id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
birdhouse5/finetuning2 | 2023-10-24T07:42:01.000Z | [
"region:us"
] | birdhouse5 | null | null | 0 | 0 | 2023-10-24T06:49:16 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text (string)
dtype: string
splits:
- name: train
num_bytes: 1251887.0
num_examples: 8
download_size: 1219237
dataset_size: 1251887.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "finetuning2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 482 | [
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namngo/viquad-qg-muti | 2023-10-24T07:30:09.000Z | [
"region:us"
] | namngo | null | null | 0 | 0 | 2023-10-24T07:30:09 | Entry not found | 15 | [
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birdhouse5/finetuning3 | 2023-10-24T07:42:53.000Z | [
"region:us"
] | birdhouse5 | null | null | 0 | 0 | 2023-10-24T07:42:51 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text (string)
dtype: string
splits:
- name: train
num_bytes: 1251887.0
num_examples: 8
download_size: 1219237
dataset_size: 1251887.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "finetuning3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 482 | [
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kjappelbaum/chemnlp-qm8 | 2023-10-24T09:04:57.000Z | [
"region:us"
] | kjappelbaum | null | null | 0 | 0 | 2023-10-24T07:53:50 | Entry not found | 15 | [
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open-llm-leaderboard/details_RWKV__rwkv-raven-14b | 2023-10-24T07:58:35.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-24T07:58:28 | ---
pretty_name: Evaluation run of RWKV/rwkv-raven-14b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [RWKV/rwkv-raven-14b](https://huggingface.co/RWKV/rwkv-raven-14b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_RWKV__rwkv-raven-14b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T07:58:24.994437](https://huggingface.co/datasets/open-llm-leaderboard/details_RWKV__rwkv-raven-14b/blob/main/results_2023-10-24T07-58-24.994437.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.038485738255033555,\n\
\ \"em_stderr\": 0.0019700054489528757,\n \"f1\": 0.11610738255033538,\n\
\ \"f1_stderr\": 0.002601381927911386,\n \"acc\": 0.34407897925588793,\n\
\ \"acc_stderr\": 0.008608321578959646\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.038485738255033555,\n \"em_stderr\": 0.0019700054489528757,\n\
\ \"f1\": 0.11610738255033538,\n \"f1_stderr\": 0.002601381927911386\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02122820318423048,\n \
\ \"acc_stderr\": 0.003970449129848635\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6669297553275454,\n \"acc_stderr\": 0.013246194028070656\n\
\ }\n}\n```"
repo_url: https://huggingface.co/RWKV/rwkv-raven-14b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T07_58_24.994437
path:
- '**/details_harness|drop|3_2023-10-24T07-58-24.994437.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T07-58-24.994437.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T07_58_24.994437
path:
- '**/details_harness|gsm8k|5_2023-10-24T07-58-24.994437.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T07-58-24.994437.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T07_58_24.994437
path:
- '**/details_harness|winogrande|5_2023-10-24T07-58-24.994437.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T07-58-24.994437.parquet'
- config_name: results
data_files:
- split: 2023_10_24T07_58_24.994437
path:
- results_2023-10-24T07-58-24.994437.parquet
- split: latest
path:
- results_2023-10-24T07-58-24.994437.parquet
---
# Dataset Card for Evaluation run of RWKV/rwkv-raven-14b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/RWKV/rwkv-raven-14b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [RWKV/rwkv-raven-14b](https://huggingface.co/RWKV/rwkv-raven-14b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_RWKV__rwkv-raven-14b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T07:58:24.994437](https://huggingface.co/datasets/open-llm-leaderboard/details_RWKV__rwkv-raven-14b/blob/main/results_2023-10-24T07-58-24.994437.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.038485738255033555,
"em_stderr": 0.0019700054489528757,
"f1": 0.11610738255033538,
"f1_stderr": 0.002601381927911386,
"acc": 0.34407897925588793,
"acc_stderr": 0.008608321578959646
},
"harness|drop|3": {
"em": 0.038485738255033555,
"em_stderr": 0.0019700054489528757,
"f1": 0.11610738255033538,
"f1_stderr": 0.002601381927911386
},
"harness|gsm8k|5": {
"acc": 0.02122820318423048,
"acc_stderr": 0.003970449129848635
},
"harness|winogrande|5": {
"acc": 0.6669297553275454,
"acc_stderr": 0.013246194028070656
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,141 | [
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rakshit12/rakshit | 2023-10-24T07:58:33.000Z | [
"region:us"
] | rakshit12 | null | null | 0 | 0 | 2023-10-24T07:58:33 | Entry not found | 15 | [
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] |
nlplabtdtu/closed-QA-en | 2023-10-24T08:50:55.000Z | [
"region:us"
] | nlplabtdtu | null | null | 0 | 0 | 2023-10-24T08:08:55 | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
- name: hint
dtype: string
- name: ok
dtype: bool
- name: english
dtype: string
splits:
- name: train
num_bytes: 9347077
num_examples: 4169
download_size: 5401215
dataset_size: 9347077
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "closed-QA-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 644 | [
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Elifslh/testv5 | 2023-10-24T08:36:02.000Z | [
"region:us"
] | Elifslh | null | null | 0 | 0 | 2023-10-24T08:36:02 | Entry not found | 15 | [
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hosnasn/chairs | 2023-10-24T09:04:56.000Z | [
"region:us"
] | hosnasn | null | null | 0 | 0 | 2023-10-24T09:04:56 | Entry not found | 15 | [
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CostelR/MyImages | 2023-10-24T09:20:58.000Z | [
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calm-and-collected/wish-you-were-here | 2023-10-25T20:59:52.000Z | [
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"photography",
"art",
"region:us"
] | calm-and-collected | null | null | 0 | 0 | 2023-10-24T09:48:54 | ---
license: cc-by-4.0
language:
- en
tags:
- photography
- art
pretty_name: Wish You were Here
size_categories:
- n<1K
---
# Wish You Were Here - Dataset
A dataset conisting out of postcards from 1900-1960 annoted with a combination of CLIP and manual annotation.
## datastructure
The dataset is strucured as follows:
- Images of postcards.
- Text file desribing the image.
- Images are liked to text file via the name of the text file.
## Metadata
| Size of dataset in quantity | Size of dataset in storage | Repeating images | Source annotation |
|---|---|---|---|
| 646 | 1.6 Gb | Yes | No |
### Collection method:
Manual search of WikiMedia pages and selection of images with attributes that allow for the usage of images without permission or attribution of the creator of the media. Licenses include:
- CC-O
- No license
- Public domain
### Annotation method:
The data was annotated using Kohya_SS in 2 phases:
1. Automated annotation using Clip.
2. Manual annotation.
During manual annotation the following features were consitently annotated:
- Type of Postcard (drawing, photograph, colored in photograph)
- Aspect ration (horizontal, vertical or square)
- Border color (if there is a border)
- Damage of the postcard (ranging from no annotation, slightly damage, damage, significant damage)
- Stamps
- Folding damage
- Lineart
- Monochrome (color images are not specified)
### Image dataset composition:
The dataset compromises of postcards originating from Germany, Poland, Russia and the United States of America. No additional annotation provded to identify where the postcards are from.
Most of the postcards depict a bias towards nature scenes E.G snowy mountain valleys at sunset. Training a model could create a bias towards these images.
## license
This dataset is licensed under CC BY 4.0 Deed. This gives you the rights to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
- 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.
- No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
The license only applies to the descriptions of the images. Not to the images themselves (see collection method for more details). | 2,840 | [
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aerdincdal/tr_wikipedia_ddo_qa | 2023-10-30T10:29:01.000Z | [
"license:apache-2.0",
"region:us"
] | aerdincdal | null | null | 0 | 0 | 2023-10-24T10:08:27 | ---
license: apache-2.0
---
*TR Wikipedia DDO QA Dataseti*
Bu dataset, Türkçe Wikipedia'dan çeşitli konularda soru-cevap verilerini içermektedir. Dataset, aşağıdaki kategorilere ayrılmıştır:
Edebiyat: edebiyat.json dosyasında bulabilirsiniz. Dosya boyutu: 9.31 MB.
Siyaset: siyaset.json dosyasında bulabilirsiniz. Dosya boyutu: 6.15 MB.
Televizyon: televizyon.json dosyasında bulabilirsiniz. Dosya boyutu: 6.67 MB.
Kullanım
Bu dataseti, doğal dil işleme uygulamalarınızda, özellikle soru-cevap sistemlerinizde kullanabilirsiniz. Veriler, JSON formatında sağlanmaktadır ve her bir kategori için ayrı dosyalarda bulunmaktadır.
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] |
viditsorg/autotrain-data-mbart-finetune | 2023-10-24T12:05:47.000Z | [
"task_categories:summarization",
"region:us"
] | viditsorg | null | null | 0 | 0 | 2023-10-24T12:05:03 | ---
task_categories:
- summarization
---
# AutoTrain Dataset for project: mbart-finetune
## Dataset Description
This dataset has been automatically processed by AutoTrain for project mbart-finetune.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
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"target": "\u092b\u094d\u0930\u0947\u0921\u0930\u093f\u0915 \u0928\u0940\u0924\u094d\u0936\u0947 \u0905\u092c \u0924\u0915 \u0915\u0947 \u0938\u092c\u0938\u0947 \u092a\u094d\u0930\u092d\u093e\u0935\u0936\u093e\u0932\u0940 \u0926\u093e\u0930\u094d\u0936\u0928\u093f\u0915\u094b\u0902 \u092e\u0947\u0902 \u0938\u0947 \u090f\u0915 \u0925\u0947\u0964 \u0935\u0939 \u092a\u093e\u0930\u0902\u092a\u0930\u093f\u0915 \u0928\u0948\u0924\u093f\u0915\u0924\u093e \u0914\u0930 \u0927\u0930\u094d\u092e \u0915\u0940 \u0906\u0932\u094b\u091a\u0928\u093e \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u092a\u094d\u0930\u0938\u093f\u0926\u094d\u0927 \u0939\u0948\u0902\u0964 \u0932\u0947\u0915\u093f\u0928 \u0905\u092a\u0928\u0947 \u092a\u0942\u0930\u0947 \u091c\u0940\u0935\u0928 \u092e\u0947\u0902 \u0909\u0928\u094d\u0939\u094b\u0902\u0928\u0947 \u092c\u0939\u0941\u0924 \u0936\u093e\u0930\u0940\u0930\u093f\u0915 \u0914\u0930 \u092e\u093e\u0928\u0938\u093f\u0915 \u092a\u0940\u0921\u093c\u093e \u091d\u0947\u0932\u0940\u0964 \u092a\u0939\u0932\u0947 \u0926\u0930\u094d\u0926 \u0936\u093e\u0930\u0940\u0930\u093f\u0915 \u0925\u093e, \u092b\u093f\u0930 \u092e\u093e\u0928\u0938\u093f\u0915 \u0930\u0942\u092a \u0938\u0947 \u092d\u0940 \u0938\u0924\u093e\u0928\u0947 \u0932\u0917\u093e\u0964 \u0927\u0940\u0930\u0947-\u0927\u0940\u0930\u0947 \u0935\u0939 \u0916\u0941\u0926 \u0915\u094b \u092a\u093e\u0917\u0932\u092a\u0928 \u0915\u0940 \u0913\u0930 \u0932\u0947 \u0917\u092f\u093e\u0964 \u0932\u0947\u0915\u093f\u0928 \u0907\u0938\u0915\u0947 \u092a\u0940\u091b\u0947 \u092e\u0941\u0916\u094d\u092f \u0915\u093e\u0930\u0923 \u0915\u094d\u092f\u093e \u0925\u093e? \u0915\u0941\u091b \u0932\u094b\u0917 \u0936\u093e\u0930\u0940\u0930\u093f\u0915 \u0930\u094b\u0917\u094b\u0902 \u0915\u094b \u091c\u093f\u092e\u094d\u092e\u0947\u0926\u093e\u0930 \u0920\u0939\u0930\u093e\u0924\u0947 \u0939\u0948\u0902, \u0932\u0947\u0915\u093f\u0928 \u0939\u093e\u0932 \u0939\u0940 \u092e\u0947\u0902 \u0915\u0908 \u0935\u093f\u0926\u094d\u0935\u093e\u0928\u094b\u0902 \u0915\u093e \u091d\u0941\u0915\u093e\u0935 \u0938\u094d\u0935\u092a\u094d\u0930\u0947\u0930\u093f\u0924 \u092a\u093e\u0917\u0932\u092a\u0928 \u0915\u0940 \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0915\u0940 \u0913\u0930 \u0930\u0939\u093e \u0939\u0948\u0964 \u092e\u0924\u0932\u092c \u0939\u094b \u0938\u0915\u0924\u093e \u0939\u0948 \u0915\u093f \u0928\u0940\u0924\u094d\u0936\u0947 \u0905\u092a\u0928\u0940 \u092e\u0930\u094d\u091c\u0940 \u0938\u0947 \u092a\u093e\u0917\u0932 \u0939\u094b \u0917\u092f\u093e \u0939\u094b, \u0909\u0938\u0928\u0947 \u092f\u0939 \u091c\u094b\u0916\u093f\u092e \u0909\u0920\u093e\u092f\u093e\u0964 \u0907\u0938 \u092a\u0949\u0921\u0915\u093e\u0938\u094d\u091f \u0938\u0947\u0917\u092e\u0947\u0902\u091f \u092e\u0947\u0902 \u0939\u092e \u0907\u0938 \u0935\u093f\u091a\u093e\u0930 \u0915\u094b \u0914\u0930 \u0905\u0927\u093f\u0915 \u090f\u0915\u094d\u0938\u092a\u094d\u0932\u094b\u0930 \u0915\u0930\u0947\u0902\u0917\u0947 \u0914\u0930 \u0938\u092e\u091d\u0947\u0902\u0917\u0947 \u0915\u093f \u0928\u0940\u0924\u094d\u0936\u0947 \u0928\u0947 \u091c\u093f\u0938 \u0924\u0930\u0939 \u0938\u0947 \u0915\u093f\u092f\u093e \u0909\u0938\u0915\u093e \u0905\u0902\u0924 \u0915\u094d\u092f\u094b\u0902 \u0939\u0941\u0906\u0964"
},
{
"text": "\u0906\u0938\u093e\u0928 \u0914\u0930 \u092e\u0941\u0936\u094d\u0915\u093f\u0932 \u092f\u0947 \u0936\u092c\u094d\u0926 \u0939\u0930 \u0907\u0902\u0938\u093e\u0928 \u0915\u0947 \u0932\u093f\u090f \u092c\u0939\u0941\u0924 \u0939\u0940 \u0930\u093f\u0932\u0947\u091f\u093f\u0935 \u0939\u094b\u0924\u0947 \u0939\u0948\u0964 \u091c\u0948\u0938\u0947 120 \u0915\u093f\u0932\u094b \u0915\u0947 \u0921\u0902\u092c\u0932 \u0915\u0908 \u0932\u094b\u0917\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u092c\u0939\u0941\u0924 \u092d\u093e\u0930\u0940 \u0939\u094b\u0924\u093e \u0939\u0948 \u0914\u0930 \u0926\u094b \u091a\u093e\u0930 \u0930\u0947\u092a\u093f\u091f\u0947\u0936\u0928 \u092e\u0947\u0902 \u0939\u0940 \u0935\u0939 \u0916\u0941\u0926 \u0915\u094b \u091a\u094b\u091f \u092a\u0939\u0941\u0902\u091a\u093e \u0938\u0915\u0924\u0947 \u0939\u0948\u0902, \u091c\u092c\u0915\u093f \u0915\u0908 \u0932\u094b\u0917 \u0907\u0924\u0928\u0947 \u0935\u091c\u0928 \u0938\u0947 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\u0924\u092c \u0924\u0941\u092e \u092c\u094b\u0932\u094b\u0917\u0947 \u0915\u093f \u091c\u093f\u0902\u0926\u0917\u0940 \u0909\u0924\u0928\u0940 \u092e\u0941\u0936\u094d\u0915\u093f\u0932 \u0914\u0930 \u0926\u0941\u0916\u0926\u093e\u092f\u0940 \u0928\u0939\u0940\u0902 \u0939\u0948 \u091c\u093f\u0924\u0928\u093e \u0939\u092e \u0909\u0938\u0947 \u092c\u0928\u093e \u0926\u0947\u0924\u0947 \u0939\u0948\u0902\u0964 \u0938\u094d\u091f\u0949\u092a \u092e\u0947\u0915\u093f\u0902\u0917 \u092f\u094b\u0930 \u0932\u093e\u0907\u092b \u0905\u0928\u0928\u0947\u0938\u0947\u0938\u0930\u0940 \u0932\u0940 \u0939\u093e\u0930\u094d\u0921 \u0924\u0941\u092e\u094d\u0939\u093e\u0930\u0940 \u091c\u093f\u0902\u0926\u0917\u0940 \u0915\u092d\u0940 \u0928\u093e \u0905\u0902\u0924 \u0939\u094b\u0928\u0947 \u0935\u093e\u0932\u0940 \u0932\u0940\u0932\u093e \u0915\u093e \u0939\u093f\u0938\u094d\u0938\u093e \u0939\u0948\u0964 \u0907\u0938\u0915\u093e \u0930\u0938 \u0932\u094b \u0914\u0930 \u091c\u093f\u0924\u0928\u093e \u0939\u094b \u0938\u0915\u0947 \u0909\u0924\u0928\u093e \u0907\u0938 \u0926\u0941\u0928\u093f\u092f\u093e \u092e\u0947\u0902 \u092c\u0926\u0932\u093e\u0935 \u0932\u093e\u0913\u0964 \u092b\u0941\u091f \u092a\u0930 \u0914\u0930 \u0916\u0941\u0926 \u0915\u0940 \u092a\u094b\u091f\u0947\u0902\u0936\u093f\u0905\u0932 \u092a\u0930 \u0915\u093e\u092e \u0915\u0930\u0915\u0947 \u0932\u093e\u0907\u092b \u0907\u095b \u0930\u093f\u092f\u0932\u0940 \u0939\u093e\u0930\u094d\u0921 \u092b\u0949\u0930 \u092f\u0942 \u092c\u091f \u0907\u091f \u0907\u0938 \u0935\u0947\u0907\u091f\u094d \u092b\u0949\u0930 \u0926\u094b\u095b \u0939\u0942\u0901 \u0914\u0930 \u0932\u093f\u0935\u093f\u0902\u0917 \u0905\u092a \u091f\u0941 \u0926\u0947\u0930 \u092a\u094b\u091f\u0947\u0902\u0936\u093f\u0905\u0932\u0964",
"target": "\u0906\u0938\u093e\u0928 \u0939\u094b \u092f\u093e \u092e\u0941\u0936\u094d\u0915\u093f\u0932, \u092f\u0947 \u0936\u092c\u094d\u0926 \u091c\u094d\u092f\u093e\u0926\u093e\u0924\u0930 \u0932\u094b\u0917\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0938\u093e\u092a\u0947\u0915\u094d\u0937 \u0939\u094b\u0924\u0947 \u0939\u0948\u0902\u0964 \u091c\u094b \u0906\u092a\u0915\u0947 \u0932\u093f\u090f \u0906\u0938\u093e\u0928 \u0939\u0948 \u0935\u0939 \u0915\u093f\u0938\u0940 \u0914\u0930 \u0915\u0947 \u0932\u093f\u090f \u092e\u0941\u0936\u094d\u0915\u093f\u0932 \u0939\u094b \u0938\u0915\u0924\u093e \u0939\u0948 \u0914\u0930 \u0907\u0938\u0915\u0947 \u0935\u093f\u092a\u0930\u0940\u0924\u0964 \u0915\u093f\u0938\u0940 \u0915\u093e\u0930\u094d\u092f \u0915\u0940 \u0915\u0920\u093f\u0928\u093e\u0908 \u0909\u0938\u0947 \u0915\u0930\u0928\u0947 \u0935\u093e\u0932\u0947 \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u0915\u0940 \u0915\u094d\u0937\u092e\u0924\u093e \u092a\u0930 \u0928\u093f\u0930\u094d\u092d\u0930 \u0915\u0930\u0924\u0940 \u0939\u0948\u0964 \u090f\u0915 \u0916\u093e\u0938 \u092e\u093e\u0928\u0938\u093f\u0915\u0924\u093e \u0930\u0916\u0928\u0947 \u0935\u093e\u0932\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u091c\u0940\u0935\u0928 \u0906\u0938\u093e\u0928 \u0939\u094b\u0924\u093e \u0939\u0948 \u0914\u0930 \u0907\u0938 \u092a\u0949\u0921\u0915\u093e\u0938\u094d\u091f \u0938\u0947\u0917\u092e\u0947\u0902\u091f \u092e\u0947\u0902 \u0939\u092e \u0910\u0938\u0940 \u0939\u0940 \u092e\u093e\u0928\u0938\u093f\u0915\u0924\u093e \u0915\u0947 \u092c\u093e\u0930\u0947 \u092e\u0947\u0902 \u092c\u093e\u0924 \u0915\u0930\u0947\u0902\u0917\u0947\u0964"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 451 |
| valid | 113 |
| 75,398 | [
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Nussiebah/jais | 2023-10-24T12:22:11.000Z | [
"region:us"
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TruongDiem/test4 | 2023-10-24T12:37:10.000Z | [
"region:us"
] | TruongDiem | null | null | 0 | 0 | 2023-10-24T12:34:07 | dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_examples: 6
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] |
Arsture/guikal | 2023-10-24T12:34:39.000Z | [
"region:us"
] | Arsture | null | null | 0 | 0 | 2023-10-24T12:34:37 | ---
dataset_info:
features:
- name: Line
dtype: string
splits:
- name: train
num_bytes: 196000
num_examples: 7378
download_size: 136705
dataset_size: 196000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guikal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 431 | [
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PDBEurope/protein_chain_conformational_states | 2023-10-24T12:43:32.000Z | [
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"Structural biology",
"Bioinformatics",
"Machine learning",
"Conformation",
"Conformational state",
"Monomeric",
"Training data",
"Benchmark",
"Manually curated",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-24T12:36:32 | ---
license: cc-by-4.0
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- feature-extraction
tags:
- Structural biology
- Bioinformatics
- Machine learning
- Conformation
- Conformational state
- Monomeric
- Training data
- Benchmark
- Manually curated
pretty_name: Curated dataset of protein chain conformational states
---
## Schema description:
The manually curated dataset of open-closed monomers is included here as `benchmarking_monomeric_open_closed_conformers.csv`.
Column descriptions:
## Schema description:
The manually curated dataset of open-closed monomers is included here as `benchmarking_monomeric_open_closed_conformers.csv`.
Column descriptions:
- **`UNP_ACC`** | UniProt accession code
- **`UNP_START`** | Start of UniProt sequence for given PDBe entries
- **`UNP_END`** | End of UniProt sequence for given PDBe entries
- **`PDBe_ID`** | Protein Data Bank code
- **`CHAIN_ID`** | Author declared chain ID (`char`)
- **`label_asym_id`** | Programmatically assigned chain ID (`char`)
- **`CONFORMER_ID`** | Unique code for PDBe entries with distinct conformation, corresponding to a given UniProt accession
- **`CONFORMER_DESCR`** | Short description of conformation, based on depositor's assessment of the protein/conformation
- **`LIT_CONFIRMED`** | True/false value based on whether a publication (scientific literature) was available for manually curating clusters. NB: Clusters with 0 in this field should be used with caution.
- **`ALT_CONFORMER_ID`** | Where the publication for a structure is currently outstanding, an executive decision on the conformation classification is made. Where the literature is not explicit on the features of a given conformation, the second most suitable `CONFORMER_ID` is provided in this column. Blank cells have no other likely conformation assignmnt and are therefore the same as in `CONFORMER_ID`.
- **`ALT_CONFORMER_DESCR`** | Description for conformation in alternative conformation ID.
## Curation process
As of 09 Mar 2022, a manually curated dataset of monomeric protein conformations has been collated, containing 'open'-'closed' pairs as well as intermediary states defined by the authors of the entry.
1. The PDBe was queried, through its Oracle DB, to find PDBe entries with 100 % sequence identity for a UniProt segment in both 'open' and 'closed' conformations, as stated in the entry's `TITLE` field. The query used:
```
select b.accession, b.unp_start, b.unp_end, a.id, a.title, d.id, d.title
from entry a, unp_entity b, unp_entity c, entry d, pdb_assembly e
where a.title like ‘%open%’ and d.title like ‘%close%’
and a.id = b.entry_id and d.id = c.entry_id and a.id != d.id
and b.accession = c.accession
and b.unp_start = c.unp_start
and b.unp_end = c.unp_end
and a.id = e.entry_id
and e.type = ‘homo’
and e.name = ‘monomer’
```
was written by Dr Sameer Velankar.
2. These results were cleaned to remove entries with 'open' or 'close' substrings in their `TITLE` fields that did not refer to conformation. The 'open' substring often appeared in ligand names in the entries' `TITLE` field, such as in *dichlorido(1,3-dimethylbenzimidaz ol-2-ylidene)(eta5-pentamethylcycl**open**tadienyl)rhodium(III)* and 'close' in terms like *dis**close**s*.
3. All remaining entries were then manually curated by investigating each PDBe entry's corresponding publication, where available.
1. Additional PDBe entries submitted by the authors, which were missed in the original search due to a lack of 'open' or 'close' substrings in their `TITLE` field but stated as fitting one of the states in the publication, were added.
2. For some UniProt accessions, intermediary conformations were reported by the authors and these were noted in the dataset under the `CONFORMER_DESCR` column.
3. Entries deposited in monomeric form but solved as a multimeric complex were also removed.
4. PDBe entries, now clustered by author-stated conformation, were cross-referenced against the PDBe-KB's existing clustering algorithm (available on the [Aggregate Views of Proteins](https://www.ebi.ac.uk/pdbe/pdbe-kb/protein) page) to assess current conformer clustering success. These results are currently awaiting publication.
### Curation process outline
<img src="http://ftp.ebi.ac.uk/pub/databases/pdbe-kb/benchmarking/distinct-monomer-conformers/work_progress_flowdiagram_200pc.png" alt="Curation flow diagram">
### Dataset summary
<img src="http://ftp.ebi.ac.uk/pub/databases/pdbe-kb/benchmarking/distinct-monomer-conformers/summary_data_visualisation.png" alt="Benchmark dataset summary graphs"> | 4,588 | [
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am96149/Llama-2-7b-chat-finetune | 2023-10-24T12:54:52.000Z | [
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] | am96149 | null | null | 0 | 0 | 2023-10-24T12:54:52 | Entry not found | 15 | [
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irlab-udc/sharegpt_galician | 2023-10-24T13:17:39.000Z | [
"license:apache-2.0",
"region:us"
] | irlab-udc | null | null | 0 | 0 | 2023-10-24T13:17:19 | ---
license: apache-2.0
---
Translating... | 43 | [
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lingvanex/lingvanex_test_references | 2023-10-24T13:48:54.000Z | [
"task_categories:translation",
"multilinguality:translation",
"size_categories:1K<n<10K",
"language:af",
"language:ar",
"language:be",
"language:bp",
"language:bs",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:es",
"language:et",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hu",
"language:id",
"language:it",
"language:lt",
"language:lv",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sq",
"language:sr",
"language:sv",
"language:tr",
"language:uk",
"license:cc-by-nc-sa-4.0",
"region:us"
] | lingvanex | null | null | 0 | 0 | 2023-10-24T13:21:32 | ---
task_categories:
- translation
multilinguality:
- translation
task_ids: []
language:
- af
- ar
- be
- bp
- bs
- cs
- da
- de
- el
- es
- et
- fa
- fi
- fr
- he
- hu
- id
- it
- lt
- lv
- no
- pl
- pt
- ro
- ru
- sq
- sr
- sv
- tr
- uk
size_categories:
- 1K<n<10K
configs:
- config_name: en-cs
data_files:
- split: test
path: data/en-cs.csv
- config_name: en-et
data_files:
- split: test
path: data/en-et.csv
- config_name: en-hu
data_files:
- split: test
path: data/en-hu.csv
- config_name: en-no
data_files:
- split: test
path: data/en-no.csv
- config_name: en-ru
data_files:
- split: test
path: data/en-ru.csv
- config_name: en-uk
data_files:
- split: test
path: data/en-uk.csv
- config_name: en-af
data_files:
- split: test
path: data/en-af.csv
- config_name: en-da
data_files:
- split: test
path: data/en-da.csv
- config_name: en-fa
data_files:
- split: test
path: data/en-fa.csv
- config_name: en-id
data_files:
- split: test
path: data/en-id.csv
- config_name: en-pl
data_files:
- split: test
path: data/en-pl.csv
- config_name: en-sq
data_files:
- split: test
path: data/en-sq.csv
- config_name: en-ar
data_files:
- split: test
path: data/en-ar.csv
- config_name: en-de
data_files:
- split: test
path: data/en-de.csv
- config_name: en-fi
data_files:
- split: test
path: data/en-fi.csv
- config_name: en-it
data_files:
- split: test
path: data/en-it.csv
- config_name: en-pt_br
data_files:
- split: test
path: data/en-pt_br.csv
- config_name: en-sr
data_files:
- split: test
path: data/en-sr.csv
- config_name: en-be
data_files:
- split: test
path: data/en-be.csv
- config_name: en-el
data_files:
- split: test
path: data/en-el.csv
- config_name: en-fr
data_files:
- split: test
path: data/en-fr.csv
- config_name: en-lt
data_files:
- split: test
path: data/en-lt.csv
- config_name: en-pt
data_files:
- split: test
path: data/en-pt.csv
- config_name: en-sv
data_files:
- split: test
path: data/en-sv.csv
- config_name: en-bs
data_files:
- split: test
path: data/en-bs.csv
- config_name: en-es
data_files:
- split: test
path: data/en-es.csv
- config_name: en-he
data_files:
- split: test
path: data/en-he.csv
- config_name: en-lv
data_files:
- split: test
path: data/en-lv.csv
- config_name: en-ro
data_files:
- split: test
path: data/en-ro.csv
- config_name: en-tr
data_files:
- split: test
path: data/en-tr.csv
- config_name: en-uk
data_files:
- split: test
path: data/en-uk.csv
license: cc-by-nc-sa-4.0
---
# LTR
LTR -- Lingvanex Test References for MT Evaluation from English into a total of 30 target languages for a big variety of cases.
## TEST CASES
| Parameter | Description |
|-----------|-------------|
| Length | Sentences from 1 to 100 words. |
| Domain | Medicine (12%), Automobile (11%), Finance (8%) |
| Tokenizer | Jupiter is 1.000.000 km far. Ask Mr. Johnson for training |
| Tags | I want to eat <tag> and swim |
| Capitalisation (Case) | HELLO my Dear frIEND |
| Different languages in one text (Up to 3 languages) | I see "Купалинка" performance near the theater. |
| Styling | Hello Dude! |
| Errors (Grammar, OCR) | I neet (need) to buy a kat (cat)|
| Abbreviations | The model was named 15.BVcX-10 |
| Named Entities | Let’s go to New York city |
| Idioms | A piece of cake. Once in a blue moon’ |
| Formulas (Math, Physics, Chemistry) | Cr2(SO4)3 + CO2 + H2O + K2SO4 + KNO3 |
| Romanian numbers | It was in MCMXVII year |
| Unicode Special Characters | №%&*/# |
# Updates
Data package has been released on 11/18/2023. Enjoy!
# License
The LTR data set is released under the [CC BY-SA 4.0 license](https://huggingface.co/datasets/lingvanex/lingvanex_test_references/blob/main/LICENSE.md).
# How to Cite
```
@inproceedings{
title = "{LTR} Lingvanex Test References for {MT} Evaluation",
author = "Aliaksei Rudak",
url = "https://lingvanex.com",
Year = "2023"
}
```
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Icaruas/book | 2023-10-24T14:55:50.000Z | [
"region:us"
] | Icaruas | null | null | 0 | 0 | 2023-10-24T13:22:58 | Entry not found | 15 | [
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] |
mekaneeky/Synthetic_Runyankole_MMS | 2023-10-24T13:41:37.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-24T13:28:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: runyankole_synthetic_audio
sequence:
sequence: float32
splits:
- name: train
num_bytes: 12669819104
num_examples: 23947
- name: dev
num_bytes: 268490157
num_examples: 500
- name: test
num_bytes: 262253648
num_examples: 500
download_size: 13209162621
dataset_size: 13200562909
---
# Dataset Card for "Synthetic_Runyankole_MMS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 896 | [
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] |
MilaNLProc/a-tale-of-pronouns | 2023-10-30T09:39:04.000Z | [
"license:apache-2.0",
"arxiv:2310.12127",
"region:us"
] | MilaNLProc | null | null | 0 | 0 | 2023-10-24T14:11:01 | ---
license: apache-2.0
pretty_name: A Tale of Pronouns
---
# A Tale of Pronouns: Attributions on WinoMT
This dataset contains the pre-computed feature attribution scores
relative to the paper [A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation](https://arxiv.org/abs/2310.12127).
## Dataset Details
We release the integrated gradient token-level attributions computed for each WinoMT translated example into Spanish and German
with Flan-T5-XXL and mtT0-XXL.
We computed the scores using [inseq](https://inseq.readthedocs.io/en/latest/).
The files here contain the FeatureAttributionOutput saved with the library's `save()` utility.
Therefore, you can load them using the `load()`. Please refer to inseq's doc to know more details.
Note that each file in the repository is compressed (`gz`) and contains a variable number of samples
(you can check how many samples by 1) loading the file into a variable (e.g., `out`) and print `len(out.sequence_attributions)`.
Each set of attribution must be loaded with a specific `inseq` version. See details below:
- Flan-T5-XXL (En-Es): v0.5.0
- Flan-Tf-XXL (En-De): TBD
- mT0-XXL: v0.4.0
- mT0-XXL: v0.4.0
If you are having issue in loading the weights, feel free to reach out.
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [Giuseppe Attanasio](https://gattanasio.cc)
- **Language(s) (NLP):** Spanish, German
- **License:** Apache 2
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [GitHub](https://github.com/MilaNLProc/interpretability-mt-gender-bias)
- **Paper:** [arxiv](https://arxiv.org/abs/2310.12127) (to appear in EMNLP 2023)
### Recommendations
TBD
**BibTeX:**
```bibtex
@article{attanasio2023tale,
title={A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation},
author={Attanasio, Giuseppe and Plaza-del-Arco, Flor Miriam and Nozza, Debora and Lauscher, Anne},
journal={arXiv preprint arXiv:2310.12127},
year={2023}
}
```
## Dataset Card Contact
[Giuseppe Attanasio](mailto:giuseppeattanasio6@gmail.com) | 2,220 | [
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Globaly/familias-final | 2023-10-24T14:29:35.000Z | [
"region:us"
] | Globaly | null | null | 0 | 0 | 2023-10-24T14:29:35 | Entry not found | 15 | [
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] |
PDBEurope/protein_structure_NER_model_v1.2 | 2023-11-01T09:51:43.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-24T14:43:14 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v1.2
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.2
There are 19 different entity types in this dataset:
"chemical", "complex_assembly", "evidence", "experimental_method", "gene", "mutant",
"oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, development
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1121 | 1121 | 204 |
| PMC4784909 | 865 | 865 | 204 |
| PMC4850288 | 716 | 708 | 146 |
| PMC4887326 | 933 | 933 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1229 | 1218 | 250 |
| PMC4786784 | 1549 | 1549 | 232 |
| PMC4848090 | 987 | 985 | 191 |
| PMC4792962 | 1268 | 1268 | 256 |
| total | 10451 | 10409 | 1961 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.2"; 1961 sentences
* train.tsv --> training subset of the data; 1372 sentences
* dev.tsv --> development subset of the data; 294 sentences
* test.tsv --> testing subset of the data; 295 sentences
The total number of annotations is: 10409
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
``` | 8,559 | [
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derek-thomas/bestofredditorupdates_history | 2023-10-24T15:05:10.000Z | [
"region:us"
] | derek-thomas | null | null | 0 | 0 | 2023-10-24T14:45:45 | Entry not found | 15 | [
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Chidera/mechanic-gpt | 2023-10-24T14:46:27.000Z | [
"region:us"
] | Chidera | null | null | 0 | 0 | 2023-10-24T14:46:27 | Entry not found | 15 | [
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] |
PDBEurope/protein_structure_NER_model_v1.4 | 2023-11-01T10:00:43.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-24T14:54:44 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v1.4
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.4
There are 19 different entity types in this dataset:
"chemical", "complex_assembly", "evidence", "experimental_method", "gene", "mutant",
"oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1121 | 1121 | 204 |
| PMC4784909 | 865 | 865 | 204 |
| PMC4850288 | 716 | 708 | 146 |
| PMC4887326 | 933 | 933 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1229 | 1218 | 250 |
| PMC4786784 | 1549 | 1549 | 232 |
| PMC4848090 | 987 | 985 | 191 |
| PMC4792962 | 1268 | 1268 | 256 |
| PMC4841544 | 1434 | 1433 | 273 |
| PMC4772114 | 825 | 825 | 166 |
| PMC4872110 | 1276 | 1276 | 253 |
| PMC4848761 | 887 | 883 | 252 |
| PMC4919469 | 1628 | 1616 | 336 |
| PMC4880283 | 771 | 771 | 166 |
| PMC4937829 | 625 | 625 | 181 |
| PMC4968113 | 1238 | 1238 | 292 |
| PMC4854314 | 481 | 471 | 139 |
| PMC4871749 | 383 | 383 | 76 |
| total | 19999 | 19930 | 4095 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.4"; 4095 sentences
* train.tsv --> training subset of the data; 2866 sentences
* dev.tsv --> development subset of the data; 614 sentences
* test.tsv --> testing subset of the data; 615 sentences
The total number of annotations is: 19930
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
PDBEurope/protein_structure_NER_model_v2.1 | 2023-11-01T10:19:37.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-24T14:57:29 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v2.1
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1129 | 1129 | 205 |
| PMC4784909 | 868 | 868 | 204 |
| PMC4850288 | 718 | 710 | 146 |
| PMC4887326 | 942 | 942 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1239 | 1228 | 250 |
| PMC4786784 | 1573 | 1573 | 232 |
| PMC4848090 | 1002 | 1000 | 192 |
| PMC4792962 | 1297 | 1297 | 256 |
| PMC4841544 | 1460 | 1459 | 274 |
| PMC4772114 | 824 | 824 | 165 |
| PMC4872110 | 1283 | 1283 | 250 |
| PMC4848761 | 888 | 884 | 252 |
| PMC4919469 | 1636 | 1624 | 336 |
| PMC4880283 | 783 | 783 | 166 |
| PMC4968113 | 1245 | 1245 | 292 |
| PMC4937829 | 633 | 633 | 181 |
| PMC4854314 | 498 | 488 | 139 |
| PMC4871749 | 411 | 411 | 79 |
| PMC4869123 | 922 | 922 | 195 |
| PMC4888278 | 580 | 580 | 102 |
| PMC4795551 | 1475 | 1475 | 297 |
| PMC4831588 | 1087 | 1070 | 224 |
| PMC4918766 | 1027 | 1027 | 210 |
| PMC4802042 | 1445 | 1445 | 268 |
| PMC4896748 | 2652 | 2638 | 480 |
| PMC4781976 | 115 | 113 | 24 |
| PMC4802085 | 983 | 983 | 193 |
| PMC4887163 | 856 | 856 | 196|
| total | 31354 | 31252 | 6286 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1"; 6286 sentences
* train.tsv --> training subset of the data; 4400 sentences
* dev.tsv --> development subset of the data; 943 sentences
* test.tsv --> testing subset of the data; 943 sentences
The total number of annotations is: 31252
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
PDBEurope/protein_structure_NER_model_v3.1 | 2023-11-01T10:27:48.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | PDBEurope | null | null | 0 | 0 | 2023-10-24T15:00:15 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v3.1
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1129 | 1129 | 205 |
| PMC4784909 | 868 | 868 | 204 |
| PMC4850288 | 717 | 709 | 146 |
| PMC4887326 | 942 | 942 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1239 | 1228 | 250 |
| PMC4786784 | 1573 | 1573 | 232 |
| PMC4848090 | 1000 | 998 | 192 |
| PMC4792962 | 1297 | 1297 | 256 |
| PMC4841544 | 1460 | 1459 | 274 |
| PMC4772114 | 824 | 824 | 165 |
| PMC4872110 | 1283 | 1283 | 250 |
| PMC4848761 | 888 | 884 | 252 |
| PMC4919469 | 1636 | 1624 | 336 |
| PMC4880283 | 783 | 783 | 166 |
| PMC4968113 | 1245 | 1245 | 292 |
| PMC4937829 | 633 | 633 | 181 |
| PMC4854314 | 498 | 488 | 139 |
| PMC4871749 | 411 | 411 | 79 |
| PMC4869123 | 922 | 922 | 195 |
| PMC4888278 | 580 | 580 | 102 |
| PMC4795551 | 1475 | 1475 | 297 |
| PMC4831588 | 1087 | 1070 | 224 |
| PMC4918766 | 1027 | 1027 | 210 |
| PMC4802042 | 1441 | 1441 | 264 |
| PMC4896748 | 2652 | 2638 | 480 |
| PMC4781976 | 115 | 113 | 24 |
| PMC4802085 | 983 | 983 | 193 |
| PMC4887163 | 856 | 856 | 196|
| PMC4918759 | 803 | 803 | 175 |
| PMC4855620 | 563 | 563 | 122 |
| PMC4822050 | 1521 | 1521 | 249 |
| PMC4822561 | 367 | 366 | 84 |
| PMC4885502 | 577 | 577 | 97 |
| PMC4746701 | 1130 | 1130 | 245 |
| PMC4820378 | 733 | 733 | 170 |
| PMC4773095 | 1323 | 1323 | 252 |
| PMC4857006 | 1358 | 1358 | 249 |
| PMC4774019 | 532 | 530 | 117 |
| total | 40254 | 40149 | 8042 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1"; 8042 sentences
* train.tsv --> training subset of the data; 5629 sentences
* dev.tsv --> development subset of the data; 1206 sentences
* test.tsv --> testing subset of the data; 1207 sentences
The total number of annotations is: 40149
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
mevol/protein_structure_NER_model_v1.2 | 2023-11-01T09:50:22.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | mevol | null | null | 0 | 0 | 2023-10-24T15:03:08 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v1.2
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.2
There are 19 different entity types in this dataset:
"chemical", "complex_assembly", "evidence", "experimental_method", "gene", "mutant",
"oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1121 | 1121 | 204 |
| PMC4784909 | 865 | 865 | 204 |
| PMC4850288 | 716 | 708 | 146 |
| PMC4887326 | 933 | 933 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1229 | 1218 | 250 |
| PMC4786784 | 1549 | 1549 | 232 |
| PMC4848090 | 987 | 985 | 191 |
| PMC4792962 | 1268 | 1268 | 256 |
| total | 10451 | 10409 | 1961 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat (https://www.teamtat.org/). More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.2"; 1961 sentences
* train.tsv --> training subset of the data; 1372 sentences
* dev.tsv --> development subset of the data; 294 sentences
* test.tsv --> testing subset of the data; 295 sentences
The total number of annotations is: 10409
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
mevol/protein_structure_NER_model_v1.4 | 2023-11-01T10:00:18.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | mevol | null | null | 0 | 0 | 2023-10-24T15:04:04 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v1.4
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.4
There are 19 different entity types in this dataset:
"chemical", "complex_assembly", "evidence", "experimental_method", "gene", "mutant",
"oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1121 | 1121 | 204 |
| PMC4784909 | 865 | 865 | 204 |
| PMC4850288 | 716 | 708 | 146 |
| PMC4887326 | 933 | 933 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1229 | 1218 | 250 |
| PMC4786784 | 1549 | 1549 | 232 |
| PMC4848090 | 987 | 985 | 191 |
| PMC4792962 | 1268 | 1268 | 256 |
| PMC4841544 | 1434 | 1433 | 273 |
| PMC4772114 | 825 | 825 | 166 |
| PMC4872110 | 1276 | 1276 | 253 |
| PMC4848761 | 887 | 883 | 252 |
| PMC4919469 | 1628 | 1616 | 336 |
| PMC4880283 | 771 | 771 | 166 |
| PMC4937829 | 625 | 625 | 181 |
| PMC4968113 | 1238 | 1238 | 292 |
| PMC4854314 | 481 | 471 | 139 |
| PMC4871749 | 383 | 383 | 76 |
| total | 19999 | 19930 | 4095 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v1.4"; 4095 sentences
* train.tsv --> training subset of the data; 2866 sentences
* dev.tsv --> development subset of the data; 614 sentences
* test.tsv --> testing subset of the data; 615 sentences
The total number of annotations is: 19930
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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mevol/protein_structure_NER_model_v2.1 | 2023-11-01T10:19:00.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | mevol | null | null | 0 | 0 | 2023-10-24T15:05:05 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v2.1
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1129 | 1129 | 205 |
| PMC4784909 | 868 | 868 | 204 |
| PMC4850288 | 718 | 710 | 146 |
| PMC4887326 | 942 | 942 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1239 | 1228 | 250 |
| PMC4786784 | 1573 | 1573 | 232 |
| PMC4848090 | 1002 | 1000 | 192 |
| PMC4792962 | 1297 | 1297 | 256 |
| PMC4841544 | 1460 | 1459 | 274 |
| PMC4772114 | 824 | 824 | 165 |
| PMC4872110 | 1283 | 1283 | 250 |
| PMC4848761 | 888 | 884 | 252 |
| PMC4919469 | 1636 | 1624 | 336 |
| PMC4880283 | 783 | 783 | 166 |
| PMC4968113 | 1245 | 1245 | 292 |
| PMC4937829 | 633 | 633 | 181 |
| PMC4854314 | 498 | 488 | 139 |
| PMC4871749 | 411 | 411 | 79 |
| PMC4869123 | 922 | 922 | 195 |
| PMC4888278 | 580 | 580 | 102 |
| PMC4795551 | 1475 | 1475 | 297 |
| PMC4831588 | 1087 | 1070 | 224 |
| PMC4918766 | 1027 | 1027 | 210 |
| PMC4802042 | 1445 | 1445 | 268 |
| PMC4896748 | 2652 | 2638 | 480 |
| PMC4781976 | 115 | 113 | 24 |
| PMC4802085 | 983 | 983 | 193 |
| PMC4887163 | 856 | 856 | 196|
| total | 31354 | 31252 | 6286 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1"; 6286 sentences
* train.tsv --> training subset of the data; 4400 sentences
* dev.tsv --> development subset of the data; 943 sentences
* test.tsv --> testing subset of the data; 943 sentences
The total number of annotations is: 31252
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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] |
mevol/protein_structure_NER_model_v3.1 | 2023-11-01T10:27:27.000Z | [
"language:en",
"license:mit",
"biology",
"protein structure",
"token classification",
"region:us"
] | mevol | null | null | 0 | 0 | 2023-10-24T15:06:19 | ---
license: mit
language:
- en
tags:
- biology
- protein structure
- token classification
configs:
- config_name: protein_structure_NER_model_v3.1
data_files:
- split: train
path: "annotation_IOB/train.tsv"
- split: dev
path: "annotation_IOB/dev.tsv"
- split: test
path: "annotation_IOB/test.tsv"
---
## Overview
This data was used to train model:
https://huggingface.co/mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1
There are 20 different entity types in this dataset:
"bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene",
"mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name",
"residue_name_number","residue_number", "residue_range", "site", "species", "structure_element",
"taxonomy_domain"
The data prepared as IOB formated input has been used during training, develiopment
and testing. Additional data formats such as JSON and XML as well as CSV files are
also available and are described below.
Annotation was carried out with the free annotation tool TeamTat (https://www.teamtat.org/) and
documents were downloaded as BioC XML before converting them to IOB, annotation only JSON and CSV format.
The number of annotations and sentences in each file is given below:
| document ID | number of annotations in BioC XML | number of annotations in IOB/JSON/CSV | number of sentences |
| --- | --- | --- | --- |
| PMC4850273 | 1129 | 1129 | 205 |
| PMC4784909 | 868 | 868 | 204 |
| PMC4850288 | 717 | 709 | 146 |
| PMC4887326 | 942 | 942 | 152 |
| PMC4833862 | 1044 | 1044 | 192 |
| PMC4832331 | 739 | 718 | 134 |
| PMC4852598 | 1239 | 1228 | 250 |
| PMC4786784 | 1573 | 1573 | 232 |
| PMC4848090 | 1000 | 998 | 192 |
| PMC4792962 | 1297 | 1297 | 256 |
| PMC4841544 | 1460 | 1459 | 274 |
| PMC4772114 | 824 | 824 | 165 |
| PMC4872110 | 1283 | 1283 | 250 |
| PMC4848761 | 888 | 884 | 252 |
| PMC4919469 | 1636 | 1624 | 336 |
| PMC4880283 | 783 | 783 | 166 |
| PMC4968113 | 1245 | 1245 | 292 |
| PMC4937829 | 633 | 633 | 181 |
| PMC4854314 | 498 | 488 | 139 |
| PMC4871749 | 411 | 411 | 79 |
| PMC4869123 | 922 | 922 | 195 |
| PMC4888278 | 580 | 580 | 102 |
| PMC4795551 | 1475 | 1475 | 297 |
| PMC4831588 | 1087 | 1070 | 224 |
| PMC4918766 | 1027 | 1027 | 210 |
| PMC4802042 | 1441 | 1441 | 264 |
| PMC4896748 | 2652 | 2638 | 480 |
| PMC4781976 | 115 | 113 | 24 |
| PMC4802085 | 983 | 983 | 193 |
| PMC4887163 | 856 | 856 | 196|
| PMC4918759 | 803 | 803 | 175 |
| PMC4855620 | 563 | 563 | 122 |
| PMC4822050 | 1521 | 1521 | 249 |
| PMC4822561 | 367 | 366 | 84 |
| PMC4885502 | 577 | 577 | 97 |
| PMC4746701 | 1130 | 1130 | 245 |
| PMC4820378 | 733 | 733 | 170 |
| PMC4773095 | 1323 | 1323 | 252 |
| PMC4857006 | 1358 | 1358 | 249 |
| PMC4774019 | 532 | 530 | 117 |
| total | 40254 | 40149 | 8042 |
Documents and annotations are easiest viewed by using the BioC XML files and opening
them in free annotation tool TeamTat. More about the BioC
format can be found here: https://bioc.sourceforge.net/
## Raw BioC XML files
These are the raw, un-annotated XML files for the publications in the dataset in BioC format.
The files are found in the directory: "raw_BioC_XML".
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_raw.xml.
## Annotations in IOB format
The IOB formated files can be found in the directory: "annotation_IOB"
The four files are as follows:
* all.tsv --> all sentences and annotations used to create model
"mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v3.1"; 8042 sentences
* train.tsv --> training subset of the data; 5629 sentences
* dev.tsv --> development subset of the data; 1206 sentences
* test.tsv --> testing subset of the data; 1207 sentences
The total number of annotations is: 40149
## Annotations in BioC JSON
The BioC formated JSON files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_JSON"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID"_ann.json
Each document JSON contains the following relevant keys:
* "sourceid" --> giving the numerical part of the unique PubMedCentral ID
* "text" --> containing the complete raw text of the publication as a string
* "denotations" --> containing a list of all the annotations for the text
Each annotation is a dictionary with the following keys:
* "span" --> gives the start and end of the annotatiom span defined by sub keys:
* "begin" --> character start position of annotation
* "end" --> character end position of annotation
* "obj" --> a string containing a number of terms that can be separated by ","; the order
of the terms gives the following: entity type, reference to ontology, annotator,
time stamp
* "id" --> unique annotation ID
Here an example:
```json
[{"sourceid":"4784909",
"sourcedb":"",
"project":"",
"target":"",
"text":"",
"denotations":[{"span":{"begin":24,
"end":34},
"obj":"chemical,CHEBI:,melaniev@ebi.ac.uk,2023-03-21T15:19:42Z",
"id":"4500"},
{"span":{"begin":50,
"end":59},
"obj":"taxonomy_domain,DUMMY:,melaniev@ebi.ac.uk,2023-03-21T15:15:03Z",
"id":"1281"}]
}
]
```
## Annotations in BioC XML
The BioC formated XML files of the publications have been downloaded from the annotation
tool TeamTat. The files are found in the directory: "annotated_BioC_XML"
There is one file for each document and they follow standard naming
"unique PubMedCentral ID_ann.xml
The key XML tags to be able to visualise the annotations in TeamTat as well as extracting
them to create the training data are "passage" and "offset". The "passage" tag encloses a
text passage or paragraph to which the annotations are linked. "Offset" gives the passage/
paragraph offset and allows to determine the character starting and ending postions of the
annotations. The tag "text" encloses the raw text of the passage.
Each annotation in the XML file is tagged as below:
* "annotation id=" --> giving the unique ID of the annotation
* "infon key="type"" --> giving the entity type of the annotation
* "infon key="identifier"" --> giving a reference to an ontology for the annotation
* "infon key="annotator"" --> giving the annotator
* "infon key="updated_at"" --> providing a time stamp for annotation creation/update
* "location" --> start and end character positions for the annotated text span
* "offset" --> start character position as defined by offset value
* "length" --> length of the annotation span; sum of "offset" and "length" creates
the end character position
Here is a basic example of what the BioC XML looks like. Additional tags for document
management are not given. Please refer to the documenttation to find out more.
```xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE collection SYSTEM "BioC.dtd">
<collection>
<source>PMC</source>
<date>20140719</date>
<key>pmc.key</key>
<document>
<id>4784909</id>
<passage>
<offset>0</offset>
<text>The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle</text>
<annotation id="4500">
<infon key="type">chemical</infon>
<infon key="identifier">CHEBI:</infon>
<infon key="annotator">melaniev@ebi.ac.uk</infon>
<infon key="updated_at">2023-03-21T15:19:42Z</infon>
<location offset="24" length="10"/>
<text>Coenzyme A</text>
</annotation>
</passage>
</document>
</collection>
```
## Annotations in CSV
The annotations and the relevant sentences they have been found in have also been made
available as tab-separated CSV files, one for each publication in the dataset. The files can
be found in directory "annotation_CSV". Each file is named as "unique PubMedCentral ID".csv.
The column labels in the CSV files are as follows:
* "anno_start" --> character start position of the annotation
* "anno_end" --> character end position of the annotation
* "anno_text" --> text covered by the annotation
* "entity_type" --> entity type of the annotation
* "sentence" --> sentence text in which the annotation was found
* "section" --> publication section in which the annotation was found
## Annotations in JSON
A combined JSON file was created only containing the relevant sentences and associated
annotations for each publication in the dataset. The file can be found in directory
"annotation_JSON" under the name "annotations.json".
The following keys are used:
* "PMC4850273" --> unique PubMedCentral of the publication
* "annotations" --> list of dictionaries for the relevant, annotated sentences of the
document; each dictionary has the following sub keys
* "sid" --> unique sentence ID
* "sent" --> sentence text as string
* "section" --> publication section the sentence is in
* "ner" --> nested list of annotations; each sublist contains the following items:
start character position, end character position, annotation text,
entity type
Here is an example of a sentence and its annotations:
```json
{"PMC4850273": {"annotations":
[{"sid": 0,
"sent": "Molecular Dissection of Xyloglucan Recognition in a Prominent Human Gut Symbiont",
"section": "TITLE",
"ner": [
[24,34,"Xyloglucan","chemical"],
[62,67,"Human","species"],]
},]
}}
```
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virtualmodel/test | 2023-10-24T15:08:24.000Z | [
"region:us"
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] |
nadsoft/Syria_audio | 2023-10-24T15:31:11.000Z | [
"region:us"
] | nadsoft | null | null | 0 | 0 | 2023-10-24T15:21:55 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 9669767962.166
num_examples: 72377
download_size: 10562028174
dataset_size: 9669767962.166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Syria_audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 491 | [
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andyP/ro-paraphrase-bible | 2023-10-29T12:19:50.000Z | [
"task_categories:sentence-similarity",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"task_ids:semantic-similarity-classification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:ro",
"license:apache-2.0",
"sentence similarity",
"paraphrase",
"romanian",
"nlp",
"region:us"
] | andyP | null | null | 0 | 0 | 2023-10-24T16:09:37 | ---
license: apache-2.0
annotations_creators:
- machine-generated
language_creators:
- found
task_categories:
- sentence-similarity
language:
- ro
multilinguality:
- monolingual
tags:
- sentence similarity
- paraphrase
- romanian
- nlp
task_ids:
- text-scoring
- semantic-similarity-scoring
- semantic-similarity-classification
pretty_name: Romanian Bible Paraphrase Corpus
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: text1
dtype: string
- name: text2
dtype: string
- name: score
dtype: int
---
# Dataset Card for "Romanian Bible Paraphrase Corpus"
## 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
<!--
- **Paper:** Fake News Opensources
-->
- **Homepage:** [https://github.com/AndyTheFactory/ro-paraphrase-bible](https://github.com/AndyTheFactory/ro-paraphrase-bible)
- **Repository:** [https://github.com/AndyTheFactory/ro-paraphrase-bible](https://github.com/AndyTheFactory/ro-paraphrase-bible)
- **Point of Contact:** [Andrei Paraschiv](https://github.com/AndyTheFactory)
-
### Dataset Summary
A paraphprase corpus created from 10 different Romanian language Bible versions. Since the Bible has all paragraphs uniquely numbered an alignment between two
versions is straighforward.
We compiled a complete combination of paragraph pairs from the following Bible versions:
- Română Noul Testament Interconfesional 2009
- Biblia în versuri 2014
- Biblia Traducerea Fidela 2015
- Biblia în Versiune Actualizată 2018
- Ediția Dumitru Cornilescu revizuită 2022
- Noua Traducere Românească
- Noul Testament SBR 2023
- Versiunea Biblia Romano-Catolică 2020
- Biblia sau Sfânta Scriptură cu Trimiteri 1924, Dumitru Cornilescu
- Traducere Literală Cornilescu 1931
In order to provide a similarity score between two paragraphs, we use FuzzyWuzzy library to compute the partial_token_sort_ratio (PTSoR)
between the lemmatized versions of the two texts. The final similarity score between matching paragraphs was computed as follows:
> score = 50 + ptsor // 2
where ptsor = partial_token_sort_ratio(lemmatize(text1), lemmatize(text2))
We also provide a set of non-matching texts, generated from a random pairing of paragraphs.
The score for the non-matching paragraphs was computed using this formula:
> score = int(ptsor / 2 +10)
The final dataset contains 904,815 similar records and 218,977 non matching records, totaling 1,123,927
### Languages
Romanian
## Dataset Structure
### Data Instances
An example record looks as follows.
```
{
'text1': 'Poporul a început să strige: „Este glasul lui Dumnezeu, nu al unui om!”',
'text2': 'Norodul a strigat: „Glas de Dumnezeu, nu de om!”',
'score': 82
}
```
### Data Fields
- `text1`, `text2`: compared Paragraphs
- `score`: 0 - 100 matching score (integer) - records with score > 50 are matches
### Data Statistics
**Score Distribution**:
Statistics for matching record scores:
```
count 904950.000000
mean 83.998228
std 7.083741
min 50.000000
25% 80.000000
50% 84.000000
75% 89.000000
max 100.000000
```
Statistics for non-matching record scores:
```
count 218977.000000
mean 29.400873
std 3.339921
min 10.000000
25% 27.000000
50% 29.000000
75% 32.000000
max 40.000000
```
## Dataset Creation
### Curation Rationale
### Source Data
Romanian Language Bible translations
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
The dataset is biased towards some archaic language. Additionally, it involves religious figures and phrase constructions that are rarly used in the common language.
## Additional Information
### Dataset Curators
### Licensing Information
This data is available and distributed under Apache-2.0 license
### Citation Information
```
tbd
```
### Contributions | 5,283 | [
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fearvel/aki | 2023-10-24T18:16:17.000Z | [
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mekaneeky/Synthetic_Runyankole_VITS_22.5k | 2023-10-24T16:34:22.000Z | [
"region:us"
] | mekaneeky | null | null | 0 | 0 | 2023-10-24T16:19:12 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: runyankole_synthetic_audio
sequence:
sequence: float32
splits:
- name: train
num_bytes: 14611930432
num_examples: 23947
- name: dev
num_bytes: 304694860
num_examples: 500
- name: test
num_bytes: 324234504
num_examples: 500
download_size: 15255437028
dataset_size: 15240859796
---
# Dataset Card for "Synthetic_Runyankole_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 934 | [
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Haneen-12/Style | 2023-10-24T16:32:04.000Z | [
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Haneen-12/Style_transform | 2023-10-24T16:51:47.000Z | [
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khederwaaOne/audioNtext | 2023-10-24T17:18:24.000Z | [
"region:us"
] | khederwaaOne | null | null | 0 | 0 | 2023-10-24T17:17:30 | Entry not found | 15 | [
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] |
andstor/code_search_net_extended | 2023-10-24T17:26:32.000Z | [
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:code",
"license:other",
"region:us"
] | andstor | null | null | 0 | 0 | 2023-10-24T17:20:13 | ---
annotations_creators:
- no-annotation
language_creators:
- machine-generated
language:
- code
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
---
# Dataset Card for Extended CodeSearchNet corpus
| 301 | [
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BangumiBase/kon | 2023-10-25T10:02:33.000Z | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-24T17:29:17 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of K-on!
This is the image base of bangumi K-ON!, we detected 51 characters, 8731 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1392 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 1467 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 248 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 48 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 326 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 38 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 934 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 147 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 130 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 117 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 38 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 19 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 26 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 1185 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 35 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 241 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 45 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 90 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 119 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 11 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 15 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 208 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 30 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 28 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 26 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 949 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 23 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 51 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 37 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 25 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 20 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 37 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 19 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 26 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 17 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 16 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 13 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 29 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 27 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 22 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 13 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 29 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 15 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 7 | [Download](44/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 45 | 10 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 11 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 11 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 6 | [Download](48/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 49 | 13 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 331 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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rwitz/me | 2023-10-24T17:55:54.000Z | [
"region:us"
] | rwitz | null | null | 0 | 0 | 2023-10-24T17:54:46 | Entry not found | 15 | [
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danjacobellis/musdb_mp3 | 2023-10-24T17:56:22.000Z | [
"region:us"
] | danjacobellis | null | null | 0 | 0 | 2023-10-24T17:56:21 | ---
dataset_info:
features:
- name: name
dtype: string
- name: mixture
dtype:
audio:
sampling_rate: 44100
mono: false
decode: false
- name: bps
dtype: float32
splits:
- name: test
num_bytes: 1437.0
num_examples: 15
download_size: 3180
dataset_size: 1437.0
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "musdb_mp3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 573 | [
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gmaijoe-emailchaser/emailchaser-llm-subject-data-v0.0.1 | 2023-10-24T18:03:42.000Z | [
"region:us"
] | gmaijoe-emailchaser | null | null | 0 | 0 | 2023-10-24T18:03:40 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 512963
num_examples: 713
download_size: 70894
dataset_size: 512963
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emailchaser-llm-subject-data-v0.0.3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 458 | [
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] |
dumyy/Title_CC | 2023-10-24T18:39:55.000Z | [
"region:us"
] | dumyy | null | null | 0 | 0 | 2023-10-24T18:39:47 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
splits:
- name: train
num_bytes: 6364154
num_examples: 1846
- name: test
num_bytes: 6381296
num_examples: 1500
download_size: 1320245
dataset_size: 12745450
---
# Dataset Card for "Title_CC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 580 | [
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newsmediabias/news-bias-full-data | 2023-10-24T19:00:48.000Z | [
"region:us"
] | newsmediabias | null | null | 0 | 0 | 2023-10-24T18:51:24 | # Navigating News Narratives: A Media Bias Analysis Dataset
## Introduction
The growing prevalence of bias in news media critically influences public perceptions on various imperative subjects, encompassing areas like political standpoints, health, resource allocation, racial perspectives, age, gender biases, and climate change. In light of this, it's crucial for media outlets to uphold the ethics of providing precise information and heightening public cognizance about these pivotal issues. The necessity for a mechanism that could counteract the spread of fallacious or misleading details is thus underscored, aiming to rejuvenate public confidence in the media.
## Dataset Description
This dataset encompasses multiple dimensions of biases in news media, such as political inclinations, hate speech, toxicity, sexism, ageism, and more, establishing its distinctiveness in the realm of similar datasets. It's noteworthy that the dataset explicitly refrains from including any personally identifiable information (PII).
### Format
The data structure is tabulated as follows:
- **ID**: Unique numeric identifier.
- **Text**: The main content.
- **Dimension**: Descriptive category of the text.
- **Biased_Words**: A compilation of words regarded as biased.
- **Aspect**: Specific sub-topic within the main content.
- **Label**: Indicates the presence (True) or absence (False) of bias.
- **Aggregate Label**: Derived using various weighted formulae.
### Annotation Scheme
The labels and annotations in the dataset are generated through a system of Active Learning, cycling through:
- Manual Labeling
- Semi-Supervised Learning
- Human Verification
The scheme comprises:
- **Bias Label**: Specifies the degree of bias (e.g., no bias, mild, or strong).
- **Words/Phrases Level Biases**: Pinpoints specific biased terms or phrases.
- **Subjective Bias (Aspect)**: Highlights biases pertinent to content dimensions.
## Datasets Utilized
For a comprehensive perspective, various news categories like climate crisis summaries, occupational, spiritual/faith, etc., have been curated using RSS. Active learning has been harnessed to classify sentences based on their neutrality or degree of bias and identify biased terms.
Moreover, this compilation incorporates data from several reputable sources:
- **MBIC (media bias)**: [Source](https://zenodo.org/records/4474336)
- **Hyperpartisan news**: [Source](https://huggingface.co/datasets/hyperpartisan_news_detection)
- **Toxic comment classification**: [Kaggle Source](https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge)
- **Jigsaw Unintended Bias**: [Kaggle Source](https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification)
- **Age Bias**: [Harvard Source](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/L4OAKN)
- **Multi-dimensional news (Ukraine)**: [Source](https://zenodo.org/records/3885351#.ZF0KoxHMLtV)
- **Social biases**: [Source](https://maartensap.com/social-bias-frames/)
## Objective
This dataset aims to provide unrestricted, complimentary access, striving to facilitate its adoption among global AI researchers and practitioners. Emphasis is placed on ensuring easy accessibility and usage of the dataset.
## Licensing & Citation
If you leverage this dataset in your work, we kindly request you to cite us:
> Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
For any other datasets utilized in this project, please ensure appropriate attribution as detailed in the aforementioned list.
[Hugging Face Source](https://huggingface.co/datasets/newsmediabias/news-bias-full-data/edit/main/README.md) - Accessed on 10/24/2023. | 3,806 | [
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ostapeno/qa-openai_icl0_clen512_maxD-1_maxC5_0 | 2023-10-24T19:24:49.000Z | [
"region:us"
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metashx/BBM_IV_Round_1_09272023 | 2023-10-24T19:27:47.000Z | [
"region:us"
] | metashx | null | null | 0 | 0 | 2023-10-24T19:26:39 | Entry not found | 15 | [
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thesergiu/Visual-Traffic-Monitoring | 2023-10-24T19:34:54.000Z | [
"region:us"
] | thesergiu | null | null | 0 | 0 | 2023-10-24T19:27:38 | Entry not found | 15 | [
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] |
BangumiBase/fairytail | 2023-10-26T01:32:22.000Z | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | BangumiBase | null | null | 0 | 0 | 2023-10-24T19:45:11 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Fairy Tail
This is the image base of bangumi Fairy Tail, we detected 270 characters, 33650 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 1894 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 83 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 62 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 49 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 36 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 76 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 162 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 4062 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 52 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 210 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 441 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 481 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 2387 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 108 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 200 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 137 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 481 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 81 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 320 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 225 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 42 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 81 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 167 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 99 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 112 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 81 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 27 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 108 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 717 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 221 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 61 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 37 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 20 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 86 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 55 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 27 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 80 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 50 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 643 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 352 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 35 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 94 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 50 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 50 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 29 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 62 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 21 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 26 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 30 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 253 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 39 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 126 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 127 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
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| 55 | 34 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 46 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 109 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 72 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 45 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 25 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 59 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 53 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 56 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 149 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 311 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 64 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 58 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 53 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 21 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 27 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 57 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 44 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 26 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 8 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 29 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 37 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 379 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 188 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 1209 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 413 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 14 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 19 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 34 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 56 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 20 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 27 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 16 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 28 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 29 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 52 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 30 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 29 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 21 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 36 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 81 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 30 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 85 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 34 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 80 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 96 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
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| 102 | 57 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
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| 106 | 35 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 27 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 42 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 27 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 24 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 51 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 33 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 15 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 43 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 64 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 171 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 358 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 99 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 15 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 29 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 33 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 18 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 31 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 16 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 30 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
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| 171 | 30 | [Download](171/dataset.zip) |  |  |  |  |  |  |  |  |
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| 177 | 24 | [Download](177/dataset.zip) |  |  |  |  |  |  |  |  |
| 178 | 17 | [Download](178/dataset.zip) |  |  |  |  |  |  |  |  |
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| 183 | 427 | [Download](183/dataset.zip) |  |  |  |  |  |  |  |  |
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| 185 | 27 | [Download](185/dataset.zip) |  |  |  |  |  |  |  |  |
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| 187 | 23 | [Download](187/dataset.zip) |  |  |  |  |  |  |  |  |
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| 190 | 13 | [Download](190/dataset.zip) |  |  |  |  |  |  |  |  |
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| 193 | 21 | [Download](193/dataset.zip) |  |  |  |  |  |  |  |  |
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| 226 | 30 | [Download](226/dataset.zip) |  |  |  |  |  |  |  |  |
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| 230 | 30 | [Download](230/dataset.zip) |  |  |  |  |  |  |  |  |
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| 236 | 11 | [Download](236/dataset.zip) |  |  |  |  |  |  |  |  |
| 237 | 14 | [Download](237/dataset.zip) |  |  |  |  |  |  |  |  |
| 238 | 11 | [Download](238/dataset.zip) |  |  |  |  |  |  |  |  |
| 239 | 36 | [Download](239/dataset.zip) |  |  |  |  |  |  |  |  |
| 240 | 150 | [Download](240/dataset.zip) |  |  |  |  |  |  |  |  |
| 241 | 18 | [Download](241/dataset.zip) |  |  |  |  |  |  |  |  |
| 242 | 17 | [Download](242/dataset.zip) |  |  |  |  |  |  |  |  |
| 243 | 37 | [Download](243/dataset.zip) |  |  |  |  |  |  |  |  |
| 244 | 38 | [Download](244/dataset.zip) |  |  |  |  |  |  |  |  |
| 245 | 16 | [Download](245/dataset.zip) |  |  |  |  |  |  |  |  |
| 246 | 13 | [Download](246/dataset.zip) |  |  |  |  |  |  |  |  |
| 247 | 42 | [Download](247/dataset.zip) |  |  |  |  |  |  |  |  |
| 248 | 67 | [Download](248/dataset.zip) |  |  |  |  |  |  |  |  |
| 249 | 14 | [Download](249/dataset.zip) |  |  |  |  |  |  |  |  |
| 250 | 16 | [Download](250/dataset.zip) |  |  |  |  |  |  |  |  |
| 251 | 16 | [Download](251/dataset.zip) |  |  |  |  |  |  |  |  |
| 252 | 32 | [Download](252/dataset.zip) |  |  |  |  |  |  |  |  |
| 253 | 73 | [Download](253/dataset.zip) |  |  |  |  |  |  |  |  |
| 254 | 10 | [Download](254/dataset.zip) |  |  |  |  |  |  |  |  |
| 255 | 36 | [Download](255/dataset.zip) |  |  |  |  |  |  |  |  |
| 256 | 14 | [Download](256/dataset.zip) |  |  |  |  |  |  |  |  |
| 257 | 14 | [Download](257/dataset.zip) |  |  |  |  |  |  |  |  |
| 258 | 13 | [Download](258/dataset.zip) |  |  |  |  |  |  |  |  |
| 259 | 45 | [Download](259/dataset.zip) |  |  |  |  |  |  |  |  |
| 260 | 12 | [Download](260/dataset.zip) |  |  |  |  |  |  |  |  |
| 261 | 18 | [Download](261/dataset.zip) |  |  |  |  |  |  |  |  |
| 262 | 14 | [Download](262/dataset.zip) |  |  |  |  |  |  |  |  |
| 263 | 40 | [Download](263/dataset.zip) |  |  |  |  |  |  |  |  |
| 264 | 13 | [Download](264/dataset.zip) |  |  |  |  |  |  |  |  |
| 265 | 11 | [Download](265/dataset.zip) |  |  |  |  |  |  |  |  |
| 266 | 11 | [Download](266/dataset.zip) |  |  |  |  |  |  |  |  |
| 267 | 6 | [Download](267/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 268 | 8 | [Download](268/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 494 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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typeof/algebraic-stack | 2023-10-24T21:56:39.000Z | [
"task_categories:text-generation",
"size_categories:10B<n<100B",
"language:en",
"math",
"region:us"
] | typeof | null | null | 0 | 0 | 2023-10-24T20:12:06 | ---
task_categories:
- text-generation
language:
- en
tags:
- math
size_categories:
- 10B<n<100B
---
# **NOTE**: Please see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
This is a cherry-picked repackaging of the `algebraic-stack` segment from the proof-pile-2 dataset as parquet files
### License
see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
### Citation
see [EleutherAI/proof-pile-2](https://huggingface.co/datasets/EleutherAI/proof-pile-2/blob/main/README.md)
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mt-evan/image_captioning | 2023-10-24T20:28:51.000Z | [
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Violetmae14/image-to-sound-with-effects-using-videos | 2023-10-24T20:40:56.000Z | [
"license:artistic-2.0",
"region:us"
] | Violetmae14 | null | null | 0 | 0 | 2023-10-24T20:35:23 | ---
license: artistic-2.0
---
image to sounds using facebook: images here?
space: button, images and audios, | 108 | [
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NathanRoll/TalkBank_CA_CABNC_1 | 2023-10-24T21:29:58.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-24T21:18:44 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 17080612876
num_examples: 100
download_size: 17100223923
dataset_size: 17080612876
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CABNC_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 509 | [
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] |
NathanRoll/TalkBank_CA_CABNC_2 | 2023-10-24T21:48:27.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-24T21:37:39 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 16314017575
num_examples: 93
download_size: 16328534636
dataset_size: 16314017575
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CABNC_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 508 | [
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] |
NathanRoll/TalkBank_CA_CABNC_3 | 2023-10-24T21:49:16.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-24T21:49:14 | ---
dataset_info:
features:
- name: __index_level_0__
dtype: 'null'
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 582
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CABNC_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 441 | [
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] |
NathanRoll/TalkBank_CA_CABNC_4 | 2023-10-24T21:51:51.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-24T21:50:49 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 1619533958
num_examples: 10
download_size: 1622055892
dataset_size: 1619533958
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CABNC_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 505 | [
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netskink/hftesty | 2023-10-24T21:55:49.000Z | [
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] |
NathanRoll/TalkBank_CA_CABNC_5 | 2023-10-24T22:18:21.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-24T22:06:29 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 17917023985
num_examples: 99
download_size: 17934314304
dataset_size: 17917023985
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CABNC_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 508 | [
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stonet2000/robot_demos_with_state_reset | 2023-10-24T22:20:34.000Z | [
"task_categories:robotics",
"task_categories:reinforcement-learning",
"language:en",
"license:mit",
"Robotics",
"Embodied AI",
"Imitation Learning",
"Learning from Demonstrations",
"State Reset",
"Simulation",
"region:us"
] | stonet2000 | null | null | 0 | 0 | 2023-10-24T22:06:59 | ---
license: mit
task_categories:
- robotics
- reinforcement-learning
language:
- en
tags:
- Robotics
- Embodied AI
- Imitation Learning
- Learning from Demonstrations
- State Reset
- Simulation
---
# Robot Demonstrations with Environment States
Dataset of robot demonstrations for various benchmarks formatted to contain environment states following the ManiSkill2 trajectory format: https://haosulab.github.io/ManiSkill2/concepts/demonstrations.html#format
Benchmarks supported:
- ManiSkill2
- Adroit (D4RL)
- Metaworld
For code to load and reset to states, as well as formatting the origianl demonstration datasets in supported benchmarks, see this repo: https://github.com/stonet2000/robot-state-reset-utils.
Data originally used for and generated from the [Reverse Forward Curriculum Learning for Extreme Sample and Demo Efficiency]() research project. | 862 | [
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AlexSaz/by | 2023-10-24T22:30:51.000Z | [
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kheopsai/data | 2023-10-24T23:01:03.000Z | [
"region:us"
] | kheopsai | null | null | 0 | 0 | 2023-10-24T23:00:20 | Entry not found | 15 | [
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] |
sidaga123/autotrain-data-dsadsad | 2023-10-24T23:02:41.000Z | [
"region:us"
] | sidaga123 | null | null | 0 | 0 | 2023-10-24T23:02:14 | ---
{}
---
# AutoTrain Dataset for project: dsadsad
## Dataset Description
This dataset has been automatically processed by AutoTrain for project dsadsad.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "Adjustment Card Transaction\nAccount XXXX9820\nCard XXXX5713\nCNY 896.2\nAmazon Sydney Australia\n25/09/23 18:30\nAvailable Balance CNY 249385.43",
"question": "What is the transaction currency?",
"answers.text": [
"CNY"
],
"answers.answer_start": [
59
]
},
{
"context": "Withdrawal Card Transaction\nAccount XXXX4602\nCard XXXX9502\nINR 59.17\nWalmart Tokyo Japan\n09/12/22 08:30\nAvailable Balance INR 92357.83",
"question": "What is the Merchant Name?",
"answers.text": [
"Walmart"
],
"answers.answer_start": [
69
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2400 |
| valid | 600 |
| 1,564 | [
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open-llm-leaderboard/details_shaohang__SparseOPT-1.3B | 2023-10-24T23:18:51.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-24T23:18:45 | ---
pretty_name: Evaluation run of shaohang/SparseOPT-1.3B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_shaohang__SparseOPT-1.3B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.003145973154362416,\n\
\ \"em_stderr\": 0.0005734993648436396,\n \"f1\": 0.048264471476510196,\n\
\ \"f1_stderr\": 0.0013025272598396927,\n \"acc\": 0.29319675461487227,\n\
\ \"acc_stderr\": 0.007301498172995543\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.003145973154362416,\n \"em_stderr\": 0.0005734993648436396,\n\
\ \"f1\": 0.048264471476510196,\n \"f1_stderr\": 0.0013025272598396927\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225237\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.585635359116022,\n \"acc_stderr\": 0.013844846232268563\n\
\ }\n}\n```"
repo_url: https://huggingface.co/shaohang/SparseOPT-1.3B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T23-18-41.395628.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-18-41.395628.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- '**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T23-18-41.395628.parquet'
- config_name: results
data_files:
- split: 2023_10_24T23_18_41.395628
path:
- results_2023-10-24T23-18-41.395628.parquet
- split: latest
path:
- results_2023-10-24T23-18-41.395628.parquet
---
# Dataset Card for Evaluation run of shaohang/SparseOPT-1.3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/shaohang/SparseOPT-1.3B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [shaohang/SparseOPT-1.3B](https://huggingface.co/shaohang/SparseOPT-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_shaohang__SparseOPT-1.3B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:18:41.395628](https://huggingface.co/datasets/open-llm-leaderboard/details_shaohang__SparseOPT-1.3B/blob/main/results_2023-10-24T23-18-41.395628.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927,
"acc": 0.29319675461487227,
"acc_stderr": 0.007301498172995543
},
"harness|drop|3": {
"em": 0.003145973154362416,
"em_stderr": 0.0005734993648436396,
"f1": 0.048264471476510196,
"f1_stderr": 0.0013025272598396927
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225237
},
"harness|winogrande|5": {
"acc": 0.585635359116022,
"acc_stderr": 0.013844846232268563
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,199 | [
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] |
sidaga123/autotrain-data-fdsfds | 2023-10-24T23:22:08.000Z | [
"region:us"
] | sidaga123 | null | null | 0 | 0 | 2023-10-24T23:19:19 | ---
{}
---
# AutoTrain Dataset for project: fdsfds
## Dataset Description
This dataset has been automatically processed by AutoTrain for project fdsfds.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "Levy Card Transaction\nAccount XXXX3724\nCard XXXX6333\nRUB 950.9\nMcDonald's Fast Food Berlin Germany\n16/02/23 16:26\nAvailable Balance RUB 265123.86",
"question": "What is the transaction type?",
"answers.text": [
"Debit"
],
"answers.answer_start": [
0
]
},
{
"context": "Purchase Card Transaction\nAccount XXXX8364\nCard XXXX8220\nMYR 221.08\nAmazon Online Marketplace Tokyo Japan\n24/11/22 00:51\nAvailable Balance MYR 386077.5",
"question": "What is the Merchant Name?",
"answers.text": [
"Amazon"
],
"answers.answer_start": [
68
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 2240 |
| valid | 560 |
| 1,581 | [
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open-llm-leaderboard/details_csitfun__llama-7b-logicot | 2023-10-24T23:33:12.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-24T23:33:02 | ---
pretty_name: Evaluation run of csitfun/llama-7b-logicot
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [csitfun/llama-7b-logicot](https://huggingface.co/csitfun/llama-7b-logicot) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_csitfun__llama-7b-logicot\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T23:32:58.123828](https://huggingface.co/datasets/open-llm-leaderboard/details_csitfun__llama-7b-logicot/blob/main/results_2023-10-24T23-32-58.123828.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0020973154362416107,\n\
\ \"em_stderr\": 0.0004685065030368161,\n \"f1\": 0.05921245805369133,\n\
\ \"f1_stderr\": 0.0013293292378975478,\n \"acc\": 0.3378058405682715,\n\
\ \"acc_stderr\": 0.006578612863320816\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0020973154362416107,\n \"em_stderr\": 0.0004685065030368161,\n\
\ \"f1\": 0.05921245805369133,\n \"f1_stderr\": 0.0013293292378975478\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.675611681136543,\n\
\ \"acc_stderr\": 0.013157225726641632\n }\n}\n```"
repo_url: https://huggingface.co/csitfun/llama-7b-logicot
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T23_32_58.123828
path:
- '**/details_harness|drop|3_2023-10-24T23-32-58.123828.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T23-32-58.123828.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T23_32_58.123828
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-32-58.123828.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-32-58.123828.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T23_32_58.123828
path:
- '**/details_harness|winogrande|5_2023-10-24T23-32-58.123828.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T23-32-58.123828.parquet'
- config_name: results
data_files:
- split: 2023_10_24T23_32_58.123828
path:
- results_2023-10-24T23-32-58.123828.parquet
- split: latest
path:
- results_2023-10-24T23-32-58.123828.parquet
---
# Dataset Card for Evaluation run of csitfun/llama-7b-logicot
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/csitfun/llama-7b-logicot
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [csitfun/llama-7b-logicot](https://huggingface.co/csitfun/llama-7b-logicot) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_csitfun__llama-7b-logicot",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:32:58.123828](https://huggingface.co/datasets/open-llm-leaderboard/details_csitfun__llama-7b-logicot/blob/main/results_2023-10-24T23-32-58.123828.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368161,
"f1": 0.05921245805369133,
"f1_stderr": 0.0013293292378975478,
"acc": 0.3378058405682715,
"acc_stderr": 0.006578612863320816
},
"harness|drop|3": {
"em": 0.0020973154362416107,
"em_stderr": 0.0004685065030368161,
"f1": 0.05921245805369133,
"f1_stderr": 0.0013293292378975478
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.675611681136543,
"acc_stderr": 0.013157225726641632
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,134 | [
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open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha | 2023-10-24T23:49:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-24T23:49:09 | ---
pretty_name: Evaluation run of HuggingFaceH4/zephyr-7b-alpha
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T23:49:05.604326](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha/blob/main/results_2023-10-24T23-49-05.604326.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.010171979865771811,\n\
\ \"em_stderr\": 0.001027595678252703,\n \"f1\": 0.0982225251677855,\n\
\ \"f1_stderr\": 0.00197861649884212,\n \"acc\": 0.46318334487213475,\n\
\ \"acc_stderr\": 0.010544787617759458\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.010171979865771811,\n \"em_stderr\": 0.001027595678252703,\n\
\ \"f1\": 0.0982225251677855,\n \"f1_stderr\": 0.00197861649884212\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14025777103866566,\n \
\ \"acc_stderr\": 0.00956510828142867\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090248\n\
\ }\n}\n```"
repo_url: https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T23_49_05.604326
path:
- '**/details_harness|drop|3_2023-10-24T23-49-05.604326.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T23-49-05.604326.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T23_49_05.604326
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-49-05.604326.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T23-49-05.604326.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T23_49_05.604326
path:
- '**/details_harness|winogrande|5_2023-10-24T23-49-05.604326.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T23-49-05.604326.parquet'
- config_name: results
data_files:
- split: 2023_10_24T23_49_05.604326
path:
- results_2023-10-24T23-49-05.604326.parquet
- split: latest
path:
- results_2023-10-24T23-49-05.604326.parquet
---
# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-alpha
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T23:49:05.604326](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-alpha/blob/main/results_2023-10-24T23-49-05.604326.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.010171979865771811,
"em_stderr": 0.001027595678252703,
"f1": 0.0982225251677855,
"f1_stderr": 0.00197861649884212,
"acc": 0.46318334487213475,
"acc_stderr": 0.010544787617759458
},
"harness|drop|3": {
"em": 0.010171979865771811,
"em_stderr": 0.001027595678252703,
"f1": 0.0982225251677855,
"f1_stderr": 0.00197861649884212
},
"harness|gsm8k|5": {
"acc": 0.14025777103866566,
"acc_stderr": 0.00956510828142867
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090248
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 7,247 | [
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] |
iamnguyen/ds_by_sys_prompt_1 | 2023-10-24T23:57:54.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-24T23:57:48 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 56262008.882733345
num_examples: 32987
download_size: 27809798
dataset_size: 56262008.882733345
---
# Dataset Card for "ds_by_sys_prompt_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
iamnguyen/ds_by_sys_prompt_2 | 2023-10-24T23:59:36.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-24T23:58:42 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 787059231.8499715
num_examples: 461461
download_size: 456893655
dataset_size: 787059231.8499715
---
# Dataset Card for "ds_by_sys_prompt_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
[
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] |
iamnguyen/ds_by_sys_prompt_3 | 2023-10-25T00:00:32.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:00:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 56945946.966280684
num_examples: 33388
download_size: 30077160
dataset_size: 56945946.966280684
---
# Dataset Card for "ds_by_sys_prompt_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
iamnguyen/ds_by_sys_prompt_4 | 2023-10-25T00:01:29.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:01:23 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 55803207.524942234
num_examples: 32718
download_size: 24839940
dataset_size: 55803207.524942234
---
# Dataset Card for "ds_by_sys_prompt_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
iamnguyen/ds_by_sys_prompt_5 | 2023-10-25T00:04:21.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:02:19 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1646317423.8362083
num_examples: 965253
download_size: 935877105
dataset_size: 1646317423.8362083
---
# Dataset Card for "ds_by_sys_prompt_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 586 | [
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mesolitica/embedding-pair-mining-malaysian-llama2-1b | 2023-11-01T05:18:41.000Z | [
"region:us"
] | mesolitica | null | null | 0 | 0 | 2023-10-25T00:03:07 | Entry not found | 15 | [
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Eididkd/Pedrinhonovo.zip | 2023-10-25T03:18:09.000Z | [
"region:us"
] | Eididkd | null | null | 0 | 0 | 2023-10-25T00:04:39 | Pederinhonovo.zip | 17 | [
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] |
iamnguyen/ds_by_sys_prompt_6 | 2023-10-25T00:06:05.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:05:11 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 885194966.1421082
num_examples: 518999
download_size: 354133820
dataset_size: 885194966.1421082
---
# Dataset Card for "ds_by_sys_prompt_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
iamnguyen/ds_by_sys_prompt_7 | 2023-10-25T00:07:01.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:06:54 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 80587008.7519407
num_examples: 47249
download_size: 24293019
dataset_size: 80587008.7519407
---
# Dataset Card for "ds_by_sys_prompt_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 580 | [
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] |
iamnguyen/ds_by_sys_prompt_8 | 2023-10-25T00:08:22.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:08:05 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 55085157.81628031
num_examples: 32297
download_size: 21824628
dataset_size: 55085157.81628031
---
# Dataset Card for "ds_by_sys_prompt_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 582 | [
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weillon/weillonvoz | 2023-10-25T00:08:41.000Z | [
"region:us"
] | weillon | null | null | 0 | 0 | 2023-10-25T00:08:07 | Entry not found | 15 | [
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] |
iamnguyen/ds_by_sys_prompt_9 | 2023-10-25T00:10:16.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:09:15 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 800642480.9705975
num_examples: 469425
download_size: 515492123
dataset_size: 800642480.9705975
---
# Dataset Card for "ds_by_sys_prompt_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 584 | [
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] |
iamnguyen/ds_by_sys_prompt_10 | 2023-10-25T00:11:09.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:11:02 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 80897424.54048337
num_examples: 47431
download_size: 24051168
dataset_size: 80897424.54048337
---
# Dataset Card for "ds_by_sys_prompt_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 583 | [
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] |
iamnguyen/ds_by_sys_prompt_11 | 2023-10-25T00:12:03.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:11:56 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 80187902.7381001
num_examples: 47015
download_size: 17360728
dataset_size: 80187902.7381001
---
# Dataset Card for "ds_by_sys_prompt_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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] |
iamnguyen/ds_by_sys_prompt_12 | 2023-10-25T00:12:59.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:12:52 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 76027990.055377
num_examples: 44576
download_size: 26594526
dataset_size: 76027990.055377
---
# Dataset Card for "ds_by_sys_prompt_12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 579 | [
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] |
iamnguyen/ds_by_sys_prompt_13 | 2023-10-25T00:13:59.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:13:52 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 75900071.46119733
num_examples: 44501
download_size: 24694465
dataset_size: 75900071.46119733
---
# Dataset Card for "ds_by_sys_prompt_13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 583 | [
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] |
iamnguyen/ds_by_sys_prompt_14 | 2023-10-25T00:14:54.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:14:47 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 55078335.4912574
num_examples: 32293
download_size: 27776587
dataset_size: 55078335.4912574
---
# Dataset Card for "ds_by_sys_prompt_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 581 | [
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] |
iamnguyen/ds_by_sys_prompt_15 | 2023-10-25T00:17:49.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:15:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 1636232321.8710828
num_examples: 959340
download_size: 1139633886
dataset_size: 1636232321.8710828
---
# Dataset Card for "ds_by_sys_prompt_15"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 588 | [
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] |
iamnguyen/ds_by_sys_prompt_16 | 2023-10-25T00:18:48.000Z | [
"region:us"
] | iamnguyen | null | null | 0 | 0 | 2023-10-25T00:18:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 133035.33794686393
num_examples: 78
download_size: 89536
dataset_size: 133035.33794686393
---
# Dataset Card for "ds_by_sys_prompt_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 579 | [
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] |
NathanRoll/TalkBank_CA_wM_TaiwanHakka | 2023-10-25T00:34:56.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:29:53 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 2411964909
num_examples: 44
download_size: 2413211183
dataset_size: 2411964909
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_TaiwanHakka"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 545 | [
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] |
NathanRoll/TalkBank_CA_wM_SBCSAE | 2023-10-25T00:42:28.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:39:20 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 5375202658
num_examples: 60
download_size: 5380925506
dataset_size: 5375202658
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_SBCSAE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 540 | [
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] |
NathanRoll/TalkBank_CA_wM_GCSAusE | 2023-10-25T00:44:25.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:43:55 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 832830161
num_examples: 36
download_size: 833725190
dataset_size: 832830161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_GCSAusE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 538 | [
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] |
NathanRoll/TalkBank_CA_wM_Croatian | 2023-10-25T00:54:58.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:51:40 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 5830164732
num_examples: 135
download_size: 5836483668
dataset_size: 5830164732
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_Croatian"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 543 | [
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] |
NathanRoll/TalkBank_CA_wM_Bergmann | 2023-10-25T00:57:04.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:56:58 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 170307012
num_examples: 83
download_size: 169370475
dataset_size: 170307012
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_Bergmann"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 539 | [
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] |
NathanRoll/TalkBank_CA_wM_CLAPI | 2023-10-25T00:59:45.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T00:58:50 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 1651019172
num_examples: 24
download_size: 1653522367
dataset_size: 1651019172
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_CLAPI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 539 | [
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] |
NathanRoll/TalkBank_CA_wM_CORAAL | 2023-10-25T01:12:13.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T01:06:39 | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: text
dtype: string
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 9337421051
num_examples: 85
download_size: 9344589919
dataset_size: 9337421051
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_CORAAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 540 | [
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NathanRoll/TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl | 2023-10-25T01:12:15.000Z | [
"region:us"
] | NathanRoll | null | null | 0 | 0 | 2023-10-25T01:12:13 | ---
dataset_info:
features:
- name: __index_level_0__
dtype: 'null'
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 582
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_wM_cv_gender_accent_50k_16kHz.pkl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 467 | [
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open-llm-leaderboard/details_Locutusque__gpt2-large-conversational | 2023-10-25T01:46:52.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 0 | 2023-10-25T01:46:44 | ---
pretty_name: Evaluation run of Locutusque/gpt2-large-conversational
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Locutusque/gpt2-large-conversational](https://huggingface.co/Locutusque/gpt2-large-conversational)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Locutusque__gpt2-large-conversational\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T01:46:41.651316](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-large-conversational/blob/main/results_2023-10-25T01-46-41.651316.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0026216442953020135,\n\
\ \"em_stderr\": 0.00052366856429659,\n \"f1\": 0.05192533557046985,\n\
\ \"f1_stderr\": 0.001344504704587554,\n \"acc\": 0.2805684988926939,\n\
\ \"acc_stderr\": 0.007353899944869109\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0026216442953020135,\n \"em_stderr\": 0.00052366856429659,\n\
\ \"f1\": 0.05192533557046985,\n \"f1_stderr\": 0.001344504704587554\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225207\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5603788476716653,\n \"acc_stderr\": 0.013949649776015698\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Locutusque/gpt2-large-conversational
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T01_46_41.651316
path:
- '**/details_harness|drop|3_2023-10-25T01-46-41.651316.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T01-46-41.651316.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T01_46_41.651316
path:
- '**/details_harness|gsm8k|5_2023-10-25T01-46-41.651316.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T01-46-41.651316.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T01_46_41.651316
path:
- '**/details_harness|winogrande|5_2023-10-25T01-46-41.651316.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T01-46-41.651316.parquet'
- config_name: results
data_files:
- split: 2023_10_25T01_46_41.651316
path:
- results_2023-10-25T01-46-41.651316.parquet
- split: latest
path:
- results_2023-10-25T01-46-41.651316.parquet
---
# Dataset Card for Evaluation run of Locutusque/gpt2-large-conversational
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Locutusque/gpt2-large-conversational
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Locutusque/gpt2-large-conversational](https://huggingface.co/Locutusque/gpt2-large-conversational) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Locutusque__gpt2-large-conversational",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T01:46:41.651316](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__gpt2-large-conversational/blob/main/results_2023-10-25T01-46-41.651316.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0026216442953020135,
"em_stderr": 0.00052366856429659,
"f1": 0.05192533557046985,
"f1_stderr": 0.001344504704587554,
"acc": 0.2805684988926939,
"acc_stderr": 0.007353899944869109
},
"harness|drop|3": {
"em": 0.0026216442953020135,
"em_stderr": 0.00052366856429659,
"f1": 0.05192533557046985,
"f1_stderr": 0.001344504704587554
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225207
},
"harness|winogrande|5": {
"acc": 0.5603788476716653,
"acc_stderr": 0.013949649776015698
}
}
```
### 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
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## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
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### Licensing Information
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### Citation Information
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### Contributions
[More Information Needed] | 7,343 | [
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] |
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