Datasets:
Fix crd3 (#4705)
Browse files* fix crd3
* fix dummy data
Commit from https://github.com/huggingface/datasets/commit/c15b391942764152f6060b59921b09cacc5f22a6
- README.md +4 -15
- crd3.py +11 -9
- dataset_infos.json +1 -1
- dummy/0.0.0/dummy_data.zip +2 -2
README.md
CHANGED
@@ -55,9 +55,6 @@ paperswithcode_id: crd3
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- **Repository:** [CRD3 repository](https://github.com/RevanthRameshkumar/CRD3)
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- **Paper:** [Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](https://www.aclweb.org/anthology/2020.acl-main.459/)
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- **Size of downloaded dataset files:** 279.93 MB
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- **Size of the generated dataset:** 4020.33 MB
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- **Total amount of disk used:** 4300.25 MB
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### Dataset Summary
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@@ -69,6 +66,7 @@ collaboration and spoken interaction. For each dialogue, there are a large numbe
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and semantic ties to the previous dialogues.
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### Supported Tasks and Leaderboards
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`summarization`: The dataset can be used to train a model for abstractive summarization. A [fast abstractive summarization-RL](https://github.com/ChenRocks/fast_abs_rl) model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.
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### Languages
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### Data Instances
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#### default
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- **Size of downloaded dataset files:** 279.93 MB
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- **Size of the generated dataset:** 4020.33 MB
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- **Total amount of disk used:** 4300.25 MB
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-
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An example of 'train' looks as follows.
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```
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{
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"alignment_score": 3.679936647415161,
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The data fields are the same among all splits.
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#### default
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- `chunk`: a `string` feature.
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- `chunk_id`: a `int32` feature.
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- `turn_start`: a `int32` feature.
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| name | train |validation| test |
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|-------|------:|---------:|------:|
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-
|default|
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## Dataset Creation
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### Citation Information
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```
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@inproceedings{
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title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
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author = {Rameshkumar, Revanth and Bailey, Peter},
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publisher = {Association for Computational Linguistics},
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conference = {ACL}
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}
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```
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### Contributions
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Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
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- **Repository:** [CRD3 repository](https://github.com/RevanthRameshkumar/CRD3)
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- **Paper:** [Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](https://www.aclweb.org/anthology/2020.acl-main.459/)
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- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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### Dataset Summary
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and semantic ties to the previous dialogues.
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### Supported Tasks and Leaderboards
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+
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`summarization`: The dataset can be used to train a model for abstractive summarization. A [fast abstractive summarization-RL](https://github.com/ChenRocks/fast_abs_rl) model was presented as a baseline, which achieves ROUGE-L-F1 of 25.18.
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### Languages
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### Data Instances
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An example of 'train' looks as follows.
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```
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{
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"alignment_score": 3.679936647415161,
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The data fields are the same among all splits.
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- `chunk`: a `string` feature.
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- `chunk_id`: a `int32` feature.
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- `turn_start`: a `int32` feature.
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| name | train |validation| test |
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|-------|------:|---------:|------:|
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|default|38,969| 6,327|7,500|
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## Dataset Creation
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### Citation Information
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```bibtex
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@inproceedings{
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title = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},
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author = {Rameshkumar, Revanth and Bailey, Peter},
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publisher = {Association for Computational Linguistics},
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conference = {ACL}
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}
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```
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### Contributions
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Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset.
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crd3.py
CHANGED
@@ -45,11 +45,11 @@ collaboration and spoken interaction. For each dialogue, there are a large numbe
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and semantic ties to the previous dialogues.
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"""
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_URL = "https://
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def get_train_test_dev_files(files, test_split, train_split, dev_split):
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test_files
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for file in files:
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filename = os.path.split(file)[1].split("_")[0]
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if filename in test_split:
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)
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def _split_generators(self, dl_manager):
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with open(test_file, encoding="utf-8") as f:
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test_splits = [file.replace("\n", "") for file in f.readlines()]
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train_splits = [file.replace("\n", "") for file in f.readlines()]
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with open(dev_file, encoding="utf-8") as f:
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dev_splits = [file.replace("\n", "") for file in f.readlines()]
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c2 = "
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c3 = "
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c4 = "
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files = [os.path.join(path, c2, file) for file in sorted(os.listdir(os.path.join(path, c2)))]
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files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))])
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files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))])
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and semantic ties to the previous dialogues.
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"""
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_URL = "https://huggingface.co/datasets/crd3/resolve/72bffe55b4d5bf19b530d3e417447b3384ba3673/data/aligned%20data.zip"
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def get_train_test_dev_files(files, test_split, train_split, dev_split):
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test_files, dev_files, train_files = [], [], []
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for file in files:
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filename = os.path.split(file)[1].split("_")[0]
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if filename in test_split:
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)
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def _split_generators(self, dl_manager):
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root = dl_manager.download_and_extract(_URL)
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path = os.path.join(root, "aligned data")
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test_file = os.path.join(path, "test_files")
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train_file = os.path.join(path, "train_files")
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dev_file = os.path.join(path, "val_files")
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with open(test_file, encoding="utf-8") as f:
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test_splits = [file.replace("\n", "") for file in f.readlines()]
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train_splits = [file.replace("\n", "") for file in f.readlines()]
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with open(dev_file, encoding="utf-8") as f:
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dev_splits = [file.replace("\n", "") for file in f.readlines()]
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c2 = "c=2"
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c3 = "c=3"
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c4 = "c=4"
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files = [os.path.join(path, c2, file) for file in sorted(os.listdir(os.path.join(path, c2)))]
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files.extend([os.path.join(path, c3, file) for file in sorted(os.listdir(os.path.join(path, c3)))])
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files.extend([os.path.join(path, c4, file) for file in sorted(os.listdir(os.path.join(path, c4)))])
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dataset_infos.json
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{"default": {"description": "\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.\n", "citation": "\n@inproceedings{\ntitle = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},\nauthor = {Rameshkumar, Revanth and Bailey, Peter},\nyear = {2020},\npublisher = {Association for Computational Linguistics},\nconference = {ACL}\n}\n ", "homepage": "https://github.com/RevanthRameshkumar/CRD3", "license": "", "features": {"chunk": {"dtype": "string", "id": null, "_type": "Value"}, "chunk_id": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_start": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_end": {"dtype": "int32", "id": null, "_type": "Value"}, "alignment_score": {"dtype": "float32", "id": null, "_type": "Value"}, "turns": {"
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{"default": {"description": "\nStorytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset.\nCritical Role is an unscripted, live-streamed show where a fixed group of people play Dungeons and Dragons, an open-ended role-playing game.\nThe dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. It also includes corresponding\nabstractive summaries collected from the Fandom wiki. The dataset is linguistically unique in that the narratives are generated entirely through player\ncollaboration and spoken interaction. For each dialogue, there are a large number of turns, multiple abstractive summaries with varying levels of detail,\nand semantic ties to the previous dialogues.\n", "citation": "\n@inproceedings{\ntitle = {Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset},\nauthor = {Rameshkumar, Revanth and Bailey, Peter},\nyear = {2020},\npublisher = {Association for Computational Linguistics},\nconference = {ACL}\n}\n ", "homepage": "https://github.com/RevanthRameshkumar/CRD3", "license": "", "features": {"chunk": {"dtype": "string", "id": null, "_type": "Value"}, "chunk_id": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_start": {"dtype": "int32", "id": null, "_type": "Value"}, "turn_end": {"dtype": "int32", "id": null, "_type": "Value"}, "alignment_score": {"dtype": "float32", "id": null, "_type": "Value"}, "turns": [{"names": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "utterances": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "number": {"dtype": "int32", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "crd3", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 236605152, "num_examples": 38969, "dataset_name": "crd3"}, "test": {"name": "test", "num_bytes": 40269203, "num_examples": 7500, "dataset_name": "crd3"}, "validation": {"name": "validation", "num_bytes": 41543528, "num_examples": 6327, "dataset_name": "crd3"}}, "download_checksums": {"https://huggingface.co/datasets/crd3/resolve/72bffe55b4d5bf19b530d3e417447b3384ba3673/data/aligned%20data.zip": {"num_bytes": 117519820, "checksum": "c66bd9f7848bcd514a35c154edd2fc874f1a3076876d8bd7208bf3caf4b7fb0b"}}, "download_size": 117519820, "post_processing_size": null, "dataset_size": 318417883, "size_in_bytes": 435937703}}
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dummy/0.0.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:9b58147635d59ebacc64e62d0d8855902cb14b447e28acb7794b97c48ea35cef
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size 22306
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