID as a string
- `is_impossible`: whether the question is unanswerable (impossible to answer) or not as a boolean
- `question`: question as a string
- `answers`: list of answers
- `text`: answer as a string
- `answer_start`: answer start index as an integer
### Data Splits
- `train`: 9,332 (5,042 answerable, 4,290 unanswerable)
- `valid`: 764 (382 answerable, 382 unanswerable)
- `test`: 844 (422 answerable, 422 unanswerable)
## Dataset Creation
### Curation Rationale
IDK-MRC dataset is built based on the existing paragraph and answerable questions (ans) in TyDiQA-GoldP (Clark et al., 2020). The new unanswerable questions are automatically generated using the combination of mT5 (Xue et al., 2021) and XLM-R (Conneau et al., 2020) models, which are then manually verified by human annotators (filtered ans and filtered unans). We also asked the annotators to manually write additional unanswerable questions as described in §3.3 (additional unans). Each paragraphs in the final dataset will have a set of filtered ans, filtered unans, and additional unans questions.
### Annotations
#### Annotation process
In our dataset collection pipeline, the annotators are asked to validate the model-generated unanswerable questions and write a new additional unanswerable questions.
#### Who are the annotators?
We recruit four annotators with 2+ years of experience in Indonesian NLP annotation using direct recruitment. All of them are Indonesian native speakers who reside in Indonesia (Java Island) and fall under the 18–34 age category. We set the payment to around $7.5 per hour. Given the annotators’ demographic, we ensure that the payment is above the minimum wage rate (as of December 2021). All annotators also have signed the consent form and agreed to participate in this project.
## Considerations for Using the Data
The paragraphs and answerable questions that we utilized to build IDK-MRC dataset are taken from Indonesian subset of TyDiQA-GoldP dataset (Clark et al., 2020), which originates from Wikipedia articles. Since those articles are written from a neutral point of view, the risk of harmful content is minimal. Also, all model-generated questions in our dataset have been validated by human annotators to eliminate the risk of harmful questions. During the manual question generation process, the annotators are also encouraged to avoid producing possibly offensive questions.
Even so, we argue that further assessment is needed before using our dataset and models in real-world applications. This measurement is especially required for the pre-trained language models used in our experiments, namely mT5 (Xue et al., 2021), IndoBERT (Wilie et al., 2020), mBERT (Devlin et al., 2019), and XLM-R (Conneau et al., 2020). These language models are mostly pre-trained on the common-crawl dataset, which may contain harmful biases or stereotypes.
## Additional Information
### Licensing Information
CC BY-SA 4.0
### Citation Information
```bibtex
@inproceedings{putri-oh-2022-idk,
title = "{IDK}-{MRC}: Unanswerable Questions for {I}ndonesian Machine Reading Comprehension",
author = "Putri, Rifki Afina and
Oh, Alice",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.465",
pages = "6918--6933",
}
```
|
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] |
bigbio/iepa
|
bigbio
| 2022-12-22T15:44:47Z | 29 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:09:00Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: IEPA
homepage: http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
---
# Dataset Card for IEPA
## Dataset Description
- **Homepage:** http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE
The IEPA benchmark PPI corpus is designed for relation extraction. It was created from 303 PubMed abstracts, each of which contains a specific pair of co-occurring chemicals.
## Citation Information
```
@ARTICLE{ding2001mining,
title = "Mining {MEDLINE}: abstracts, sentences, or phrases?",
author = "Ding, J and Berleant, D and Nettleton, D and Wurtele, E",
journal = "Pac Symp Biocomput",
pages = "326--337",
year = 2002,
address = "United States",
language = "en"
}
```
|
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] |
bigbio/lll
|
bigbio
| 2022-12-22T15:44:52Z | 29 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | null | 2022-11-13T22:09:11Z |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: LLL05
homepage: http://genome.jouy.inra.fr/texte/LLLchallenge
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
---
# Dataset Card for LLL05
## Dataset Description
- **Homepage:** http://genome.jouy.inra.fr/texte/LLLchallenge
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE
The LLL05 challenge task is to learn rules to extract protein/gene interactions from biology abstracts from the Medline
bibliography database. The goal of the challenge is to test the ability of the participating IE systems to identify the
interactions and the gene/proteins that interact. The participants will test their IE patterns on a test set with the
aim of extracting the correct agent and target.The challenge focuses on information extraction of gene interactions in
Bacillus subtilis. Extracting gene interaction is the most popular event IE task in biology. Bacillus subtilis (Bs) is
a model bacterium and many papers have been published on direct gene interactions involved in sporulation. The gene
interactions are generally mentioned in the abstract and the full text of the paper is not needed. Extracting gene
interaction means, extracting the agent (proteins) and the target (genes) of all couples of genic interactions from
sentences.
## Citation Information
```
@article{article,
author = {Nédellec, C.},
year = {2005},
month = {01},
pages = {},
title = {Learning Language in Logic - Genic Interaction Extraction Challenge},
journal = {Proceedings of the Learning Language in Logic 2005 Workshop at the International Conference on Machine Learning}
}
```
|
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] |
PublicPrompts/Karsh
|
PublicPrompts
| 2023-01-08T05:15:57Z | 29 | 19 |
[
"license:openrail++",
"region:us"
] | null | 2022-11-28T19:18:32Z |
---
license: openrail++
---
Textual Inversion embedding to create portraits in the style of the most famous portrait photographer ever, "Yousuf Karsh"
Trigger word is "karsh"
Example images generated with this prompt template: portrait photo of "character", highly detailed, by karsh
|
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cjvt/cc_gigafida
|
cjvt
| 2023-01-17T13:11:14Z | 29 | 0 |
[
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:100M<n<1B",
"language:sl",
"license:cc-by-nc-sa-4.0",
"gigafida",
"gigafida2",
"kres",
"cckres",
"reference corpus",
"region:us"
] |
[
"fill-mask",
"text-generation"
] | 2022-11-29T15:03:45Z |
---
annotations_creators:
- no-annotation
language:
- sl
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Written corpus ccGigafida 1.0
size_categories:
- 10K<n<100K
- 100M<n<1B
source_datasets: []
tags:
- gigafida
- gigafida2
- kres
- cckres
- reference corpus
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
- language-modeling
---
# Dataset Card for ccGigafida
This repository by default loads the publicly available dataset ccGigafida, which contains a small subset of the Gigafida/Gigafida2 corpus.
The full datasets are private due to copyright. **If you happen to have access to the full datasets, the script will also work with those.**
Instead of
```
datasets.load_dataset("cjvt/cc_gigafida")
```
please use
```
datasets.load_dataset("cjvt/cc_gigafida", "private", data_dir="<directory-containing-gigafida(2)-TEI-files>")
```
**IMPORTANT:** The script will process all `.xml` files in the provided directory and its subdirectories - make sure there are no schema or metadata files in there!
### Dataset Summary
ccGigafida is a reference corpus of Slovene texts. It is a publicly available subsample of an even larger reference corpus, Gigafida (and its successor Gigafida 2).
The Gigafida corpus is an extensive collection of Slovene text of various genres, from daily newspapers, magazines, all kinds of books (fiction, non-fiction, textbooks),
web pages, transcriptions of parliamentary debates and similar.
### Supported Tasks and Leaderboards
Language modeling.
### Languages
Slovenian.
## Dataset Structure
### Data Instances
The data is loaded at document-level, i.e. one instance is one document.
```
{
'id_doc': 'F0000123',
'doc_title': 'Novi tednik NT&RC',
'authors': ['neznani novinar'],
'publish_date': '1998-03-27',
'publisher': 'Novi tednik',
'genres': ['tisk/periodično/časopis'],
'doc_tokenized': [
[
['Po', 'nekajletnem', 'počitku', 'pa', 'se', 'vračajo', 'tudi', 'kralji', 'dark', 'rock', 'godbe', 'JESUS', 'AND', 'THE', 'MARY', 'CHAIN', '.'],
['Brata', 'Reid', 'bosta', 'svojo', 'najnovejšo', 'kreacijo', '»', 'Cracking', 'Up', '«', 'objavila', 'v', 'ponedeljek', 'pri', 'trenutno', 'najuspešnejši', 'neodvisni', 'založbi', 'Creation', '(', 'vodi', 'jo', 'njun', 'nekdanji', 'menager', 'Alan', 'McGee', ',', 'zanjo', 'pa', 'poleg', 'Oasis', 'snema', 'še', 'cel', 'kup', 'popularnih', 'brit', '-', 'popovcev', ')', ',', 'tej', 'pa', 'bo', 'kmalu', 'sledil', 'tudi', 'album', '»', 'Munki', '«', '.']
],
[
['Kultni', 'ameriški', 'tehno', 'freak', 'PLASTIKMAN', 'že', 'vrsto', 'let', 'velja', 'za', 'enega', 'izmed', 'najbolj', 'inovativnih', 'in', 'produktivnih', 'ustvarjalcev', 'sodobne', 'elektronske', 'glasbe', '.'],
['Za', 'založbo', 'Nova', 'Mute', 'je', 'v', 'preteklih', 'nekaj', 'letih', 'posnel', 'cel', 'kup', 'izvrstnih', 'underground', 'dance', 'glasbenih', 'izdelkov', ',', 'pred', 'nedavnim', 'pa', 'je', 'ljubitelje', 'tovrstne', 'godbe', 'presenetil', 'z', 'ambientalnimi', 'odisejadami', ',', 'zbranimi', 'na', 'LP-ju', '»', 'Refused', '«', ',', 'ki', 'ga', 'lahko', 'od', 'prejšnjega', 'ponedeljka', 'kupite', 'tudi', 'v', 'bolje', 'založenih', 'trgovinah', 'z', 'nosilci', 'zvoka', 'na', 'sončni', 'strani', 'Alp', '.']
],
[
['STANE', 'ŠPEGEL']
]
],
'doc_lemmas': [...],
'doc_msds': [...],
'doc_string': [
[
'Po nekajletnem počitku pa se vračajo tudi kralji dark rock godbe JESUS AND THE MARY CHAIN. ',
'Brata Reid bosta svojo najnovejšo kreacijo »Cracking Up« objavila v ponedeljek pri trenutno najuspešnejši neodvisni založbi Creation (vodi jo njun nekdanji menager Alan McGee, zanjo pa poleg Oasis snema še cel kup popularnih brit-popovcev), tej pa bo kmalu sledil tudi album »Munki«.'
],
[
'Kultni ameriški tehno freak PLASTIKMAN že vrsto let velja za enega izmed najbolj inovativnih in produktivnih ustvarjalcev sodobne elektronske glasbe. ',
'Za založbo Nova Mute je v preteklih nekaj letih posnel cel kup izvrstnih underground dance glasbenih izdelkov, pred nedavnim pa je ljubitelje tovrstne godbe presenetil z ambientalnimi odisejadami, zbranimi na LP-ju »Refused«, ki ga lahko od prejšnjega ponedeljka kupite tudi v bolje založenih trgovinah z nosilci zvoka na sončni strani Alp.'
],
[
'STANE ŠPEGEL'
]
],
'id_sents': [['F0000123.000005.0', 'F0000123.000005.1'], ['F0000123.000013.0', 'F0000123.000013.1'], ['F0000123.000020.0']]
}
```
### Data Fields
- `id_doc`: the document ID (string);
- `doc_title`: the document title (string);
- `authors`: author(s) of the document (list of string): "neznani novinar" (sl) = ("unknown/unspecified journalist");
- `publish_date`: publish date (string);
- `publisher`: publisher, e.g., the name of a news agency (string);
- `genres`: genre(s) of the document (list of string) - possible genres: `['tisk', 'tisk/knjižno', 'tisk/knjižno/leposlovno', 'tisk/knjižno/strokovno', 'tisk/periodično', 'tisk/periodično/časopis', 'tisk/periodično/revija', 'tisk/drugo', 'internet']`;
- `doc_tokenized`: tokenized document - the top level lists represent paragraphs, the lists in the level deeper represent sentences, and each sentence contains tokens;
- `doc_lemmas`: lemmatized document - same structure as `doc_tokenized`;
- `doc_msds`: MSD tags of the document - same structure as `doc_tokenized` ([tagset](http://nl.ijs.si/ME/V6/msd/html/msd-sl.html));
- `doc_string`: same as `doc_tokenized` but with properly placed spaces in sentences;
- `id_sents`: IDs of sentences contained inside paragraphs of the document.
## Dataset Creation
Gigafida consists of texts which were published between 1990 and 2011. The texts come from printed sources and from the web.
Printed part contains fiction, non-fiction and textbooks, and periodicals such as daily newspapers and magazines.
Texts originating from the web were published on news portals, pages of big Slovene companies and more important governmental,
educational, research, cultural and similar institutions.
For more information, please check http://eng.slovenscina.eu/korpusi/gigafida.
## Additional Information
### Dataset Curators
Nataša Logar; et al. (please see http://hdl.handle.net/11356/1035 for the full list)
### Licensing Information
CC BY-NC-SA 4.0.
### Citation Information
```
@misc{ccGigafida,
title = {Written corpus {ccGigafida} 1.0},
author = {Logar, Nata{\v s}a and Erjavec, Toma{\v z} and Krek, Simon and Gr{\v c}ar, Miha and Holozan, Peter},
url = {http://hdl.handle.net/11356/1035},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{NonCommercial}-{ShareAlike} 4.0 International ({CC} {BY}-{NC}-{SA} 4.0)},
issn = {2820-4042},
year = {2013}
}
```
### Contributions
Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.
|
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rcds/MultiLegalSBD
|
rcds
| 2023-10-23T06:36:36Z | 29 | 3 |
[
"task_categories:token-classification",
"size_categories:100K<n<1M",
"language:en",
"language:es",
"language:de",
"language:it",
"language:pt",
"language:fr",
"region:us"
] |
[
"token-classification"
] | 2023-01-10T15:17:41Z |
---
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task_categories:
- token-classification
language:
- en
- es
- de
- it
- pt
- fr
pretty_name: 'MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset'
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a multilingual dataset containing ~130k annotated sentence boundaries. It contains laws and court decision in 6 different languages.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English, French, Italian, German, Portuguese, Spanish
## Dataset Structure
It is structured in the following format: {language}\_{type}\_{shard}.jsonl.xz
type is one of the following:
- laws
- judgements
Use the the dataset like this:
```
from datasets import load_dataset
config = 'fr_laws' #{language}_{type} | to load all languages and/or all types, use 'all_all'
dataset = load_dataset('rdcs/MultiLegalSBD', config)
```
### Data Instances
[More Information Needed]
### Data Fields
- text: the original text
- spans:
- start: offset of the first character
- end: offset of the last character
- label: One label only -> Sentence
- token_start: id of the first token
- token_end: id of the last token
- tokens:
- text: token text
- start: offset of the first character
- end: offset of the last character
- id: token id
- ws: whether the token is followed by whitespace
### Data Splits
There is only one split available
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{10.1145/3594536.3595132,
author = {Brugger, Tobias and St\"{u}rmer, Matthias and Niklaus, Joel},
title = {MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset},
year = {2023},
isbn = {9798400701979},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3594536.3595132},
doi = {10.1145/3594536.3595132},
abstract = {Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.},
booktitle = {Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law},
pages = {42–51},
numpages = {10},
keywords = {Natural Language Processing, Sentence Boundary Detection, Text Annotation, Legal Document Analysis, Multilingual},
location = {Braga, Portugal},
series = {ICAIL '23}
}
```
### Contributions
[More Information Needed]
|
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chromeNLP/quality
|
chromeNLP
| 2023-02-08T04:32:55Z | 29 | 1 |
[
"license:mit",
"region:us"
] | null | 2023-01-26T07:34:36Z |
---
license: mit
---
|
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] |
metaeval/lonli
|
metaeval
| 2023-05-31T08:41:36Z | 29 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-02-04T14:48:11Z |
---
license: mit
task_ids:
- natural-language-inference
task_categories:
- text-classification
language:
- en
---
https://github.com/microsoft/LoNLI
```bibtex
@article{Tarunesh2021TrustingRO,
title={Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task},
author={Ishan Tarunesh and Somak Aditya and Monojit Choudhury},
journal={ArXiv},
year={2021},
volume={abs/2107.07229}
}
```
|
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badmatr11x/hate-offensive-speech
|
badmatr11x
| 2023-03-15T20:17:11Z | 29 | 3 |
[
"task_categories:text-classification",
"task_ids:multi-label-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification"
] | 2023-03-14T18:01:04Z |
---
license: mit
language:
- en
size_categories:
- 10K<n<100K
source_dataset:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
dataset_info:
features:
- name: label
dtype: int64
- name: tweet
dtype: string
splits:
- name: train
num_bytes: 5045816.7990131285
num_examples: 51070
- name: test
num_bytes: 280301.1995065645
num_examples: 2837
- name: validation
num_bytes: 280400.0014803066
num_examples: 2838
download_size: 3879287
dataset_size: 5606517.999999999
---
# **Dataset Card for Hate-Offensive Speech**
This is the original dataset created by the user [badmatr11x](https://www.huggingface.co/badmatr11x/). Datasets contains the annotated tweets classifying into the three categories; **hate-speech**, **offensive-speech** and **neither**.
# **Dataset Structure**
Database Structure as follows:
```
{
"label": {
0: "hate-speech",
1: "offensive-speech",
2: "neither"
},
"tweet": <string>
}
```
### **Dataset Instances**
Examples from the datasets as follows:
Lable-0 (Hate Speech)
```
{
"label": 0,
"tweet": "@user @user @user we were? maybe you are-but don't you dare demonize innocent infants born with white skin, "
}
```
Label-1 (Offensive Speech)
```
{
"label": 1,
"tweet": "...and I'm goin back to school.. only for the hoes and a class or two"
}
```
Label-2 (Neither)
```
{
"label": 2,
"tweet": "@user @user are you guys going to take forever to bring the new gmc?"
}
```
# **Data Fields**
- `label`: a int64 value
- `tweet`: a string
# **Data Splits**
- Datasets splits into the three parts; train, validation and test.
- Training datasets contains 90% tweeets, validation contains 5% and rest of 5% assigned to test datasets.
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open-source-metrics/issues-external
|
open-source-metrics
| 2023-11-22T19:55:03Z | 29 | 0 |
[
"region:us"
] | null | 2023-03-24T16:20:35Z |
---
dataset_info:
features:
- name: dates
dtype: string
- name: type
struct:
- name: authorAssociation
dtype: string
- name: comment
dtype: bool
- name: issue
dtype: bool
splits:
- name: openai_python
num_bytes: 104501
num_examples: 2942
- name: stable_diffusion_webui
num_bytes: 1681962
num_examples: 48514
- name: langchain
num_bytes: 1539721
num_examples: 43232
- name: pytorch
num_bytes: 22349570
num_examples: 590614
- name: tensorflow
num_bytes: 14130923
num_examples: 396925
download_size: 10818493
dataset_size: 39806677
configs:
- config_name: default
data_files:
- split: stable_diffusion_webui
path: data/stable_diffusion_webui-*
- split: langchain
path: data/langchain-*
- split: pytorch
path: data/pytorch-*
- split: tensorflow
path: data/tensorflow-*
---
# Dataset Card for "issues-external"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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