Upload batch 60 (20 files, last=huggingface_dataset/Dataset_Card/classla_FRENK-hate-en.md)
Browse files- huggingface_dataset/Dataset_Card/3DJedi_56chevy.md +101 -0
- huggingface_dataset/Dataset_Card/Ana85baul_autotrain-data-shapes.md +53 -0
- huggingface_dataset/Dataset_Card/BrunoHays_common_voice_11_0_processed.md +107 -0
- huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test-name__1653295318.md +12 -0
- huggingface_dataset/Dataset_Card/KGraph_FB15k-237.md +165 -0
- huggingface_dataset/Dataset_Card/Twitter_HashtagPrediction.md +109 -0
- huggingface_dataset/Dataset_Card/ai-characters_4elements-diffusion-captioned-dataset.md +22 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667119.md +34 -0
- huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-glue-mrpc-4a87ed-14445977.md +34 -0
- huggingface_dataset/Dataset_Card/bond005_sova_rudevices.md +118 -0
- huggingface_dataset/Dataset_Card/classla_FRENK-hate-en.md +139 -0
- huggingface_dataset/Dataset_Card/huggingartists_drake.md +204 -0
- huggingface_dataset/Dataset_Card/huggingartists_nervy.md +204 -0
- huggingface_dataset/Dataset_Card/irds_gov2_trec-tb-2006_named-page.md +49 -0
- huggingface_dataset/Dataset_Card/keremberke_blood-cell-object-detection.md +89 -0
- huggingface_dataset/Dataset_Card/librispeech_asr.md +305 -0
- huggingface_dataset/Dataset_Card/nlpso_m2m3_qualitative_analysis_ref_ptrn_cmbert_io.md +51 -0
- huggingface_dataset/Dataset_Card/poolrf2001_FaceMask.md +198 -0
- huggingface_dataset/Dataset_Card/tne.md +291 -0
- huggingface_dataset/Dataset_Card/totto.md +527 -0
huggingface_dataset/Dataset_Card/3DJedi_56chevy.md
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---
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{}
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---
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# Dataset Card for Dataset Name
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## Dataset Description
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| 8 |
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| 9 |
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- **Homepage:**
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- **Repository:**
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| 11 |
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- **Paper:**
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| 12 |
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- **Leaderboard:**
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| 13 |
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- **Point of Contact:**
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| 14 |
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### Dataset Summary
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| 16 |
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[More Information Needed]
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### Supported Tasks and Leaderboards
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[More Information Needed]
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### Languages
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[More Information Needed]
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## Dataset Structure
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### Data Instances
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[More Information Needed]
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### Data Fields
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[More Information Needed]
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### Data Splits
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[More Information Needed]
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| 40 |
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| 41 |
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## Dataset Creation
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| 42 |
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| 43 |
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### Curation Rationale
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| 44 |
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| 45 |
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[More Information Needed]
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| 46 |
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| 47 |
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### Source Data
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| 48 |
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#### Initial Data Collection and Normalization
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| 50 |
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[More Information Needed]
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| 52 |
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#### Who are the source language producers?
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[More Information Needed]
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| 56 |
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### Annotations
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| 58 |
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| 59 |
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#### Annotation process
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| 60 |
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[More Information Needed]
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| 62 |
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| 63 |
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#### Who are the annotators?
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| 64 |
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[More Information Needed]
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| 66 |
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| 67 |
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### Personal and Sensitive Information
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| 68 |
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| 69 |
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[More Information Needed]
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| 70 |
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| 71 |
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## Considerations for Using the Data
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| 72 |
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| 73 |
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### Social Impact of Dataset
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| 74 |
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| 75 |
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[More Information Needed]
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| 76 |
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| 77 |
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### Discussion of Biases
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| 78 |
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[More Information Needed]
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| 80 |
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| 81 |
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### Other Known Limitations
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| 82 |
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| 83 |
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[More Information Needed]
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| 84 |
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| 85 |
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## Additional Information
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| 86 |
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| 87 |
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### Dataset Curators
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| 88 |
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| 89 |
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[More Information Needed]
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| 90 |
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| 91 |
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### Licensing Information
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| 92 |
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| 93 |
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[More Information Needed]
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| 94 |
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| 95 |
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### Citation Information
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| 96 |
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| 97 |
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[More Information Needed]
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| 98 |
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| 99 |
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### Contributions
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| 100 |
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| 101 |
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[More Information Needed]
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huggingface_dataset/Dataset_Card/Ana85baul_autotrain-data-shapes.md
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---
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task_categories:
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- image-classification
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---
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# AutoTrain Dataset for project: shapes
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## Dataset Description
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| 9 |
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This dataset has been automatically processed by AutoTrain for project shapes.
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### Languages
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The BCP-47 code for the dataset's language is unk.
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## Dataset Structure
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### Data Instances
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| 19 |
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A sample from this dataset looks as follows:
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```json
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[
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{
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"image": "<28x28 RGB PIL image>",
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"target": 0
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},
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{
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"image": "<28x28 RGB PIL image>",
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"target": 2
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}
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]
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```
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### Dataset Fields
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| 36 |
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The dataset has the following fields (also called "features"):
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| 38 |
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```json
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{
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"image": "Image(decode=True, id=None)",
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"target": "ClassLabel(num_classes=3, names=['circles', 'squares', 'triangles'], id=None)"
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}
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```
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### Dataset Splits
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| 47 |
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This dataset is split into a train and validation split. The split sizes are as follow:
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| 49 |
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| Split name | Num samples |
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| ------------ | ------------------- |
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| train | 240 |
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| valid | 60 |
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huggingface_dataset/Dataset_Card/BrunoHays_common_voice_11_0_processed.md
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# common_voice_11_0_processed
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| 2 |
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## Dataset Description
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| 6 |
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| 8 |
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| 9 |
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### Dataset Summary
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| 10 |
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The data files can be found on the illuin gcloud instance at this adress: unknown_url
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| 12 |
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This dataset has been processed from Huggingface Hub dataset ``mozilla-foundation/common_voice_11_0`` and the config ``fr``
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| 14 |
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### Supported Tasks and Leaderboards
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| 16 |
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[More Information Needed]
|
| 18 |
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| 19 |
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### Languages
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| 20 |
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| 21 |
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[More Information Needed]
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| 22 |
+
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| 23 |
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## Dataset Structure
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| 24 |
+
|
| 25 |
+
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| 26 |
+
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| 27 |
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### Data Instances
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| 28 |
+
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| 29 |
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[More Information Needed]
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| 30 |
+
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| 31 |
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### Data Fields
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| 32 |
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| 33 |
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[More Information Needed]
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| 34 |
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#### Columns
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``path`` ``audio`` ``sentence`` ``taxonomy`` ``taxonomy_large`` ``sentence_processed``
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#### Sample
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| 40 |
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```
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{ 'audio': { 'array': array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
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-6.10351562e-05, -6.10351562e-05, 0.00000000e+00]),
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| 44 |
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'path': None,
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| 45 |
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'sampling_rate': 16000},
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| 46 |
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'path': '/home/brunohays/.cache/huggingface/datasets/downloads/extracted/e95cbf12249133734218c89e4b09426a7807adcae4a709e56bc54e89ec65bc58/common_voice_fr_27787676.mp3',
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| 47 |
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'sentence': 'Il collaborera aussi avec Alan Bennett sur différentes comédies et téléfilm dramatiques.',
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| 48 |
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'sentence_processed': 'il collaborera aussi avec alan bennett sur différentes comédies et téléfilm dramatiques',
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| 49 |
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'taxonomy': 'common_voice',
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| 50 |
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'taxonomy_large': 'common_voice'}
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```
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| 52 |
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### Data Splits
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| 54 |
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|split|number_of_rows|
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| 56 |
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|:---:|:---:
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| 57 |
+
|train|487011|
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| 58 |
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|test|15611|
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| 59 |
+
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| 60 |
+
## Dataset Creation
|
| 61 |
+
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| 62 |
+
|
| 63 |
+
|
| 64 |
+
### Curation Rationale
|
| 65 |
+
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| 66 |
+
[More Information Needed]
|
| 67 |
+
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| 68 |
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### Source Data
|
| 69 |
+
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| 70 |
+
[More Information Needed]
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| 71 |
+
|
| 72 |
+
### Annotations
|
| 73 |
+
|
| 74 |
+
[More Information Needed]
|
| 75 |
+
|
| 76 |
+
### Personal and Sensitive Information
|
| 77 |
+
|
| 78 |
+
[More Information Needed]
|
| 79 |
+
|
| 80 |
+
## Considerations for Using the dataset
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
### Discussion of Biases
|
| 85 |
+
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
|
| 88 |
+
### Other Known Limitations
|
| 89 |
+
|
| 90 |
+
[More Information Needed]
|
| 91 |
+
|
| 92 |
+
## Additional Information
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
|
| 96 |
+
### Dataset Curators
|
| 97 |
+
|
| 98 |
+
[More Information Needed]
|
| 99 |
+
|
| 100 |
+
### Licensing Information
|
| 101 |
+
|
| 102 |
+
Property of Illuin Technology
|
| 103 |
+
|
| 104 |
+
### Contributions
|
| 105 |
+
|
| 106 |
+
This dataset has been pushed using the repo [illuin-hf-dataset-pusher](https://gitlab.illuin.tech/data-science/ml/libraries/illuin-hf-dataset-pusher)
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| 107 |
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huggingface_dataset/Dataset_Card/GEM-submissions_lewtun__this-is-a-test-name__1653295318.md
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| 1 |
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---
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benchmark: gem
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type: prediction
|
| 4 |
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submission_name: This is a test name
|
| 5 |
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tags:
|
| 6 |
+
- evaluation
|
| 7 |
+
- benchmark
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| 8 |
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---
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| 9 |
+
# GEM Submission
|
| 10 |
+
|
| 11 |
+
Submission name: This is a test name
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| 12 |
+
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huggingface_dataset/Dataset_Card/KGraph_FB15k-237.md
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|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- found
|
| 4 |
+
- crowdsourced
|
| 5 |
+
language:
|
| 6 |
+
- en
|
| 7 |
+
language_creators: []
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-4.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: FB15k-237
|
| 13 |
+
size_categories:
|
| 14 |
+
- 100K<n<1M
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
tags:
|
| 18 |
+
- knowledge graph
|
| 19 |
+
- knowledge
|
| 20 |
+
- link prediction
|
| 21 |
+
- link
|
| 22 |
+
task_categories:
|
| 23 |
+
- other
|
| 24 |
+
task_ids: []
|
| 25 |
+
---
|
| 26 |
+
|
| 27 |
+
# Dataset Card for FB15k-237
|
| 28 |
+
|
| 29 |
+
## Table of Contents
|
| 30 |
+
- [Dataset Card for FB15k-237](#dataset-card-for-fb15k-237)
|
| 31 |
+
- [Table of Contents](#table-of-contents)
|
| 32 |
+
- [Dataset Description](#dataset-description)
|
| 33 |
+
- [Dataset Summary](#dataset-summary)
|
| 34 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 35 |
+
- [Languages](#languages)
|
| 36 |
+
- [Dataset Structure](#dataset-structure)
|
| 37 |
+
- [Data Instances](#data-instances)
|
| 38 |
+
- [Data Fields](#data-fields)
|
| 39 |
+
- [Data Splits](#data-splits)
|
| 40 |
+
- [Dataset Creation](#dataset-creation)
|
| 41 |
+
- [Curation Rationale](#curation-rationale)
|
| 42 |
+
- [Source Data](#source-data)
|
| 43 |
+
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
|
| 44 |
+
- [Who are the source language producers?](#who-are-the-source-language-producers)
|
| 45 |
+
- [Annotations](#annotations)
|
| 46 |
+
- [Annotation process](#annotation-process)
|
| 47 |
+
- [Who are the annotators?](#who-are-the-annotators)
|
| 48 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 49 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 50 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 51 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 52 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 53 |
+
- [Additional Information](#additional-information)
|
| 54 |
+
- [Dataset Curators](#dataset-curators)
|
| 55 |
+
- [Licensing Information](#licensing-information)
|
| 56 |
+
- [Citation Information](#citation-information)
|
| 57 |
+
- [Contributions](#contributions)
|
| 58 |
+
|
| 59 |
+
## Dataset Description
|
| 60 |
+
|
| 61 |
+
- **Homepage:** [https://deepai.org/dataset/fb15k-237](https://deepai.org/dataset/fb15k-237)
|
| 62 |
+
- **Repository:**
|
| 63 |
+
- **Paper:** [More Information Needed](https://paperswithcode.com/dataset/fb15k-237)
|
| 64 |
+
- **Leaderboard:**
|
| 65 |
+
- **Point of Contact:**
|
| 66 |
+
|
| 67 |
+
### Dataset Summary
|
| 68 |
+
|
| 69 |
+
FB15k-237 is a link prediction dataset created from FB15k. While FB15k consists of 1,345 relations, 14,951 entities, and 592,213 triples, many triples are inverses that cause leakage from the training to testing and validation splits. FB15k-237 was created by Toutanova and Chen (2015) to ensure that the testing and evaluation datasets do not have inverse relation test leakage. In summary, FB15k-237 dataset contains 310,079 triples with 14,505 entities and 237 relation types.
|
| 70 |
+
|
| 71 |
+
### Supported Tasks and Leaderboards
|
| 72 |
+
|
| 73 |
+
Supported Tasks: link prediction task on knowledge graphs.
|
| 74 |
+
|
| 75 |
+
Leaderboads:
|
| 76 |
+
[More Information Needed](https://paperswithcode.com/sota/link-prediction-on-fb15k-237)
|
| 77 |
+
|
| 78 |
+
### Languages
|
| 79 |
+
|
| 80 |
+
[More Information Needed]
|
| 81 |
+
|
| 82 |
+
## Dataset Structure
|
| 83 |
+
|
| 84 |
+
### Data Instances
|
| 85 |
+
|
| 86 |
+
[More Information Needed]
|
| 87 |
+
|
| 88 |
+
### Data Fields
|
| 89 |
+
|
| 90 |
+
[More Information Needed]
|
| 91 |
+
|
| 92 |
+
### Data Splits
|
| 93 |
+
|
| 94 |
+
[More Information Needed]
|
| 95 |
+
|
| 96 |
+
## Dataset Creation
|
| 97 |
+
|
| 98 |
+
### Curation Rationale
|
| 99 |
+
|
| 100 |
+
[More Information Needed]
|
| 101 |
+
|
| 102 |
+
### Source Data
|
| 103 |
+
|
| 104 |
+
#### Initial Data Collection and Normalization
|
| 105 |
+
|
| 106 |
+
[More Information Needed]
|
| 107 |
+
|
| 108 |
+
#### Who are the source language producers?
|
| 109 |
+
|
| 110 |
+
[More Information Needed]
|
| 111 |
+
|
| 112 |
+
### Annotations
|
| 113 |
+
|
| 114 |
+
#### Annotation process
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
#### Who are the annotators?
|
| 119 |
+
|
| 120 |
+
[More Information Needed]
|
| 121 |
+
|
| 122 |
+
### Personal and Sensitive Information
|
| 123 |
+
|
| 124 |
+
[More Information Needed]
|
| 125 |
+
|
| 126 |
+
## Considerations for Using the Data
|
| 127 |
+
|
| 128 |
+
### Social Impact of Dataset
|
| 129 |
+
|
| 130 |
+
[More Information Needed]
|
| 131 |
+
|
| 132 |
+
### Discussion of Biases
|
| 133 |
+
|
| 134 |
+
[More Information Needed]
|
| 135 |
+
|
| 136 |
+
### Other Known Limitations
|
| 137 |
+
|
| 138 |
+
[More Information Needed]
|
| 139 |
+
|
| 140 |
+
## Additional Information
|
| 141 |
+
|
| 142 |
+
### Dataset Curators
|
| 143 |
+
|
| 144 |
+
[More Information Needed]
|
| 145 |
+
|
| 146 |
+
### Licensing Information
|
| 147 |
+
|
| 148 |
+
[More Information Needed]
|
| 149 |
+
|
| 150 |
+
### Citation Information
|
| 151 |
+
|
| 152 |
+
```
|
| 153 |
+
@inproceedings{schlichtkrull2018modeling,
|
| 154 |
+
title={Modeling relational data with graph convolutional networks},
|
| 155 |
+
author={Schlichtkrull, Michael and Kipf, Thomas N and Bloem, Peter and Berg, Rianne van den and Titov, Ivan and Welling, Max},
|
| 156 |
+
booktitle={European semantic web conference},
|
| 157 |
+
pages={593--607},
|
| 158 |
+
year={2018},
|
| 159 |
+
organization={Springer}
|
| 160 |
+
}
|
| 161 |
+
```
|
| 162 |
+
|
| 163 |
+
### Contributions
|
| 164 |
+
|
| 165 |
+
Thanks to [@pp413](https://github.com/pp413) for adding this dataset.
|
huggingface_dataset/Dataset_Card/Twitter_HashtagPrediction.md
ADDED
|
@@ -0,0 +1,109 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: cc-by-4.0
|
| 3 |
+
language:
|
| 4 |
+
- sl
|
| 5 |
+
- ur
|
| 6 |
+
- sd
|
| 7 |
+
- pl
|
| 8 |
+
- vi
|
| 9 |
+
- sv
|
| 10 |
+
- am
|
| 11 |
+
- da
|
| 12 |
+
- mr
|
| 13 |
+
- no
|
| 14 |
+
- gu
|
| 15 |
+
- in
|
| 16 |
+
- ja
|
| 17 |
+
- el
|
| 18 |
+
- lv
|
| 19 |
+
- it
|
| 20 |
+
- ca
|
| 21 |
+
- is
|
| 22 |
+
- cs
|
| 23 |
+
- te
|
| 24 |
+
- tl
|
| 25 |
+
- ro
|
| 26 |
+
- ckb
|
| 27 |
+
- pt
|
| 28 |
+
- ps
|
| 29 |
+
- zh
|
| 30 |
+
- sr
|
| 31 |
+
- pa
|
| 32 |
+
- si
|
| 33 |
+
- ml
|
| 34 |
+
- ht
|
| 35 |
+
- kn
|
| 36 |
+
- ar
|
| 37 |
+
- hu
|
| 38 |
+
- nl
|
| 39 |
+
- bg
|
| 40 |
+
- bn
|
| 41 |
+
- ne
|
| 42 |
+
- hi
|
| 43 |
+
- de
|
| 44 |
+
- ko
|
| 45 |
+
- fi
|
| 46 |
+
- fr
|
| 47 |
+
- es
|
| 48 |
+
- et
|
| 49 |
+
- en
|
| 50 |
+
- fa
|
| 51 |
+
- lt
|
| 52 |
+
- or
|
| 53 |
+
- cy
|
| 54 |
+
- eu
|
| 55 |
+
- iw
|
| 56 |
+
- ta
|
| 57 |
+
- th
|
| 58 |
+
- tr
|
| 59 |
+
tags:
|
| 60 |
+
- Twitter
|
| 61 |
+
- Multilingual
|
| 62 |
+
- Classification
|
| 63 |
+
- Benchmark
|
| 64 |
+
---
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
# Hashtag Prediction Dataset from paper TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
|
| 68 |
+
[](https://huggingface.co/datasets/Twitter/HashtagPrediction/discussions) [](https://arxiv.org/abs/2209.07562) [](https://github.com/xinyangz/TwHIN-BERT)
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
This repo contains the Hashtag prediction dataset from our paper [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations](https://arxiv.org/abs/2209.07562). <br />
|
| 72 |
+
[[arXiv]](https://arxiv.org/abs/2209.07562)
|
| 73 |
+
[[HuggingFace Models]](https://huggingface.co/Twitter/twhin-bert-base)
|
| 74 |
+
[[Github repo]](https://github.com/xinyangz/TwHIN-BERT)
|
| 75 |
+
|
| 76 |
+
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
|
| 77 |
+
|
| 78 |
+
## Download
|
| 79 |
+
Use the `hashtag-classification-id.zip` in this repo. [Link](https://huggingface.co/datasets/Twitter/HashtagPrediction/blob/main/hashtag-classification-id.zip).
|
| 80 |
+
|
| 81 |
+
Check the first-author's GitHub repo for any supplemental dataset material or code. [Link](https://github.com/xinyangz/TwHIN-BERT)
|
| 82 |
+
|
| 83 |
+
## Dataset Description
|
| 84 |
+
|
| 85 |
+
The hashtag prediction dataset is a multilingual classification dataset. Separate datasets are given for different languages. We first select 500 (or all available) popular hashtags of each language and then sample 10k (or all available) popular Tweets that contain these hashtags. We make sure each Tweet will have exactly one of the selected hashtags.
|
| 86 |
+
|
| 87 |
+
The evaluation task is a multiclass classification task, with hashtags as labels. We remove the hashtag from the Tweet, and let the model predict the removed hashtag.
|
| 88 |
+
|
| 89 |
+
We provide Tweet ID and raw text hashtag labels in `tsv` files. For each language, we provide train, development, and test splits.
|
| 90 |
+
|
| 91 |
+
To use the dataset, you must hydrate the Tweet text with [Twitter API](https://developer.twitter.com/en/docs/twitter-api), and **remove the hashtag used for label from each Tweet** .
|
| 92 |
+
|
| 93 |
+
The data format is displayed below.
|
| 94 |
+
|
| 95 |
+
| ID | label |
|
| 96 |
+
| ------------- | ------------- |
|
| 97 |
+
| 1 | hashtag |
|
| 98 |
+
| 2 | another hashtag |
|
| 99 |
+
|
| 100 |
+
## Citation
|
| 101 |
+
If you use our dataset in your work, please cite the following:
|
| 102 |
+
```bib
|
| 103 |
+
@article{zhang2022twhin,
|
| 104 |
+
title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
|
| 105 |
+
author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
|
| 106 |
+
journal={arXiv preprint arXiv:2209.07562},
|
| 107 |
+
year={2022}
|
| 108 |
+
}
|
| 109 |
+
```
|
huggingface_dataset/Dataset_Card/ai-characters_4elements-diffusion-captioned-dataset.md
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
license: creativeml-openrail-m
|
| 6 |
+
tags:
|
| 7 |
+
- stable-diffusion
|
| 8 |
+
- text-to-image
|
| 9 |
+
- image-to-image
|
| 10 |
+
|
| 11 |
+
---
|
| 12 |
+
# 4elements-diffusion dataset
|
| 13 |
+
|
| 14 |
+
##### Dataset composed of 1142 manually captioned images used for my [4elements-diffusion](https://huggingface.co/ai-characters/4elements-diffusion) model
|
| 15 |
+
|
| 16 |
+
**Feel free to donate to my [KoFi](https://ko-fi.com/aicharacters)** to help me fund renting GPU's for further model creation and experimentation!
|
| 17 |
+
|
| 18 |
+
Follow me on [Twitter](https://twitter.com/ai_characters) and [Instagram](https://www.instagram.com/ai_characters/) for AI art posts and model updates!
|
| 19 |
+
|
| 20 |
+
[Alternative download link of this dataset](https://www.dropbox.com/s/iobslrmyvdoi8oy/1142%20images%2C%20manually%20captioned%2C%20manual%20and%20automatic%20cropping%2C%20downscaled%20from%201024x1024.7z?dl=1)
|
| 21 |
+
|
| 22 |
+
[Download link to the 14000 show screencaps used to build this dataset among other images](https://www.dropbox.com/s/406u0tv9xuttgku/14284%20images%2C%20512x512%2C%20automatically%20cropped%2C%20downscaled%20from%201080x1080.7z?dl=1)
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-mathemakitten__winobias_antistereotype_test_v5-mathemak-b6a817-2053667119.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- mathemakitten/winobias_antistereotype_test_v5
|
| 8 |
+
eval_info:
|
| 9 |
+
task: text_zero_shot_classification
|
| 10 |
+
model: inverse-scaling/opt-6.7b_eval
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: mathemakitten/winobias_antistereotype_test_v5
|
| 13 |
+
dataset_config: mathemakitten--winobias_antistereotype_test_v5
|
| 14 |
+
dataset_split: test
|
| 15 |
+
col_mapping:
|
| 16 |
+
text: text
|
| 17 |
+
classes: classes
|
| 18 |
+
target: target
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Zero-Shot Text Classification
|
| 25 |
+
* Model: inverse-scaling/opt-6.7b_eval
|
| 26 |
+
* Dataset: mathemakitten/winobias_antistereotype_test_v5
|
| 27 |
+
* Config: mathemakitten--winobias_antistereotype_test_v5
|
| 28 |
+
* Split: test
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model.
|
huggingface_dataset/Dataset_Card/autoevaluate_autoeval-staging-eval-glue-mrpc-4a87ed-14445977.md
ADDED
|
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
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|
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|
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|
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|
|
|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
type: predictions
|
| 3 |
+
tags:
|
| 4 |
+
- autotrain
|
| 5 |
+
- evaluation
|
| 6 |
+
datasets:
|
| 7 |
+
- glue
|
| 8 |
+
eval_info:
|
| 9 |
+
task: natural_language_inference
|
| 10 |
+
model: Intel/roberta-base-mrpc
|
| 11 |
+
metrics: []
|
| 12 |
+
dataset_name: glue
|
| 13 |
+
dataset_config: mrpc
|
| 14 |
+
dataset_split: validation
|
| 15 |
+
col_mapping:
|
| 16 |
+
text1: sentence1
|
| 17 |
+
text2: sentence2
|
| 18 |
+
target: label
|
| 19 |
+
---
|
| 20 |
+
# Dataset Card for AutoTrain Evaluator
|
| 21 |
+
|
| 22 |
+
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
|
| 23 |
+
|
| 24 |
+
* Task: Natural Language Inference
|
| 25 |
+
* Model: Intel/roberta-base-mrpc
|
| 26 |
+
* Dataset: glue
|
| 27 |
+
* Config: mrpc
|
| 28 |
+
* Split: validation
|
| 29 |
+
|
| 30 |
+
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
|
| 31 |
+
|
| 32 |
+
## Contributions
|
| 33 |
+
|
| 34 |
+
Thanks to [@xinhe](https://huggingface.co/xinhe) for evaluating this model.
|
huggingface_dataset/Dataset_Card/bond005_sova_rudevices.md
ADDED
|
@@ -0,0 +1,118 @@
|
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|
|
|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: RuDevices
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- expert-generated
|
| 5 |
+
language_creators:
|
| 6 |
+
- crowdsourced
|
| 7 |
+
language:
|
| 8 |
+
- ru
|
| 9 |
+
license:
|
| 10 |
+
- cc-by-4.0
|
| 11 |
+
multilinguality:
|
| 12 |
+
- monolingual
|
| 13 |
+
paperswithcode_id:
|
| 14 |
+
size_categories:
|
| 15 |
+
- 10K<n<100k
|
| 16 |
+
source_datasets:
|
| 17 |
+
- extended
|
| 18 |
+
task_categories:
|
| 19 |
+
- automatic-speech-recognition
|
| 20 |
+
- audio-classification
|
| 21 |
+
---
|
| 22 |
+
# Dataset Card for sova_rudevices
|
| 23 |
+
## Table of Contents
|
| 24 |
+
- [Dataset Description](#dataset-description)
|
| 25 |
+
- [Dataset Summary](#dataset-summary)
|
| 26 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 27 |
+
- [Languages](#languages)
|
| 28 |
+
- [Dataset Structure](#dataset-structure)
|
| 29 |
+
- [Data Instances](#data-instances)
|
| 30 |
+
- [Data Fields](#data-fields)
|
| 31 |
+
- [Data Splits](#data-splits)
|
| 32 |
+
- [Dataset Creation](#dataset-creation)
|
| 33 |
+
- [Curation Rationale](#curation-rationale)
|
| 34 |
+
- [Source Data](#source-data)
|
| 35 |
+
- [Annotations](#annotations)
|
| 36 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 37 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 38 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 39 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 40 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 41 |
+
- [Additional Information](#additional-information)
|
| 42 |
+
- [Dataset Curators](#dataset-curators)
|
| 43 |
+
- [Licensing Information](#licensing-information)
|
| 44 |
+
- [Citation Information](#citation-information)
|
| 45 |
+
- [Contributions](#contributions)
|
| 46 |
+
## Dataset Description
|
| 47 |
+
- **Homepage:** [SOVA RuDevices](https://github.com/sovaai/sova-dataset)
|
| 48 |
+
- **Repository:** [SOVA Dataset](https://github.com/sovaai/sova-dataset)
|
| 49 |
+
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
|
| 50 |
+
- **Point of Contact:** [SOVA.ai](mailto:support@sova.ai)
|
| 51 |
+
### Dataset Summary
|
| 52 |
+
SOVA Dataset is free public STT/ASR dataset. It consists of several parts, one of them is SOVA RuDevices. This part is an acoustic corpus of approximately 100 hours of 16kHz Russian live speech with manual annotating, prepared by [SOVA.ai team](https://github.com/sovaai).
|
| 53 |
+
|
| 54 |
+
Authors do not divide the dataset into train, validation and test subsets. Therefore, I was compelled to prepare this splitting. The training subset includes more than 82 hours, the validation subset includes approximately 6 hours, and the test subset includes approximately 6 hours too.
|
| 55 |
+
|
| 56 |
+
### Supported Tasks and Leaderboards
|
| 57 |
+
- `automatic-speech-recognition`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER.
|
| 58 |
+
### Languages
|
| 59 |
+
The audio is in Russian.
|
| 60 |
+
## Dataset Structure
|
| 61 |
+
### Data Instances
|
| 62 |
+
A typical data point comprises the audio data, usually called `audio` and its transcription, called `transcription`. Any additional information about the speaker and the passage which contains the transcription is not provided.
|
| 63 |
+
```
|
| 64 |
+
{'audio': {'path': '/home/bond005/datasets/sova_rudevices/data/train/00003ec0-1257-42d1-b475-db1cd548092e.wav',
|
| 65 |
+
'array': array([ 0.00787354, 0.00735474, 0.00714111, ...,
|
| 66 |
+
-0.00018311, -0.00015259, -0.00018311]), dtype=float32),
|
| 67 |
+
'sampling_rate': 16000},
|
| 68 |
+
'transcription': 'мне получше стало'}
|
| 69 |
+
```
|
| 70 |
+
### Data Fields
|
| 71 |
+
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
|
| 72 |
+
- transcription: the transcription of the audio file.
|
| 73 |
+
### Data Splits
|
| 74 |
+
This dataset consists of three splits: training, validation, and test. This splitting was realized with accounting of internal structure of SOVA RuDevices (the validation split is based on the subdirectory `0`, and the test split is based on the subdirectory `1` of the original dataset), but audio recordings of the same speakers can be in different splits at the same time (the opposite is not guaranteed).
|
| 75 |
+
| | Train | Validation | Test |
|
| 76 |
+
| ----- | ------ | ---------- | ----- |
|
| 77 |
+
| examples | 81607 | 5835 | 5799 |
|
| 78 |
+
| hours | 82.4h | 5.9h | 5.8h |
|
| 79 |
+
## Dataset Creation
|
| 80 |
+
### Curation Rationale
|
| 81 |
+
[Needs More Information]
|
| 82 |
+
### Source Data
|
| 83 |
+
#### Initial Data Collection and Normalization
|
| 84 |
+
[Needs More Information]
|
| 85 |
+
#### Who are the source language producers?
|
| 86 |
+
[Needs More Information]
|
| 87 |
+
### Annotations
|
| 88 |
+
#### Annotation process
|
| 89 |
+
All recorded audio files were manually annotated.
|
| 90 |
+
#### Who are the annotators?
|
| 91 |
+
[Needs More Information]
|
| 92 |
+
### Personal and Sensitive Information
|
| 93 |
+
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
|
| 94 |
+
## Considerations for Using the Data
|
| 95 |
+
### Social Impact of Dataset
|
| 96 |
+
[More Information Needed]
|
| 97 |
+
### Discussion of Biases
|
| 98 |
+
[More Information Needed]
|
| 99 |
+
### Other Known Limitations
|
| 100 |
+
[Needs More Information]
|
| 101 |
+
## Additional Information
|
| 102 |
+
### Dataset Curators
|
| 103 |
+
The dataset was initially created by Egor Zubarev, Timofey Moskalets, and SOVA.ai team.
|
| 104 |
+
### Licensing Information
|
| 105 |
+
[Creative Commons BY 4.0](https://creativecommons.org/licenses/by/4.0/)
|
| 106 |
+
### Citation Information
|
| 107 |
+
```
|
| 108 |
+
@misc{sova2021rudevices,
|
| 109 |
+
author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai},
|
| 110 |
+
title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},
|
| 111 |
+
publisher = {GitHub},
|
| 112 |
+
journal = {GitHub repository},
|
| 113 |
+
year = {2021},
|
| 114 |
+
howpublished = {\url{https://github.com/sovaai/sova-dataset}},
|
| 115 |
+
}
|
| 116 |
+
```
|
| 117 |
+
### Contributions
|
| 118 |
+
Thanks to [@bond005](https://github.com/bond005) for adding this dataset.
|
huggingface_dataset/Dataset_Card/classla_FRENK-hate-en.md
ADDED
|
@@ -0,0 +1,139 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
license:
|
| 5 |
+
- other
|
| 6 |
+
size_categories:
|
| 7 |
+
- 1K<n<10K
|
| 8 |
+
task_categories:
|
| 9 |
+
- text-classification
|
| 10 |
+
task_ids: []
|
| 11 |
+
tags:
|
| 12 |
+
- hate-speech-detection
|
| 13 |
+
- offensive-language
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
# Offensive language dataset of Croatian comments FRENK 1.0
|
| 17 |
+
|
| 18 |
+
English subset of the [FRENK dataset](http://hdl.handle.net/11356/1433). Also available on HuggingFace dataset hub: [Croatian subset](https://huggingface.co/datasets/5roop/FRENK-hate-hr), [Slovenian subset](https://huggingface.co/datasets/5roop/FRENK-hate-sl).
|
| 19 |
+
|
| 20 |
+
## Dataset Description
|
| 21 |
+
|
| 22 |
+
- **Homepage:** http://hdl.handle.net/11356/1433
|
| 23 |
+
|
| 24 |
+
- **Repository:** http://hdl.handle.net/11356/1433
|
| 25 |
+
|
| 26 |
+
- **Paper:** https://arxiv.org/abs/1906.02045
|
| 27 |
+
|
| 28 |
+
- **Project page** https://nl.ijs.si/frenk/
|
| 29 |
+
|
| 30 |
+
## Description of the original dataset
|
| 31 |
+
|
| 32 |
+
The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [https://arxiv.org/pdf/1906.02045.pdf]. Usernames in the metadata are pseudo-anonymised and removed from the comments.
|
| 33 |
+
|
| 34 |
+
The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.
|
| 35 |
+
|
| 36 |
+
For this dataset only the English data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).
|
| 37 |
+
|
| 38 |
+
|
| 39 |
+
## Usage in `Transformers`
|
| 40 |
+
```python
|
| 41 |
+
import datasets
|
| 42 |
+
ds = datasets.load_dataset("classla/FRENK-hate-en","binary")
|
| 43 |
+
```
|
| 44 |
+
|
| 45 |
+
For binary classification the following encoding is used:
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
```python
|
| 49 |
+
_CLASS_MAP_BINARY = {
|
| 50 |
+
'Acceptable': 0,
|
| 51 |
+
'Offensive': 1,
|
| 52 |
+
}
|
| 53 |
+
```
|
| 54 |
+
The original labels are available if the dataset is loaded with the `multiclass` option:
|
| 55 |
+
|
| 56 |
+
```python
|
| 57 |
+
import datasets
|
| 58 |
+
ds = datasets.load_dataset("5roop/FRENK-hate-en","multiclass").
|
| 59 |
+
```
|
| 60 |
+
|
| 61 |
+
In this case the encoding used is:
|
| 62 |
+
```python
|
| 63 |
+
_CLASS_MAP_MULTICLASS = {
|
| 64 |
+
'Acceptable speech': 0,
|
| 65 |
+
'Inappropriate': 1,
|
| 66 |
+
'Background offensive': 2,
|
| 67 |
+
'Other offensive': 3,
|
| 68 |
+
'Background violence': 4,
|
| 69 |
+
'Other violence': 5,
|
| 70 |
+
}
|
| 71 |
+
```
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
The original labels are available if the dataset is loaded with the `multiclass` option:
|
| 75 |
+
|
| 76 |
+
```python
|
| 77 |
+
import datasets
|
| 78 |
+
ds = datasets.load_dataset("classla/FRENK-hate-en","multiclass").
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
In this case the encoding used is:
|
| 82 |
+
```python
|
| 83 |
+
_CLASS_MAP_MULTICLASS = {
|
| 84 |
+
'Acceptable speech': 0,
|
| 85 |
+
'Inappropriate': 1,
|
| 86 |
+
'Background offensive': 2,
|
| 87 |
+
'Other offensive': 3,
|
| 88 |
+
'Background violence': 4,
|
| 89 |
+
'Other violence': 5,
|
| 90 |
+
}
|
| 91 |
+
```
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
## Data structure
|
| 95 |
+
|
| 96 |
+
* `text`: text
|
| 97 |
+
* `target`: who is the target of the hate-speech text ("no target", "commenter", "target" (migrants or LGBT, depending on the topic), or "related to" (again, the topic))
|
| 98 |
+
* `topic`: whether the text relates to lgbt or migrants hate-speech domains
|
| 99 |
+
* `label`: label of the text instance, see above.
|
| 100 |
+
|
| 101 |
+
## Data instance
|
| 102 |
+
|
| 103 |
+
```
|
| 104 |
+
{'text': "Not everyone has the option of a rainbow reaction; I don't but wish I did.",
|
| 105 |
+
'target': 'No target',
|
| 106 |
+
'topic': 'lgbt',
|
| 107 |
+
'label': 0}
|
| 108 |
+
```
|
| 109 |
+
|
| 110 |
+
## Licensing information
|
| 111 |
+
|
| 112 |
+
CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0
|
| 113 |
+
|
| 114 |
+
## Citation information
|
| 115 |
+
|
| 116 |
+
When using this dataset please cite the following paper:
|
| 117 |
+
|
| 118 |
+
```
|
| 119 |
+
@misc{ljubešić2019frenk,
|
| 120 |
+
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
|
| 121 |
+
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
|
| 122 |
+
year={2019},
|
| 123 |
+
eprint={1906.02045},
|
| 124 |
+
archivePrefix={arXiv},
|
| 125 |
+
primaryClass={cs.CL},
|
| 126 |
+
url={https://arxiv.org/abs/1906.02045}
|
| 127 |
+
}
|
| 128 |
+
```
|
| 129 |
+
|
| 130 |
+
The original dataset can be cited as
|
| 131 |
+
```
|
| 132 |
+
@misc{11356/1433,
|
| 133 |
+
title = {Offensive language dataset of Croatian, English and Slovenian comments {FRENK} 1.0},
|
| 134 |
+
author = {Ljube{\v s}i{\'c}, Nikola and Fi{\v s}er, Darja and Erjavec, Toma{\v z}},
|
| 135 |
+
url = {http://hdl.handle.net/11356/1433},
|
| 136 |
+
note = {Slovenian language resource repository {CLARIN}.{SI}},
|
| 137 |
+
copyright = {{CLARIN}.{SI} Licence {ACA} {ID}-{BY}-{NC}-{INF}-{NORED} 1.0},
|
| 138 |
+
year = {2021} }
|
| 139 |
+
```
|
huggingface_dataset/Dataset_Card/huggingartists_drake.md
ADDED
|
@@ -0,0 +1,204 @@
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/drake"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 6.063474 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/631b206379b60df5e1da90e84d35fdbe.1000x1000x1.png')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/drake">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">Drake</div>
|
| 53 |
+
<a href="https://genius.com/artists/drake">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@drake</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/drake).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/drake")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|1298| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/drake")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2022
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/huggingartists_nervy.md
ADDED
|
@@ -0,0 +1,204 @@
|
|
|
|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
|
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|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
tags:
|
| 5 |
+
- huggingartists
|
| 6 |
+
- lyrics
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for "huggingartists/nervy"
|
| 10 |
+
|
| 11 |
+
## Table of Contents
|
| 12 |
+
- [Dataset Description](#dataset-description)
|
| 13 |
+
- [Dataset Summary](#dataset-summary)
|
| 14 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 15 |
+
- [Languages](#languages)
|
| 16 |
+
- [How to use](#how-to-use)
|
| 17 |
+
- [Dataset Structure](#dataset-structure)
|
| 18 |
+
- [Data Fields](#data-fields)
|
| 19 |
+
- [Data Splits](#data-splits)
|
| 20 |
+
- [Dataset Creation](#dataset-creation)
|
| 21 |
+
- [Curation Rationale](#curation-rationale)
|
| 22 |
+
- [Source Data](#source-data)
|
| 23 |
+
- [Annotations](#annotations)
|
| 24 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 25 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 26 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 27 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 28 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 29 |
+
- [Additional Information](#additional-information)
|
| 30 |
+
- [Dataset Curators](#dataset-curators)
|
| 31 |
+
- [Licensing Information](#licensing-information)
|
| 32 |
+
- [Citation Information](#citation-information)
|
| 33 |
+
- [About](#about)
|
| 34 |
+
|
| 35 |
+
## Dataset Description
|
| 36 |
+
|
| 37 |
+
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 38 |
+
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
|
| 39 |
+
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 40 |
+
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 41 |
+
- **Size of the generated dataset:** 0.290463 MB
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
<div class="inline-flex flex-col" style="line-height: 1.5;">
|
| 45 |
+
<div class="flex">
|
| 46 |
+
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/690c7ea858696b779e94dc99b204f034.1000x1000x1.jpg')">
|
| 47 |
+
</div>
|
| 48 |
+
</div>
|
| 49 |
+
<a href="https://huggingface.co/huggingartists/nervy">
|
| 50 |
+
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
|
| 51 |
+
</a>
|
| 52 |
+
<div style="text-align: center; font-size: 16px; font-weight: 800">Нервы (Nervy)</div>
|
| 53 |
+
<a href="https://genius.com/artists/nervy">
|
| 54 |
+
<div style="text-align: center; font-size: 14px;">@nervy</div>
|
| 55 |
+
</a>
|
| 56 |
+
</div>
|
| 57 |
+
|
| 58 |
+
### Dataset Summary
|
| 59 |
+
|
| 60 |
+
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
|
| 61 |
+
Model is available [here](https://huggingface.co/huggingartists/nervy).
|
| 62 |
+
|
| 63 |
+
### Supported Tasks and Leaderboards
|
| 64 |
+
|
| 65 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 66 |
+
|
| 67 |
+
### Languages
|
| 68 |
+
|
| 69 |
+
en
|
| 70 |
+
|
| 71 |
+
## How to use
|
| 72 |
+
|
| 73 |
+
How to load this dataset directly with the datasets library:
|
| 74 |
+
|
| 75 |
+
```python
|
| 76 |
+
from datasets import load_dataset
|
| 77 |
+
|
| 78 |
+
dataset = load_dataset("huggingartists/nervy")
|
| 79 |
+
```
|
| 80 |
+
|
| 81 |
+
## Dataset Structure
|
| 82 |
+
|
| 83 |
+
An example of 'train' looks as follows.
|
| 84 |
+
```
|
| 85 |
+
This example was too long and was cropped:
|
| 86 |
+
|
| 87 |
+
{
|
| 88 |
+
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
|
| 89 |
+
}
|
| 90 |
+
```
|
| 91 |
+
|
| 92 |
+
### Data Fields
|
| 93 |
+
|
| 94 |
+
The data fields are the same among all splits.
|
| 95 |
+
|
| 96 |
+
- `text`: a `string` feature.
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
### Data Splits
|
| 100 |
+
|
| 101 |
+
| train |validation|test|
|
| 102 |
+
|------:|---------:|---:|
|
| 103 |
+
|132| -| -|
|
| 104 |
+
|
| 105 |
+
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
|
| 106 |
+
|
| 107 |
+
```python
|
| 108 |
+
from datasets import load_dataset, Dataset, DatasetDict
|
| 109 |
+
import numpy as np
|
| 110 |
+
|
| 111 |
+
datasets = load_dataset("huggingartists/nervy")
|
| 112 |
+
|
| 113 |
+
train_percentage = 0.9
|
| 114 |
+
validation_percentage = 0.07
|
| 115 |
+
test_percentage = 0.03
|
| 116 |
+
|
| 117 |
+
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
|
| 118 |
+
|
| 119 |
+
datasets = DatasetDict(
|
| 120 |
+
{
|
| 121 |
+
'train': Dataset.from_dict({'text': list(train)}),
|
| 122 |
+
'validation': Dataset.from_dict({'text': list(validation)}),
|
| 123 |
+
'test': Dataset.from_dict({'text': list(test)})
|
| 124 |
+
}
|
| 125 |
+
)
|
| 126 |
+
```
|
| 127 |
+
|
| 128 |
+
## Dataset Creation
|
| 129 |
+
|
| 130 |
+
### Curation Rationale
|
| 131 |
+
|
| 132 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 133 |
+
|
| 134 |
+
### Source Data
|
| 135 |
+
|
| 136 |
+
#### Initial Data Collection and Normalization
|
| 137 |
+
|
| 138 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 139 |
+
|
| 140 |
+
#### Who are the source language producers?
|
| 141 |
+
|
| 142 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 143 |
+
|
| 144 |
+
### Annotations
|
| 145 |
+
|
| 146 |
+
#### Annotation process
|
| 147 |
+
|
| 148 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 149 |
+
|
| 150 |
+
#### Who are the annotators?
|
| 151 |
+
|
| 152 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 153 |
+
|
| 154 |
+
### Personal and Sensitive Information
|
| 155 |
+
|
| 156 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 157 |
+
|
| 158 |
+
## Considerations for Using the Data
|
| 159 |
+
|
| 160 |
+
### Social Impact of Dataset
|
| 161 |
+
|
| 162 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 163 |
+
|
| 164 |
+
### Discussion of Biases
|
| 165 |
+
|
| 166 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 167 |
+
|
| 168 |
+
### Other Known Limitations
|
| 169 |
+
|
| 170 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 171 |
+
|
| 172 |
+
## Additional Information
|
| 173 |
+
|
| 174 |
+
### Dataset Curators
|
| 175 |
+
|
| 176 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 177 |
+
|
| 178 |
+
### Licensing Information
|
| 179 |
+
|
| 180 |
+
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
| 181 |
+
|
| 182 |
+
### Citation Information
|
| 183 |
+
|
| 184 |
+
```
|
| 185 |
+
@InProceedings{huggingartists,
|
| 186 |
+
author={Aleksey Korshuk}
|
| 187 |
+
year=2021
|
| 188 |
+
}
|
| 189 |
+
```
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
## About
|
| 193 |
+
|
| 194 |
+
*Built by Aleksey Korshuk*
|
| 195 |
+
|
| 196 |
+
[](https://github.com/AlekseyKorshuk)
|
| 197 |
+
|
| 198 |
+
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
|
| 199 |
+
|
| 200 |
+
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
|
| 201 |
+
|
| 202 |
+
For more details, visit the project repository.
|
| 203 |
+
|
| 204 |
+
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingface_dataset/Dataset_Card/irds_gov2_trec-tb-2006_named-page.md
ADDED
|
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: '`gov2/trec-tb-2006/named-page`'
|
| 3 |
+
viewer: false
|
| 4 |
+
source_datasets: ['irds/gov2']
|
| 5 |
+
task_categories:
|
| 6 |
+
- text-retrieval
|
| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
# Dataset Card for `gov2/trec-tb-2006/named-page`
|
| 10 |
+
|
| 11 |
+
The `gov2/trec-tb-2006/named-page` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
|
| 12 |
+
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2006/named-page).
|
| 13 |
+
|
| 14 |
+
# Data
|
| 15 |
+
|
| 16 |
+
This dataset provides:
|
| 17 |
+
- `queries` (i.e., topics); count=181
|
| 18 |
+
- `qrels`: (relevance assessments); count=2,361
|
| 19 |
+
|
| 20 |
+
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
|
| 21 |
+
|
| 22 |
+
## Usage
|
| 23 |
+
|
| 24 |
+
```python
|
| 25 |
+
from datasets import load_dataset
|
| 26 |
+
|
| 27 |
+
queries = load_dataset('irds/gov2_trec-tb-2006_named-page', 'queries')
|
| 28 |
+
for record in queries:
|
| 29 |
+
record # {'query_id': ..., 'text': ...}
|
| 30 |
+
|
| 31 |
+
qrels = load_dataset('irds/gov2_trec-tb-2006_named-page', 'qrels')
|
| 32 |
+
for record in qrels:
|
| 33 |
+
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
|
| 34 |
+
|
| 35 |
+
```
|
| 36 |
+
|
| 37 |
+
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
|
| 38 |
+
data in 🤗 Dataset format.
|
| 39 |
+
|
| 40 |
+
## Citation Information
|
| 41 |
+
|
| 42 |
+
```
|
| 43 |
+
@inproceedings{Buttcher2006TrecTerabyte,
|
| 44 |
+
title={The TREC 2006 Terabyte Track},
|
| 45 |
+
author={Stefan B\"uttcher and Charles L. A. Clarke and Ian Soboroff},
|
| 46 |
+
booktitle={TREC},
|
| 47 |
+
year={2006}
|
| 48 |
+
}
|
| 49 |
+
```
|
huggingface_dataset/Dataset_Card/keremberke_blood-cell-object-detection.md
ADDED
|
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
task_categories:
|
| 3 |
+
- object-detection
|
| 4 |
+
tags:
|
| 5 |
+
- roboflow
|
| 6 |
+
- roboflow2huggingface
|
| 7 |
+
- Biology
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
<div align="center">
|
| 11 |
+
<img width="640" alt="keremberke/blood-cell-object-detection" src="https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/thumbnail.jpg">
|
| 12 |
+
</div>
|
| 13 |
+
|
| 14 |
+
### Dataset Labels
|
| 15 |
+
|
| 16 |
+
```
|
| 17 |
+
['platelets', 'rbc', 'wbc']
|
| 18 |
+
```
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
### Number of Images
|
| 22 |
+
|
| 23 |
+
```json
|
| 24 |
+
{'train': 255, 'test': 36, 'valid': 73}
|
| 25 |
+
```
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
### How to Use
|
| 29 |
+
|
| 30 |
+
- Install [datasets](https://pypi.org/project/datasets/):
|
| 31 |
+
|
| 32 |
+
```bash
|
| 33 |
+
pip install datasets
|
| 34 |
+
```
|
| 35 |
+
|
| 36 |
+
- Load the dataset:
|
| 37 |
+
|
| 38 |
+
```python
|
| 39 |
+
from datasets import load_dataset
|
| 40 |
+
|
| 41 |
+
ds = load_dataset("keremberke/blood-cell-object-detection", name="full")
|
| 42 |
+
example = ds['train'][0]
|
| 43 |
+
```
|
| 44 |
+
|
| 45 |
+
### Roboflow Dataset Page
|
| 46 |
+
[https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface)
|
| 47 |
+
|
| 48 |
+
### Citation
|
| 49 |
+
|
| 50 |
+
```
|
| 51 |
+
@misc{ blood-cell-detection-1ekwu_dataset,
|
| 52 |
+
title = { Blood Cell Detection Dataset },
|
| 53 |
+
type = { Open Source Dataset },
|
| 54 |
+
author = { Team Roboflow },
|
| 55 |
+
howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } },
|
| 56 |
+
url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu },
|
| 57 |
+
journal = { Roboflow Universe },
|
| 58 |
+
publisher = { Roboflow },
|
| 59 |
+
year = { 2022 },
|
| 60 |
+
month = { nov },
|
| 61 |
+
note = { visited on 2023-01-18 },
|
| 62 |
+
}
|
| 63 |
+
```
|
| 64 |
+
|
| 65 |
+
### License
|
| 66 |
+
Public Domain
|
| 67 |
+
|
| 68 |
+
### Dataset Summary
|
| 69 |
+
This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT
|
| 70 |
+
|
| 71 |
+
Roboflow is an end-to-end computer vision platform that helps you
|
| 72 |
+
* collaborate with your team on computer vision projects
|
| 73 |
+
* collect & organize images
|
| 74 |
+
* understand unstructured image data
|
| 75 |
+
* annotate, and create datasets
|
| 76 |
+
* export, train, and deploy computer vision models
|
| 77 |
+
* use active learning to improve your dataset over time
|
| 78 |
+
|
| 79 |
+
It includes 364 images.
|
| 80 |
+
Cells are annotated in COCO format.
|
| 81 |
+
|
| 82 |
+
The following pre-processing was applied to each image:
|
| 83 |
+
* Auto-orientation of pixel data (with EXIF-orientation stripping)
|
| 84 |
+
* Resize to 416x416 (Stretch)
|
| 85 |
+
|
| 86 |
+
No image augmentation techniques were applied.
|
| 87 |
+
|
| 88 |
+
|
| 89 |
+
|
huggingface_dataset/Dataset_Card/librispeech_asr.md
ADDED
|
@@ -0,0 +1,305 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
pretty_name: LibriSpeech
|
| 3 |
+
annotations_creators:
|
| 4 |
+
- expert-generated
|
| 5 |
+
language_creators:
|
| 6 |
+
- crowdsourced
|
| 7 |
+
- expert-generated
|
| 8 |
+
language:
|
| 9 |
+
- en
|
| 10 |
+
license:
|
| 11 |
+
- cc-by-4.0
|
| 12 |
+
multilinguality:
|
| 13 |
+
- monolingual
|
| 14 |
+
paperswithcode_id: librispeech-1
|
| 15 |
+
size_categories:
|
| 16 |
+
- 100K<n<1M
|
| 17 |
+
source_datasets:
|
| 18 |
+
- original
|
| 19 |
+
task_categories:
|
| 20 |
+
- automatic-speech-recognition
|
| 21 |
+
- audio-classification
|
| 22 |
+
task_ids:
|
| 23 |
+
- speaker-identification
|
| 24 |
+
dataset_info:
|
| 25 |
+
- config_name: clean
|
| 26 |
+
features:
|
| 27 |
+
- name: file
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: audio
|
| 30 |
+
dtype:
|
| 31 |
+
audio:
|
| 32 |
+
sampling_rate: 16000
|
| 33 |
+
- name: text
|
| 34 |
+
dtype: string
|
| 35 |
+
- name: speaker_id
|
| 36 |
+
dtype: int64
|
| 37 |
+
- name: chapter_id
|
| 38 |
+
dtype: int64
|
| 39 |
+
- name: id
|
| 40 |
+
dtype: string
|
| 41 |
+
splits:
|
| 42 |
+
- name: train.100
|
| 43 |
+
num_bytes: 6619683041
|
| 44 |
+
num_examples: 28539
|
| 45 |
+
- name: train.360
|
| 46 |
+
num_bytes: 23898214592
|
| 47 |
+
num_examples: 104014
|
| 48 |
+
- name: validation
|
| 49 |
+
num_bytes: 359572231
|
| 50 |
+
num_examples: 2703
|
| 51 |
+
- name: test
|
| 52 |
+
num_bytes: 367705423
|
| 53 |
+
num_examples: 2620
|
| 54 |
+
download_size: 30121377654
|
| 55 |
+
dataset_size: 31245175287
|
| 56 |
+
- config_name: other
|
| 57 |
+
features:
|
| 58 |
+
- name: file
|
| 59 |
+
dtype: string
|
| 60 |
+
- name: audio
|
| 61 |
+
dtype:
|
| 62 |
+
audio:
|
| 63 |
+
sampling_rate: 16000
|
| 64 |
+
- name: text
|
| 65 |
+
dtype: string
|
| 66 |
+
- name: speaker_id
|
| 67 |
+
dtype: int64
|
| 68 |
+
- name: chapter_id
|
| 69 |
+
dtype: int64
|
| 70 |
+
- name: id
|
| 71 |
+
dtype: string
|
| 72 |
+
splits:
|
| 73 |
+
- name: train.500
|
| 74 |
+
num_bytes: 31810256902
|
| 75 |
+
num_examples: 148688
|
| 76 |
+
- name: validation
|
| 77 |
+
num_bytes: 337283304
|
| 78 |
+
num_examples: 2864
|
| 79 |
+
- name: test
|
| 80 |
+
num_bytes: 352396474
|
| 81 |
+
num_examples: 2939
|
| 82 |
+
download_size: 31236565377
|
| 83 |
+
dataset_size: 32499936680
|
| 84 |
+
- config_name: all
|
| 85 |
+
features:
|
| 86 |
+
- name: file
|
| 87 |
+
dtype: string
|
| 88 |
+
- name: audio
|
| 89 |
+
dtype:
|
| 90 |
+
audio:
|
| 91 |
+
sampling_rate: 16000
|
| 92 |
+
- name: text
|
| 93 |
+
dtype: string
|
| 94 |
+
- name: speaker_id
|
| 95 |
+
dtype: int64
|
| 96 |
+
- name: chapter_id
|
| 97 |
+
dtype: int64
|
| 98 |
+
- name: id
|
| 99 |
+
dtype: string
|
| 100 |
+
splits:
|
| 101 |
+
- name: train.clean.100
|
| 102 |
+
num_bytes: 6627791685
|
| 103 |
+
num_examples: 28539
|
| 104 |
+
- name: train.clean.360
|
| 105 |
+
num_bytes: 23927767570
|
| 106 |
+
num_examples: 104014
|
| 107 |
+
- name: train.other.500
|
| 108 |
+
num_bytes: 31852502880
|
| 109 |
+
num_examples: 148688
|
| 110 |
+
- name: validation.clean
|
| 111 |
+
num_bytes: 359505691
|
| 112 |
+
num_examples: 2703
|
| 113 |
+
- name: validation.other
|
| 114 |
+
num_bytes: 337213112
|
| 115 |
+
num_examples: 2864
|
| 116 |
+
- name: test.clean
|
| 117 |
+
num_bytes: 368449831
|
| 118 |
+
num_examples: 2620
|
| 119 |
+
- name: test.other
|
| 120 |
+
num_bytes: 353231518
|
| 121 |
+
num_examples: 2939
|
| 122 |
+
download_size: 61357943031
|
| 123 |
+
dataset_size: 63826462287
|
| 124 |
+
---
|
| 125 |
+
|
| 126 |
+
# Dataset Card for librispeech_asr
|
| 127 |
+
|
| 128 |
+
## Table of Contents
|
| 129 |
+
- [Dataset Description](#dataset-description)
|
| 130 |
+
- [Dataset Summary](#dataset-summary)
|
| 131 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 132 |
+
- [Languages](#languages)
|
| 133 |
+
- [Dataset Structure](#dataset-structure)
|
| 134 |
+
- [Data Instances](#data-instances)
|
| 135 |
+
- [Data Fields](#data-fields)
|
| 136 |
+
- [Data Splits](#data-splits)
|
| 137 |
+
- [Dataset Creation](#dataset-creation)
|
| 138 |
+
- [Curation Rationale](#curation-rationale)
|
| 139 |
+
- [Source Data](#source-data)
|
| 140 |
+
- [Annotations](#annotations)
|
| 141 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 142 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 143 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 144 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 145 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 146 |
+
- [Additional Information](#additional-information)
|
| 147 |
+
- [Dataset Curators](#dataset-curators)
|
| 148 |
+
- [Licensing Information](#licensing-information)
|
| 149 |
+
- [Citation Information](#citation-information)
|
| 150 |
+
- [Contributions](#contributions)
|
| 151 |
+
|
| 152 |
+
## Dataset Description
|
| 153 |
+
|
| 154 |
+
- **Homepage:** [LibriSpeech ASR corpus](http://www.openslr.org/12)
|
| 155 |
+
- **Repository:** [Needs More Information]
|
| 156 |
+
- **Paper:** [LibriSpeech: An ASR Corpus Based On Public Domain Audio Books](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf)
|
| 157 |
+
- **Leaderboard:** [The 🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
|
| 158 |
+
- **Point of Contact:** [Daniel Povey](mailto:dpovey@gmail.com)
|
| 159 |
+
|
| 160 |
+
### Dataset Summary
|
| 161 |
+
|
| 162 |
+
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
|
| 163 |
+
|
| 164 |
+
### Supported Tasks and Leaderboards
|
| 165 |
+
|
| 166 |
+
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active Hugging Face leaderboard which can be found at https://huggingface.co/spaces/huggingface/hf-speech-bench. The leaderboard ranks models uploaded to the Hub based on their WER. An external leaderboard at https://paperswithcode.com/sota/speech-recognition-on-librispeech-test-clean ranks the latest models from research and academia.
|
| 167 |
+
|
| 168 |
+
### Languages
|
| 169 |
+
|
| 170 |
+
The audio is in English. There are two configurations: `clean` and `other`.
|
| 171 |
+
The speakers in the corpus were ranked according to the WER of the transcripts of a model trained on
|
| 172 |
+
a different dataset, and were divided roughly in the middle,
|
| 173 |
+
with the lower-WER speakers designated as "clean" and the higher WER speakers designated as "other".
|
| 174 |
+
|
| 175 |
+
## Dataset Structure
|
| 176 |
+
|
| 177 |
+
### Data Instances
|
| 178 |
+
|
| 179 |
+
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
|
| 180 |
+
|
| 181 |
+
```
|
| 182 |
+
{'chapter_id': 141231,
|
| 183 |
+
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
|
| 184 |
+
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
|
| 185 |
+
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
|
| 186 |
+
0.00091553, 0.00085449], dtype=float32),
|
| 187 |
+
'sampling_rate': 16000},
|
| 188 |
+
'id': '1272-141231-0000',
|
| 189 |
+
'speaker_id': 1272,
|
| 190 |
+
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
|
| 191 |
+
```
|
| 192 |
+
|
| 193 |
+
|
| 194 |
+
### Data Fields
|
| 195 |
+
|
| 196 |
+
- file: A path to the downloaded audio file in .flac format.
|
| 197 |
+
|
| 198 |
+
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
|
| 199 |
+
|
| 200 |
+
- text: the transcription of the audio file.
|
| 201 |
+
|
| 202 |
+
- id: unique id of the data sample.
|
| 203 |
+
|
| 204 |
+
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
|
| 205 |
+
|
| 206 |
+
- chapter_id: id of the audiobook chapter which includes the transcription.
|
| 207 |
+
|
| 208 |
+
### Data Splits
|
| 209 |
+
|
| 210 |
+
The size of the corpus makes it impractical, or at least inconvenient
|
| 211 |
+
for some users, to distribute it as a single large archive. Thus the
|
| 212 |
+
training portion of the corpus is split into three subsets, with approximate size 100, 360 and 500 hours respectively.
|
| 213 |
+
A simple automatic
|
| 214 |
+
procedure was used to select the audio in the first two sets to be, on
|
| 215 |
+
average, of higher recording quality and with accents closer to US
|
| 216 |
+
English. An acoustic model was trained on WSJ’s si-84 data subset
|
| 217 |
+
and was used to recognize the audio in the corpus, using a bigram
|
| 218 |
+
LM estimated on the text of the respective books. We computed the
|
| 219 |
+
Word Error Rate (WER) of this automatic transcript relative to our
|
| 220 |
+
reference transcripts obtained from the book texts.
|
| 221 |
+
The speakers in the corpus were ranked according to the WER of
|
| 222 |
+
the WSJ model’s transcripts, and were divided roughly in the middle,
|
| 223 |
+
with the lower-WER speakers designated as "clean" and the higher-WER speakers designated as "other".
|
| 224 |
+
|
| 225 |
+
For "clean", the data is split into train, validation, and test set. The train set is further split into train.100 and train.360
|
| 226 |
+
respectively accounting for 100h and 360h of the training data.
|
| 227 |
+
For "other", the data is split into train, validation, and test set. The train set contains approximately 500h of recorded speech.
|
| 228 |
+
|
| 229 |
+
| | Train.500 | Train.360 | Train.100 | Valid | Test |
|
| 230 |
+
| ----- | ------ | ----- | ---- | ---- | ---- |
|
| 231 |
+
| clean | - | 104014 | 28539 | 2703 | 2620|
|
| 232 |
+
| other | 148688 | - | - | 2864 | 2939 |
|
| 233 |
+
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
## Dataset Creation
|
| 237 |
+
|
| 238 |
+
### Curation Rationale
|
| 239 |
+
|
| 240 |
+
[Needs More Information]
|
| 241 |
+
|
| 242 |
+
### Source Data
|
| 243 |
+
|
| 244 |
+
#### Initial Data Collection and Normalization
|
| 245 |
+
|
| 246 |
+
[Needs More Information]
|
| 247 |
+
|
| 248 |
+
#### Who are the source language producers?
|
| 249 |
+
|
| 250 |
+
[Needs More Information]
|
| 251 |
+
|
| 252 |
+
### Annotations
|
| 253 |
+
|
| 254 |
+
#### Annotation process
|
| 255 |
+
|
| 256 |
+
[Needs More Information]
|
| 257 |
+
|
| 258 |
+
#### Who are the annotators?
|
| 259 |
+
|
| 260 |
+
[Needs More Information]
|
| 261 |
+
|
| 262 |
+
### Personal and Sensitive Information
|
| 263 |
+
|
| 264 |
+
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
|
| 265 |
+
|
| 266 |
+
## Considerations for Using the Data
|
| 267 |
+
|
| 268 |
+
### Social Impact of Dataset
|
| 269 |
+
|
| 270 |
+
[More Information Needed]
|
| 271 |
+
|
| 272 |
+
### Discussion of Biases
|
| 273 |
+
|
| 274 |
+
[More Information Needed]
|
| 275 |
+
|
| 276 |
+
### Other Known Limitations
|
| 277 |
+
|
| 278 |
+
[Needs More Information]
|
| 279 |
+
|
| 280 |
+
## Additional Information
|
| 281 |
+
|
| 282 |
+
### Dataset Curators
|
| 283 |
+
|
| 284 |
+
The dataset was initially created by Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.
|
| 285 |
+
|
| 286 |
+
### Licensing Information
|
| 287 |
+
|
| 288 |
+
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
|
| 289 |
+
|
| 290 |
+
### Citation Information
|
| 291 |
+
|
| 292 |
+
```
|
| 293 |
+
@inproceedings{panayotov2015librispeech,
|
| 294 |
+
title={Librispeech: an ASR corpus based on public domain audio books},
|
| 295 |
+
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
|
| 296 |
+
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
|
| 297 |
+
pages={5206--5210},
|
| 298 |
+
year={2015},
|
| 299 |
+
organization={IEEE}
|
| 300 |
+
}
|
| 301 |
+
```
|
| 302 |
+
|
| 303 |
+
### Contributions
|
| 304 |
+
|
| 305 |
+
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset.
|
huggingface_dataset/Dataset_Card/nlpso_m2m3_qualitative_analysis_ref_ptrn_cmbert_io.md
ADDED
|
@@ -0,0 +1,51 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
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|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- fr
|
| 4 |
+
multilinguality:
|
| 5 |
+
- monolingual
|
| 6 |
+
task_categories:
|
| 7 |
+
- token-classification
|
| 8 |
+
---
|
| 9 |
+
|
| 10 |
+
# m2m3_qualitative_analysis_ref_ptrn_cmbert_io
|
| 11 |
+
|
| 12 |
+
## Introduction
|
| 13 |
+
|
| 14 |
+
This dataset was used to perform **qualitative analysis** of [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained) on **nested NER task** using Independant NER layers approach [M1].
|
| 15 |
+
It contains Paris trade directories entries from the 19th century.
|
| 16 |
+
|
| 17 |
+
## Dataset parameters
|
| 18 |
+
|
| 19 |
+
* Approachrd : M2 and M3
|
| 20 |
+
* Dataset type : ground-truth
|
| 21 |
+
* Tokenizer : [HueyNemud/das22-10-camembert_pretrained](https://huggingface.co/HueyNemud/das22-10-camembert_pretrained)
|
| 22 |
+
* Tagging format : IO
|
| 23 |
+
* Counts :
|
| 24 |
+
* Train : 6084
|
| 25 |
+
* Dev : 676
|
| 26 |
+
* Test : 1685
|
| 27 |
+
* Associated fine-tuned models :
|
| 28 |
+
* M2 : [nlpso/m2_joint_label_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_ptrn_cmbert_io)
|
| 29 |
+
* M3 : [nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_ptrn_cmbert_io)
|
| 30 |
+
|
| 31 |
+
## Entity types
|
| 32 |
+
|
| 33 |
+
Abbreviation|Entity group (level)|Description
|
| 34 |
+
-|-|-
|
| 35 |
+
O |1 & 2|Outside of a named entity
|
| 36 |
+
PER |1|Person or company name
|
| 37 |
+
ACT |1 & 2|Person or company professional activity
|
| 38 |
+
TITREH |2|Military or civil distinction
|
| 39 |
+
DESC |1|Entry full description
|
| 40 |
+
TITREP |2|Professionnal reward
|
| 41 |
+
SPAT |1|Address
|
| 42 |
+
LOC |2|Street name
|
| 43 |
+
CARDINAL |2|Street number
|
| 44 |
+
FT |2|Geographical feature
|
| 45 |
+
|
| 46 |
+
## How to use this dataset
|
| 47 |
+
|
| 48 |
+
```python
|
| 49 |
+
from datasets import load_dataset
|
| 50 |
+
|
| 51 |
+
train_dev_test = load_dataset("nlpso/m2m3_qualitative_analysis_ref_ptrn_cmbert_io")
|
huggingface_dataset/Dataset_Card/poolrf2001_FaceMask.md
ADDED
|
@@ -0,0 +1,198 @@
|
|
|
|
|
|
|
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|
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|
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|
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|
|
|
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|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- expert-generated
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
pretty_name: FaceMask
|
| 13 |
+
size_categories:
|
| 14 |
+
- 1K<n<10K
|
| 15 |
+
source_datasets:
|
| 16 |
+
- original
|
| 17 |
+
task_categories:
|
| 18 |
+
- image-classification
|
| 19 |
+
task_ids:
|
| 20 |
+
- multi-class-image-classification
|
| 21 |
+
dataset_info:
|
| 22 |
+
features:
|
| 23 |
+
|
| 24 |
+
- name: image
|
| 25 |
+
dtype: image
|
| 26 |
+
- name: labels
|
| 27 |
+
dtype:
|
| 28 |
+
class_label:
|
| 29 |
+
names:
|
| 30 |
+
0: mask_weared_incorrect
|
| 31 |
+
1: with_mask
|
| 32 |
+
2: without_mask
|
| 33 |
+
splits:
|
| 34 |
+
- name: train
|
| 35 |
+
num_bytes: 38806014
|
| 36 |
+
num_examples: 1500
|
| 37 |
+
- name: validation
|
| 38 |
+
num_bytes: 4758962
|
| 39 |
+
num_examples: 180
|
| 40 |
+
- name: test
|
| 41 |
+
num_bytes: 4693735
|
| 42 |
+
num_examples: 180
|
| 43 |
+
download_size: 48258711
|
| 44 |
+
dataset_size: 49140913
|
| 45 |
+
---
|
| 46 |
+
|
| 47 |
+
# Dataset Card for Beans
|
| 48 |
+
|
| 49 |
+
## Table of Contents
|
| 50 |
+
- [Table of Contents](#table-of-contents)
|
| 51 |
+
- [Dataset Description](#dataset-description)
|
| 52 |
+
- [Dataset Summary](#dataset-summary)
|
| 53 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 54 |
+
- [Languages](#languages)
|
| 55 |
+
- [Dataset Structure](#dataset-structure)
|
| 56 |
+
- [Data Instances](#data-instances)
|
| 57 |
+
- [Data Fields](#data-fields)
|
| 58 |
+
- [Data Splits](#data-splits)
|
| 59 |
+
- [Dataset Creation](#dataset-creation)
|
| 60 |
+
- [Curation Rationale](#curation-rationale)
|
| 61 |
+
- [Source Data](#source-data)
|
| 62 |
+
- [Annotations](#annotations)
|
| 63 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 64 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 65 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 66 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 67 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 68 |
+
- [Additional Information](#additional-information)
|
| 69 |
+
- [Dataset Curators](#dataset-curators)
|
| 70 |
+
- [Licensing Information](#licensing-information)
|
| 71 |
+
- [Citation Information](#citation-information)
|
| 72 |
+
- [Contributions](#contributions)
|
| 73 |
+
|
| 74 |
+
## Dataset Description
|
| 75 |
+
|
| 76 |
+
- **Homepage:** (https://huggingface.co/datasets/poolrf2001/FaceMask)
|
| 77 |
+
- **Repository:** (https://huggingface.co/datasets/poolrf2001/FaceMask)
|
| 78 |
+
- **Paper:** N/A
|
| 79 |
+
- **Leaderboard:** N/A
|
| 80 |
+
- **Point of Contact:** N/A
|
| 81 |
+
|
| 82 |
+
### Dataset Summary
|
| 83 |
+
|
| 84 |
+
Beans leaf dataset with images of diseased and health leaves.
|
| 85 |
+
|
| 86 |
+
### Supported Tasks and Leaderboards
|
| 87 |
+
|
| 88 |
+
- `image-classification`: Based on a leaf image, the goal of this task is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.
|
| 89 |
+
|
| 90 |
+
### Languages
|
| 91 |
+
|
| 92 |
+
English
|
| 93 |
+
|
| 94 |
+
## Dataset Structure
|
| 95 |
+
|
| 96 |
+
### Data Instances
|
| 97 |
+
|
| 98 |
+
A sample from the training set is provided below:
|
| 99 |
+
|
| 100 |
+
```
|
| 101 |
+
{
|
| 102 |
+
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=128x128 at 0x16BAA72A4A8>,
|
| 103 |
+
'labels': 1
|
| 104 |
+
}
|
| 105 |
+
```
|
| 106 |
+
|
| 107 |
+
### Data Fields
|
| 108 |
+
|
| 109 |
+
The data instances have the following fields:
|
| 110 |
+
|
| 111 |
+
|
| 112 |
+
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
|
| 113 |
+
- `labels`: an `int` classification label.
|
| 114 |
+
|
| 115 |
+
Class Label Mappings:
|
| 116 |
+
|
| 117 |
+
```json
|
| 118 |
+
{
|
| 119 |
+
"mask_weared_incorrect": 0,
|
| 120 |
+
"with_mask": 1,
|
| 121 |
+
"without_mask": 2,
|
| 122 |
+
}
|
| 123 |
+
```
|
| 124 |
+
|
| 125 |
+
### Data Splits
|
| 126 |
+
|
| 127 |
+
|
| 128 |
+
| |train|validation|test|
|
| 129 |
+
|-------------|----:|---------:|---:|
|
| 130 |
+
|# of examples|1500 |180 |180 |
|
| 131 |
+
|
| 132 |
+
## Dataset Creation
|
| 133 |
+
|
| 134 |
+
### Curation Rationale
|
| 135 |
+
|
| 136 |
+
[More Information Needed]
|
| 137 |
+
|
| 138 |
+
### Source Data
|
| 139 |
+
|
| 140 |
+
#### Initial Data Collection and Normalization
|
| 141 |
+
|
| 142 |
+
[More Information Needed]
|
| 143 |
+
|
| 144 |
+
#### Who are the source language producers?
|
| 145 |
+
|
| 146 |
+
[More Information Needed]
|
| 147 |
+
|
| 148 |
+
### Annotations
|
| 149 |
+
|
| 150 |
+
#### Annotation process
|
| 151 |
+
|
| 152 |
+
[More Information Needed]
|
| 153 |
+
|
| 154 |
+
#### Who are the annotators?
|
| 155 |
+
|
| 156 |
+
[More Information Needed]
|
| 157 |
+
|
| 158 |
+
### Personal and Sensitive Information
|
| 159 |
+
|
| 160 |
+
[More Information Needed]
|
| 161 |
+
|
| 162 |
+
## Considerations for Using the Data
|
| 163 |
+
|
| 164 |
+
### Social Impact of Dataset
|
| 165 |
+
|
| 166 |
+
[More Information Needed]
|
| 167 |
+
|
| 168 |
+
### Discussion of Biases
|
| 169 |
+
|
| 170 |
+
[More Information Needed]
|
| 171 |
+
|
| 172 |
+
### Other Known Limitations
|
| 173 |
+
|
| 174 |
+
[More Information Needed]
|
| 175 |
+
|
| 176 |
+
## Additional Information
|
| 177 |
+
|
| 178 |
+
### Dataset Curators
|
| 179 |
+
|
| 180 |
+
[More Information Needed]
|
| 181 |
+
|
| 182 |
+
### Licensing Information
|
| 183 |
+
|
| 184 |
+
[More Information Needed]
|
| 185 |
+
|
| 186 |
+
### Citation Information
|
| 187 |
+
|
| 188 |
+
```
|
| 189 |
+
@ONLINE {beansdata,
|
| 190 |
+
author="Pool",
|
| 191 |
+
title="FaceMask dataset",
|
| 192 |
+
month="January",
|
| 193 |
+
year="2023",
|
| 194 |
+
url="https://github.com/poolrf2001/maskFace"
|
| 195 |
+
}
|
| 196 |
+
```
|
| 197 |
+
|
| 198 |
+
### Contributions
|
huggingface_dataset/Dataset_Card/tne.md
ADDED
|
@@ -0,0 +1,291 @@
|
|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
|
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|
|
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|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
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|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- crowdsourced
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- mit
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 1K<n<10K
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- text-retrieval
|
| 18 |
+
task_ids:
|
| 19 |
+
- document-retrieval
|
| 20 |
+
pretty_name: Text-based NP Enrichment
|
| 21 |
+
dataset_info:
|
| 22 |
+
features:
|
| 23 |
+
- name: id
|
| 24 |
+
dtype: string
|
| 25 |
+
- name: text
|
| 26 |
+
dtype: string
|
| 27 |
+
- name: tokens
|
| 28 |
+
sequence: string
|
| 29 |
+
- name: nps
|
| 30 |
+
list:
|
| 31 |
+
- name: text
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: first_char
|
| 34 |
+
dtype: int32
|
| 35 |
+
- name: last_char
|
| 36 |
+
dtype: int32
|
| 37 |
+
- name: first_token
|
| 38 |
+
dtype: int32
|
| 39 |
+
- name: last_token
|
| 40 |
+
dtype: int32
|
| 41 |
+
- name: id
|
| 42 |
+
dtype: string
|
| 43 |
+
- name: np_relations
|
| 44 |
+
list:
|
| 45 |
+
- name: anchor
|
| 46 |
+
dtype: string
|
| 47 |
+
- name: complement
|
| 48 |
+
dtype: string
|
| 49 |
+
- name: preposition
|
| 50 |
+
dtype:
|
| 51 |
+
class_label:
|
| 52 |
+
names:
|
| 53 |
+
'0': about
|
| 54 |
+
'1': for
|
| 55 |
+
'2': with
|
| 56 |
+
'3': from
|
| 57 |
+
'4': among
|
| 58 |
+
'5': by
|
| 59 |
+
'6': 'on'
|
| 60 |
+
'7': at
|
| 61 |
+
'8': during
|
| 62 |
+
'9': of
|
| 63 |
+
'10': member(s) of
|
| 64 |
+
'11': in
|
| 65 |
+
'12': after
|
| 66 |
+
'13': under
|
| 67 |
+
'14': to
|
| 68 |
+
'15': into
|
| 69 |
+
'16': before
|
| 70 |
+
'17': near
|
| 71 |
+
'18': outside
|
| 72 |
+
'19': around
|
| 73 |
+
'20': between
|
| 74 |
+
'21': against
|
| 75 |
+
'22': over
|
| 76 |
+
'23': inside
|
| 77 |
+
- name: complement_coref_cluster_id
|
| 78 |
+
dtype: string
|
| 79 |
+
- name: coref
|
| 80 |
+
list:
|
| 81 |
+
- name: id
|
| 82 |
+
dtype: string
|
| 83 |
+
- name: members
|
| 84 |
+
sequence: string
|
| 85 |
+
- name: np_type
|
| 86 |
+
dtype:
|
| 87 |
+
class_label:
|
| 88 |
+
names:
|
| 89 |
+
'0': standard
|
| 90 |
+
'1': time/date/measurement
|
| 91 |
+
'2': idiomatic
|
| 92 |
+
- name: metadata
|
| 93 |
+
struct:
|
| 94 |
+
- name: annotators
|
| 95 |
+
struct:
|
| 96 |
+
- name: coref_worker
|
| 97 |
+
dtype: int32
|
| 98 |
+
- name: consolidator_worker
|
| 99 |
+
dtype: int32
|
| 100 |
+
- name: np-relations_worker
|
| 101 |
+
sequence: int32
|
| 102 |
+
- name: url
|
| 103 |
+
dtype: string
|
| 104 |
+
- name: source
|
| 105 |
+
dtype: string
|
| 106 |
+
splits:
|
| 107 |
+
- name: train
|
| 108 |
+
num_bytes: 41308170
|
| 109 |
+
num_examples: 3988
|
| 110 |
+
- name: validation
|
| 111 |
+
num_bytes: 5495419
|
| 112 |
+
num_examples: 500
|
| 113 |
+
- name: test
|
| 114 |
+
num_bytes: 2203716
|
| 115 |
+
num_examples: 500
|
| 116 |
+
- name: test_ood
|
| 117 |
+
num_bytes: 2249352
|
| 118 |
+
num_examples: 509
|
| 119 |
+
download_size: 14194578
|
| 120 |
+
dataset_size: 51256657
|
| 121 |
+
---
|
| 122 |
+
|
| 123 |
+
# Dataset Card for Text-based NP Enrichment
|
| 124 |
+
|
| 125 |
+
## Table of Contents
|
| 126 |
+
- [Dataset Description](#dataset-description)
|
| 127 |
+
- [Dataset Summary](#dataset-summary)
|
| 128 |
+
- [Supported Tasks](#supported-tasks-and-leaderboards)
|
| 129 |
+
- [Languages](#languages)
|
| 130 |
+
- [Dataset Structure](#dataset-structure)
|
| 131 |
+
- [Data Instances](#data-instances)
|
| 132 |
+
- [Data Fields](#data-instances)
|
| 133 |
+
- [Data Splits](#data-instances)
|
| 134 |
+
- [Dataset Creation](#dataset-creation)
|
| 135 |
+
- [Curation Rationale](#curation-rationale)
|
| 136 |
+
- [Source Data](#source-data)
|
| 137 |
+
- [Annotations](#annotations)
|
| 138 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 139 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 140 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 141 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 142 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 143 |
+
- [Additional Information](#additional-information)
|
| 144 |
+
- [Dataset Curators](#dataset-curators)
|
| 145 |
+
- [Licensing Information](#licensing-information)
|
| 146 |
+
- [Citation Information](#citation-information)
|
| 147 |
+
|
| 148 |
+
## Dataset Description
|
| 149 |
+
|
| 150 |
+
- **Homepage:** https://yanaiela.github.io/TNE/
|
| 151 |
+
- **Repository:** https://github.com/yanaiela/TNE
|
| 152 |
+
- **Paper:** https://arxiv.org/abs/2109.12085
|
| 153 |
+
- **Leaderboard:** [TNE OOD](https://leaderboard.allenai.org/tne-ood/submissions/public)
|
| 154 |
+
[TNE](https://leaderboard.allenai.org/tne/submissions/public)
|
| 155 |
+
- **Point of Contact:** [Yanai Elazar](mailto:yanaiela@gmail.com)
|
| 156 |
+
|
| 157 |
+
### Dataset Summary
|
| 158 |
+
|
| 159 |
+
Text-based NP Enrichment (TNE) is a natural language understanding (NLU) task, which focus on relations between noun phrases (NPs) that can be mediated via prepositions. The dataset contains 5,497 documents, annotated exhaustively with all possible links between the NPs in each document.
|
| 160 |
+
|
| 161 |
+
The main data comes from WikiNews, which is used for train/dev/test. We also collected an additional set of 509 documents to serve as out of distribution (OOD) data points, from the Book Corpus, IMDB reviews and Reddit.
|
| 162 |
+
|
| 163 |
+
### Supported Tasks and Leaderboards
|
| 164 |
+
|
| 165 |
+
The data contain both the main data for the TNE task, as well as coreference resolution data.
|
| 166 |
+
There are two leaderboards for the TNE data, one for the standard test set, and another one for the OOD test set:
|
| 167 |
+
- [TNE Leaderboard](https://leaderboard.allenai.org/tne/submissions/public)
|
| 168 |
+
- [TNE OOD Leaderboard](https://leaderboard.allenai.org/tne-ood/submissions/public)
|
| 169 |
+
|
| 170 |
+
### Languages
|
| 171 |
+
|
| 172 |
+
The text in the dataset is in English, as spoken in the different domains we include. The associated BCP-47 code is `en`.
|
| 173 |
+
|
| 174 |
+
## Dataset Structure
|
| 175 |
+
|
| 176 |
+
### Data Instances
|
| 177 |
+
|
| 178 |
+
The original files are in a jsonl format, containing a dictionary of a single document, in each line.
|
| 179 |
+
Each document contain a different amount of labels, due to the different amount of NPs.
|
| 180 |
+
The test and ood splits come without the annotated labels.
|
| 181 |
+
|
| 182 |
+
### Data Fields
|
| 183 |
+
|
| 184 |
+
A document consists of:
|
| 185 |
+
|
| 186 |
+
* `id`: a unique identifier of a document, beginning with `r` and followed by a number
|
| 187 |
+
* `text`: the text of the document. The title and subtitles (if exists) are separated with two new lines. The paragraphs
|
| 188 |
+
are separated by a single new line.
|
| 189 |
+
* `tokens`: a list of string, containing the tokenized tokens
|
| 190 |
+
* `nps`: a list of dictionaries, containing the following entries:
|
| 191 |
+
* `text`: the text of the np
|
| 192 |
+
* `start_index`: an integer indicating the starting index in the text
|
| 193 |
+
* `end_index`: an integer indicating the ending index in the text
|
| 194 |
+
* `start_token`: an integer indicating the first token of the np out of the tokenized tokens
|
| 195 |
+
* `end_token`: an integer indicating the last token of the np out of the tokenized tokens
|
| 196 |
+
* `id`: the id of the np
|
| 197 |
+
* `np_relations`: these are the relation labels of the document. It is a list of dictionaries, where each
|
| 198 |
+
dictionary contains:
|
| 199 |
+
* `anchor`: the id of the anchor np
|
| 200 |
+
* `complement`: the id of the complement np
|
| 201 |
+
* `preposition`: the preposition that links between the anchor and the complement. This can take one out of 24 pre-defined preposition (23 + member(s)-of)
|
| 202 |
+
* `complement_coref_cluster_id`: the coreference id, which the complement is part of.
|
| 203 |
+
* `coref`: the coreference labels. It contains a list of dictionaries, where each dictionary contains:
|
| 204 |
+
* `id`: the id of the coreference cluster
|
| 205 |
+
* `members`: the ids of the nps members of such cluster
|
| 206 |
+
* `np_type`: the type of cluster. It can be either
|
| 207 |
+
* `standard`: regular coreference cluster
|
| 208 |
+
* `time/date/measurement`: a time / date / measurement np. These will be singletons.
|
| 209 |
+
* `idiomatic`: an idiomatic expression
|
| 210 |
+
* `metadata`: metadata of the document. It contains the following:
|
| 211 |
+
* `annotators`: a dictionary with anonymized annotators id
|
| 212 |
+
* `coref_worker`: the coreference worker id
|
| 213 |
+
* `consolidator_worker`: the consolidator worker id
|
| 214 |
+
* `np-relations_worker`: the np relations worker id
|
| 215 |
+
* `url`: the url where the document was taken from (not always existing)
|
| 216 |
+
* `source`: the original file name where the document was taken from
|
| 217 |
+
|
| 218 |
+
|
| 219 |
+
### Data Splits
|
| 220 |
+
|
| 221 |
+
The dataset is spread across four files, for the four different splits: train, dev, test and test_ood.
|
| 222 |
+
Additional details on the data statistics can be found in the [paper](https://arxiv.org/abs/2109.12085)
|
| 223 |
+
|
| 224 |
+
## Dataset Creation
|
| 225 |
+
|
| 226 |
+
### Curation Rationale
|
| 227 |
+
|
| 228 |
+
TNE was build as a new task for language understanding, focusing on extracting relations between nouns, moderated by prepositions.
|
| 229 |
+
|
| 230 |
+
### Source Data
|
| 231 |
+
|
| 232 |
+
#### Initial Data Collection and Normalization
|
| 233 |
+
|
| 234 |
+
[Needs More Information]
|
| 235 |
+
|
| 236 |
+
#### Who are the source language producers?
|
| 237 |
+
|
| 238 |
+
[Needs More Information]
|
| 239 |
+
|
| 240 |
+
### Annotations
|
| 241 |
+
|
| 242 |
+
#### Annotation process
|
| 243 |
+
|
| 244 |
+
[Needs More Information]
|
| 245 |
+
|
| 246 |
+
#### Who are the annotators?
|
| 247 |
+
|
| 248 |
+
[Needs More Information]
|
| 249 |
+
|
| 250 |
+
### Personal and Sensitive Information
|
| 251 |
+
|
| 252 |
+
[Needs More Information]
|
| 253 |
+
|
| 254 |
+
## Considerations for Using the Data
|
| 255 |
+
|
| 256 |
+
### Social Impact of Dataset
|
| 257 |
+
|
| 258 |
+
[Needs More Information]
|
| 259 |
+
|
| 260 |
+
### Discussion of Biases
|
| 261 |
+
|
| 262 |
+
[Needs More Information]
|
| 263 |
+
|
| 264 |
+
### Other Known Limitations
|
| 265 |
+
|
| 266 |
+
[Needs More Information]
|
| 267 |
+
|
| 268 |
+
## Additional Information
|
| 269 |
+
|
| 270 |
+
### Dataset Curators
|
| 271 |
+
|
| 272 |
+
The dataset was created by Yanai Elazar, Victoria Basmov, Yoav Goldberg, Reut Tsarfaty, during work done at Bar-Ilan University, and AI2.
|
| 273 |
+
|
| 274 |
+
### Licensing Information
|
| 275 |
+
|
| 276 |
+
The data is released under the MIT license.
|
| 277 |
+
|
| 278 |
+
### Citation Information
|
| 279 |
+
|
| 280 |
+
```bibtex
|
| 281 |
+
@article{tne,
|
| 282 |
+
author = {Elazar, Yanai and Basmov, Victoria and Goldberg, Yoav and Tsarfaty, Reut},
|
| 283 |
+
title = "{Text-based NP Enrichment}",
|
| 284 |
+
journal = {Transactions of the Association for Computational Linguistics},
|
| 285 |
+
year = {2022},
|
| 286 |
+
}
|
| 287 |
+
```
|
| 288 |
+
|
| 289 |
+
### Contributions
|
| 290 |
+
|
| 291 |
+
Thanks to [@yanaiela](https://github.com/yanaiela), who is also the first author of the paper, for adding this dataset.
|
huggingface_dataset/Dataset_Card/totto.md
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|
| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- expert-generated
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- en
|
| 8 |
+
license:
|
| 9 |
+
- cc-by-sa-3.0
|
| 10 |
+
multilinguality:
|
| 11 |
+
- monolingual
|
| 12 |
+
size_categories:
|
| 13 |
+
- 100K<n<1M
|
| 14 |
+
source_datasets:
|
| 15 |
+
- original
|
| 16 |
+
task_categories:
|
| 17 |
+
- table-to-text
|
| 18 |
+
task_ids: []
|
| 19 |
+
paperswithcode_id: totto
|
| 20 |
+
pretty_name: ToTTo
|
| 21 |
+
dataset_info:
|
| 22 |
+
features:
|
| 23 |
+
- name: id
|
| 24 |
+
dtype: int32
|
| 25 |
+
- name: table_page_title
|
| 26 |
+
dtype: string
|
| 27 |
+
- name: table_webpage_url
|
| 28 |
+
dtype: string
|
| 29 |
+
- name: table_section_title
|
| 30 |
+
dtype: string
|
| 31 |
+
- name: table_section_text
|
| 32 |
+
dtype: string
|
| 33 |
+
- name: table
|
| 34 |
+
list:
|
| 35 |
+
list:
|
| 36 |
+
- name: column_span
|
| 37 |
+
dtype: int32
|
| 38 |
+
- name: is_header
|
| 39 |
+
dtype: bool
|
| 40 |
+
- name: row_span
|
| 41 |
+
dtype: int32
|
| 42 |
+
- name: value
|
| 43 |
+
dtype: string
|
| 44 |
+
- name: highlighted_cells
|
| 45 |
+
sequence:
|
| 46 |
+
sequence: int32
|
| 47 |
+
- name: example_id
|
| 48 |
+
dtype: string
|
| 49 |
+
- name: sentence_annotations
|
| 50 |
+
sequence:
|
| 51 |
+
- name: original_sentence
|
| 52 |
+
dtype: string
|
| 53 |
+
- name: sentence_after_deletion
|
| 54 |
+
dtype: string
|
| 55 |
+
- name: sentence_after_ambiguity
|
| 56 |
+
dtype: string
|
| 57 |
+
- name: final_sentence
|
| 58 |
+
dtype: string
|
| 59 |
+
- name: overlap_subset
|
| 60 |
+
dtype: string
|
| 61 |
+
splits:
|
| 62 |
+
- name: train
|
| 63 |
+
num_bytes: 652754806
|
| 64 |
+
num_examples: 120761
|
| 65 |
+
- name: validation
|
| 66 |
+
num_bytes: 47277039
|
| 67 |
+
num_examples: 7700
|
| 68 |
+
- name: test
|
| 69 |
+
num_bytes: 40883586
|
| 70 |
+
num_examples: 7700
|
| 71 |
+
download_size: 187724372
|
| 72 |
+
dataset_size: 740915431
|
| 73 |
+
---
|
| 74 |
+
|
| 75 |
+
# Dataset Card for ToTTo
|
| 76 |
+
|
| 77 |
+
## Table of Contents
|
| 78 |
+
- [Dataset Description](#dataset-description)
|
| 79 |
+
- [Dataset Summary](#dataset-summary)
|
| 80 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 81 |
+
- [Languages](#languages)
|
| 82 |
+
- [Dataset Structure](#dataset-structure)
|
| 83 |
+
- [Data Instances](#data-instances)
|
| 84 |
+
- [Data Fields](#data-fields)
|
| 85 |
+
- [Data Splits](#data-splits)
|
| 86 |
+
- [Dataset Creation](#dataset-creation)
|
| 87 |
+
- [Curation Rationale](#curation-rationale)
|
| 88 |
+
- [Source Data](#source-data)
|
| 89 |
+
- [Annotations](#annotations)
|
| 90 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 91 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 92 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 93 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 94 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 95 |
+
- [Additional Information](#additional-information)
|
| 96 |
+
- [Dataset Curators](#dataset-curators)
|
| 97 |
+
- [Licensing Information](#licensing-information)
|
| 98 |
+
- [Citation Information](#citation-information)
|
| 99 |
+
- [Contributions](#contributions)
|
| 100 |
+
|
| 101 |
+
## Dataset Description
|
| 102 |
+
|
| 103 |
+
- **Homepage:** None
|
| 104 |
+
- **Repository:** https://github.com/google-research-datasets/ToTTo
|
| 105 |
+
- **Paper:** https://arxiv.org/abs/2004.14373
|
| 106 |
+
- **Leaderboard:** https://github.com/google-research-datasets/ToTTo#leaderboard
|
| 107 |
+
- **Point of Contact:** [totto@google.com](mailto:totto@google.com)
|
| 108 |
+
|
| 109 |
+
### Dataset Summary
|
| 110 |
+
|
| 111 |
+
ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled
|
| 112 |
+
generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
|
| 113 |
+
|
| 114 |
+
### Supported Tasks and Leaderboards
|
| 115 |
+
|
| 116 |
+
[More Information Needed]
|
| 117 |
+
|
| 118 |
+
### Languages
|
| 119 |
+
|
| 120 |
+
[More Information Needed]
|
| 121 |
+
|
| 122 |
+
## Dataset Structure
|
| 123 |
+
|
| 124 |
+
### Data Instances
|
| 125 |
+
|
| 126 |
+
A sample training set is provided below
|
| 127 |
+
|
| 128 |
+
```
|
| 129 |
+
{'example_id': '1762238357686640028',
|
| 130 |
+
'highlighted_cells': [[13, 2]],
|
| 131 |
+
'id': 0,
|
| 132 |
+
'overlap_subset': 'none',
|
| 133 |
+
'sentence_annotations': {'final_sentence': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
|
| 134 |
+
'original_sentence': ['It is also the first telenovela by the writer to air in the 9 pm timeslot.'],
|
| 135 |
+
'sentence_after_ambiguity': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
|
| 136 |
+
'sentence_after_deletion': ['It is the telenovela air in the 9 pm timeslot.']},
|
| 137 |
+
'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'},
|
| 138 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Run'},
|
| 139 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Title'},
|
| 140 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Chapters'},
|
| 141 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Author'},
|
| 142 |
+
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Director'},
|
| 143 |
+
{'column_span': 1,
|
| 144 |
+
'is_header': True,
|
| 145 |
+
'row_span': 1,
|
| 146 |
+
'value': 'Ibope Rating'}],
|
| 147 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '59'},
|
| 148 |
+
{'column_span': 1,
|
| 149 |
+
'is_header': False,
|
| 150 |
+
'row_span': 1,
|
| 151 |
+
'value': 'June 5, 2000— February 2, 2001'},
|
| 152 |
+
{'column_span': 1,
|
| 153 |
+
'is_header': False,
|
| 154 |
+
'row_span': 1,
|
| 155 |
+
'value': 'Laços de Família'},
|
| 156 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
|
| 157 |
+
{'column_span': 1,
|
| 158 |
+
'is_header': False,
|
| 159 |
+
'row_span': 1,
|
| 160 |
+
'value': 'Manoel Carlos'},
|
| 161 |
+
{'column_span': 1,
|
| 162 |
+
'is_header': False,
|
| 163 |
+
'row_span': 1,
|
| 164 |
+
'value': 'Ricardo Waddington'},
|
| 165 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.9'}],
|
| 166 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '60'},
|
| 167 |
+
{'column_span': 1,
|
| 168 |
+
'is_header': False,
|
| 169 |
+
'row_span': 1,
|
| 170 |
+
'value': 'February 5, 2001— September 28, 2001'},
|
| 171 |
+
{'column_span': 1,
|
| 172 |
+
'is_header': False,
|
| 173 |
+
'row_span': 1,
|
| 174 |
+
'value': 'Porto dos Milagres'},
|
| 175 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
|
| 176 |
+
{'column_span': 1,
|
| 177 |
+
'is_header': False,
|
| 178 |
+
'row_span': 1,
|
| 179 |
+
'value': 'Aguinaldo Silva Ricardo Linhares'},
|
| 180 |
+
{'column_span': 1,
|
| 181 |
+
'is_header': False,
|
| 182 |
+
'row_span': 1,
|
| 183 |
+
'value': 'Marcos Paulo Simões'},
|
| 184 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.6'}],
|
| 185 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '61'},
|
| 186 |
+
{'column_span': 1,
|
| 187 |
+
'is_header': False,
|
| 188 |
+
'row_span': 1,
|
| 189 |
+
'value': 'October 1, 2001— June 14, 2002'},
|
| 190 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'O Clone'},
|
| 191 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
|
| 192 |
+
{'column_span': 1,
|
| 193 |
+
'is_header': False,
|
| 194 |
+
'row_span': 1,
|
| 195 |
+
'value': 'Glória Perez'},
|
| 196 |
+
{'column_span': 1,
|
| 197 |
+
'is_header': False,
|
| 198 |
+
'row_span': 1,
|
| 199 |
+
'value': 'Jayme Monjardim'},
|
| 200 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '47.0'}],
|
| 201 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '62'},
|
| 202 |
+
{'column_span': 1,
|
| 203 |
+
'is_header': False,
|
| 204 |
+
'row_span': 1,
|
| 205 |
+
'value': 'June 17, 2002— February 14, 2003'},
|
| 206 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Esperança'},
|
| 207 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
|
| 208 |
+
{'column_span': 1,
|
| 209 |
+
'is_header': False,
|
| 210 |
+
'row_span': 1,
|
| 211 |
+
'value': 'Benedito Ruy Barbosa'},
|
| 212 |
+
{'column_span': 1,
|
| 213 |
+
'is_header': False,
|
| 214 |
+
'row_span': 1,
|
| 215 |
+
'value': 'Luiz Fernando'},
|
| 216 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '37.7'}],
|
| 217 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '63'},
|
| 218 |
+
{'column_span': 1,
|
| 219 |
+
'is_header': False,
|
| 220 |
+
'row_span': 1,
|
| 221 |
+
'value': 'February 17, 2003— October 10, 2003'},
|
| 222 |
+
{'column_span': 1,
|
| 223 |
+
'is_header': False,
|
| 224 |
+
'row_span': 1,
|
| 225 |
+
'value': 'Mulheres Apaixonadas'},
|
| 226 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
|
| 227 |
+
{'column_span': 1,
|
| 228 |
+
'is_header': False,
|
| 229 |
+
'row_span': 1,
|
| 230 |
+
'value': 'Manoel Carlos'},
|
| 231 |
+
{'column_span': 1,
|
| 232 |
+
'is_header': False,
|
| 233 |
+
'row_span': 1,
|
| 234 |
+
'value': 'Ricardo Waddington'},
|
| 235 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.6'}],
|
| 236 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '64'},
|
| 237 |
+
{'column_span': 1,
|
| 238 |
+
'is_header': False,
|
| 239 |
+
'row_span': 1,
|
| 240 |
+
'value': 'October 13, 2003— June 25, 2004'},
|
| 241 |
+
{'column_span': 1,
|
| 242 |
+
'is_header': False,
|
| 243 |
+
'row_span': 1,
|
| 244 |
+
'value': 'Celebridade'},
|
| 245 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
|
| 246 |
+
{'column_span': 1,
|
| 247 |
+
'is_header': False,
|
| 248 |
+
'row_span': 1,
|
| 249 |
+
'value': 'Gilberto Braga'},
|
| 250 |
+
{'column_span': 1,
|
| 251 |
+
'is_header': False,
|
| 252 |
+
'row_span': 1,
|
| 253 |
+
'value': 'Dennis Carvalho'},
|
| 254 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.0'}],
|
| 255 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '65'},
|
| 256 |
+
{'column_span': 1,
|
| 257 |
+
'is_header': False,
|
| 258 |
+
'row_span': 1,
|
| 259 |
+
'value': 'June 28, 2004— March 11, 2005'},
|
| 260 |
+
{'column_span': 1,
|
| 261 |
+
'is_header': False,
|
| 262 |
+
'row_span': 1,
|
| 263 |
+
'value': 'Senhora do Destino'},
|
| 264 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
|
| 265 |
+
{'column_span': 1,
|
| 266 |
+
'is_header': False,
|
| 267 |
+
'row_span': 1,
|
| 268 |
+
'value': 'Aguinaldo Silva'},
|
| 269 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
|
| 270 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '50.4'}],
|
| 271 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '66'},
|
| 272 |
+
{'column_span': 1,
|
| 273 |
+
'is_header': False,
|
| 274 |
+
'row_span': 1,
|
| 275 |
+
'value': 'March 14, 2005— November 4, 2005'},
|
| 276 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'América'},
|
| 277 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
|
| 278 |
+
{'column_span': 1,
|
| 279 |
+
'is_header': False,
|
| 280 |
+
'row_span': 1,
|
| 281 |
+
'value': 'Glória Perez'},
|
| 282 |
+
{'column_span': 1,
|
| 283 |
+
'is_header': False,
|
| 284 |
+
'row_span': 1,
|
| 285 |
+
'value': 'Jayme Monjardim Marcos Schechtman'},
|
| 286 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '49.4'}],
|
| 287 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '67'},
|
| 288 |
+
{'column_span': 1,
|
| 289 |
+
'is_header': False,
|
| 290 |
+
'row_span': 1,
|
| 291 |
+
'value': 'November 7, 2005— July 7, 2006'},
|
| 292 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Belíssima'},
|
| 293 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
|
| 294 |
+
{'column_span': 1,
|
| 295 |
+
'is_header': False,
|
| 296 |
+
'row_span': 1,
|
| 297 |
+
'value': 'Sílvio de Abreu'},
|
| 298 |
+
{'column_span': 1,
|
| 299 |
+
'is_header': False,
|
| 300 |
+
'row_span': 1,
|
| 301 |
+
'value': 'Denise Saraceni'},
|
| 302 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '48.5'}],
|
| 303 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '68'},
|
| 304 |
+
{'column_span': 1,
|
| 305 |
+
'is_header': False,
|
| 306 |
+
'row_span': 1,
|
| 307 |
+
'value': 'July 10, 2006— March 2, 2007'},
|
| 308 |
+
{'column_span': 1,
|
| 309 |
+
'is_header': False,
|
| 310 |
+
'row_span': 1,
|
| 311 |
+
'value': 'Páginas da Vida'},
|
| 312 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
|
| 313 |
+
{'column_span': 1,
|
| 314 |
+
'is_header': False,
|
| 315 |
+
'row_span': 1,
|
| 316 |
+
'value': 'Manoel Carlos'},
|
| 317 |
+
{'column_span': 1,
|
| 318 |
+
'is_header': False,
|
| 319 |
+
'row_span': 1,
|
| 320 |
+
'value': 'Jayme Monjardim'},
|
| 321 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.8'}],
|
| 322 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '69'},
|
| 323 |
+
{'column_span': 1,
|
| 324 |
+
'is_header': False,
|
| 325 |
+
'row_span': 1,
|
| 326 |
+
'value': 'March 5, 2007— September 28, 2007'},
|
| 327 |
+
{'column_span': 1,
|
| 328 |
+
'is_header': False,
|
| 329 |
+
'row_span': 1,
|
| 330 |
+
'value': 'Paraíso Tropical'},
|
| 331 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '179'},
|
| 332 |
+
{'column_span': 1,
|
| 333 |
+
'is_header': False,
|
| 334 |
+
'row_span': 1,
|
| 335 |
+
'value': 'Gilberto Braga Ricardo Linhares'},
|
| 336 |
+
{'column_span': 1,
|
| 337 |
+
'is_header': False,
|
| 338 |
+
'row_span': 1,
|
| 339 |
+
'value': 'Dennis Carvalho'},
|
| 340 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '42.8'}],
|
| 341 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '70'},
|
| 342 |
+
{'column_span': 1,
|
| 343 |
+
'is_header': False,
|
| 344 |
+
'row_span': 1,
|
| 345 |
+
'value': 'October 1, 2007— May 31, 2008'},
|
| 346 |
+
{'column_span': 1,
|
| 347 |
+
'is_header': False,
|
| 348 |
+
'row_span': 1,
|
| 349 |
+
'value': 'Duas Caras'},
|
| 350 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '210'},
|
| 351 |
+
{'column_span': 1,
|
| 352 |
+
'is_header': False,
|
| 353 |
+
'row_span': 1,
|
| 354 |
+
'value': 'Aguinaldo Silva'},
|
| 355 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
|
| 356 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '41.1'}],
|
| 357 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '71'},
|
| 358 |
+
{'column_span': 1,
|
| 359 |
+
'is_header': False,
|
| 360 |
+
'row_span': 1,
|
| 361 |
+
'value': 'June 2, 2008— January 16, 2009'},
|
| 362 |
+
{'column_span': 1,
|
| 363 |
+
'is_header': False,
|
| 364 |
+
'row_span': 1,
|
| 365 |
+
'value': 'A Favorita'},
|
| 366 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '197'},
|
| 367 |
+
{'column_span': 1,
|
| 368 |
+
'is_header': False,
|
| 369 |
+
'row_span': 1,
|
| 370 |
+
'value': 'João Emanuel Carneiro'},
|
| 371 |
+
{'column_span': 1,
|
| 372 |
+
'is_header': False,
|
| 373 |
+
'row_span': 1,
|
| 374 |
+
'value': 'Ricardo Waddington'},
|
| 375 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '39.5'}],
|
| 376 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '72'},
|
| 377 |
+
{'column_span': 1,
|
| 378 |
+
'is_header': False,
|
| 379 |
+
'row_span': 1,
|
| 380 |
+
'value': 'January 19, 2009— September 11, 2009'},
|
| 381 |
+
{'column_span': 1,
|
| 382 |
+
'is_header': False,
|
| 383 |
+
'row_span': 1,
|
| 384 |
+
'value': 'Caminho das Índias'},
|
| 385 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
|
| 386 |
+
{'column_span': 1,
|
| 387 |
+
'is_header': False,
|
| 388 |
+
'row_span': 1,
|
| 389 |
+
'value': 'Glória Perez'},
|
| 390 |
+
{'column_span': 1,
|
| 391 |
+
'is_header': False,
|
| 392 |
+
'row_span': 1,
|
| 393 |
+
'value': 'Marcos Schechtman'},
|
| 394 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '38.8'}],
|
| 395 |
+
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '73'},
|
| 396 |
+
{'column_span': 1,
|
| 397 |
+
'is_header': False,
|
| 398 |
+
'row_span': 1,
|
| 399 |
+
'value': 'September 14, 2009— May 14, 2010'},
|
| 400 |
+
{'column_span': 1,
|
| 401 |
+
'is_header': False,
|
| 402 |
+
'row_span': 1,
|
| 403 |
+
'value': 'Viver a Vida'},
|
| 404 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
|
| 405 |
+
{'column_span': 1,
|
| 406 |
+
'is_header': False,
|
| 407 |
+
'row_span': 1,
|
| 408 |
+
'value': 'Manoel Carlos'},
|
| 409 |
+
{'column_span': 1,
|
| 410 |
+
'is_header': False,
|
| 411 |
+
'row_span': 1,
|
| 412 |
+
'value': 'Jayme Monjardim'},
|
| 413 |
+
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '35.6'}]],
|
| 414 |
+
'table_page_title': 'List of 8/9 PM telenovelas of Rede Globo',
|
| 415 |
+
'table_section_text': '',
|
| 416 |
+
'table_section_title': '2000s',
|
| 417 |
+
'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_8/9_PM_telenovelas_of_Rede_Globo'}
|
| 418 |
+
```
|
| 419 |
+
|
| 420 |
+
Please note that in test set sentence annotations are not available and thus values inside `sentence_annotations` can be safely ignored.
|
| 421 |
+
|
| 422 |
+
### Data Fields
|
| 423 |
+
|
| 424 |
+
- `table_webpage_url` (`str`): Table webpage URL.
|
| 425 |
+
- `table_page_title` (`str`): Table metadata with context about the table.
|
| 426 |
+
- `table_section_title` (`str`): Table metadata with context about the table.
|
| 427 |
+
- `table_section_text` (`str`): Table metadata with context about the table.
|
| 428 |
+
- `table` (`List[List[Dict]]`): The outer lists represents rows and the inner lists columns. Each Dict has the fields:
|
| 429 |
+
- `column_span` (`int`)
|
| 430 |
+
- `is_header` (`bool`)
|
| 431 |
+
- `row_span` (`int`)
|
| 432 |
+
- `value` (`str`)
|
| 433 |
+
- `highlighted_cells` (`List[[row_index, column_index]]`): Where each `[row_index, column_index]` pair indicates that `table[row_index][column_index]` is highlighted.
|
| 434 |
+
- `example_id` (`int`): A unique id for this example.
|
| 435 |
+
- `sentence_annotations`: Consists of the `original_sentence` and the sequence of revised sentences performed in order to produce the `final_sentence`.
|
| 436 |
+
|
| 437 |
+
### Data Splits
|
| 438 |
+
|
| 439 |
+
```
|
| 440 |
+
DatasetDict({
|
| 441 |
+
train: Dataset({
|
| 442 |
+
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
|
| 443 |
+
num_rows: 120761
|
| 444 |
+
})
|
| 445 |
+
validation: Dataset({
|
| 446 |
+
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
|
| 447 |
+
num_rows: 7700
|
| 448 |
+
})
|
| 449 |
+
test: Dataset({
|
| 450 |
+
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
|
| 451 |
+
num_rows: 7700
|
| 452 |
+
})
|
| 453 |
+
})
|
| 454 |
+
```
|
| 455 |
+
|
| 456 |
+
## Dataset Creation
|
| 457 |
+
|
| 458 |
+
### Curation Rationale
|
| 459 |
+
|
| 460 |
+
[More Information Needed]
|
| 461 |
+
|
| 462 |
+
### Source Data
|
| 463 |
+
|
| 464 |
+
[More Information Needed]
|
| 465 |
+
|
| 466 |
+
#### Initial Data Collection and Normalization
|
| 467 |
+
|
| 468 |
+
[More Information Needed]
|
| 469 |
+
|
| 470 |
+
#### Who are the source language producers?
|
| 471 |
+
|
| 472 |
+
[More Information Needed]
|
| 473 |
+
|
| 474 |
+
### Annotations
|
| 475 |
+
|
| 476 |
+
[More Information Needed]
|
| 477 |
+
|
| 478 |
+
#### Annotation process
|
| 479 |
+
|
| 480 |
+
[More Information Needed]
|
| 481 |
+
|
| 482 |
+
#### Who are the annotators?
|
| 483 |
+
|
| 484 |
+
[More Information Needed]
|
| 485 |
+
|
| 486 |
+
### Personal and Sensitive Information
|
| 487 |
+
|
| 488 |
+
[More Information Needed]
|
| 489 |
+
|
| 490 |
+
## Considerations for Using the Data
|
| 491 |
+
|
| 492 |
+
### Social Impact of Dataset
|
| 493 |
+
|
| 494 |
+
[More Information Needed]
|
| 495 |
+
|
| 496 |
+
### Discussion of Biases
|
| 497 |
+
|
| 498 |
+
[More Information Needed]
|
| 499 |
+
|
| 500 |
+
### Other Known Limitations
|
| 501 |
+
|
| 502 |
+
[More Information Needed]
|
| 503 |
+
|
| 504 |
+
## Additional Information
|
| 505 |
+
|
| 506 |
+
### Dataset Curators
|
| 507 |
+
|
| 508 |
+
[More Information Needed]
|
| 509 |
+
|
| 510 |
+
### Licensing Information
|
| 511 |
+
|
| 512 |
+
[More Information Needed]
|
| 513 |
+
|
| 514 |
+
### Citation Information
|
| 515 |
+
|
| 516 |
+
```
|
| 517 |
+
@inproceedings{parikh2020totto,
|
| 518 |
+
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
|
| 519 |
+
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
|
| 520 |
+
booktitle={Proceedings of EMNLP},
|
| 521 |
+
year={2020}
|
| 522 |
+
}
|
| 523 |
+
```
|
| 524 |
+
|
| 525 |
+
### Contributions
|
| 526 |
+
|
| 527 |
+
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
|