Update files from the datasets library (from 1.3.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

README.md

@@ -46,6 +46,7 @@ task_ids:
   - [Dataset Curators](#dataset-curators)
   - [Licensing Information](#licensing-information)
   - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
 
 ## Dataset Description
 
@@ -187,3 +188,7 @@ The dataset is split into a `train`, and `test` split with the following sizes:
     abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
 }
 ```
+
+### Contributions
+
+Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset.

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.\n", "citation": "@inproceedings{manotas-etal-2020-limit,\n    title = \"{L}i{M}i{T}: The Literal Motion in Text Dataset\",\n    author = \"Manotas, Irene  and\n      Vo, Ngoc Phuoc An  and\n      Sheinin, Vadim\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.findings-emnlp.88\",\n    doi = \"10.18653/v1/2020.findings-emnlp.88\",\n    pages = \"991--1000\",\n    abstract = \"Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.\",\n}\n", "homepage": "https://github.com/ilmgut/limit_dataset", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "motion": {"dtype": "string", "id": null, "_type": "Value"}, "motion_entities": [{"entity": {"dtype": "string", "id": null, "_type": "Value"}, "start_index": {"dtype": "int32", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "limit", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3064208, "num_examples": 23559, "dataset_name": "limit"}, "test": {"name": "test", "num_bytes": 139742, "num_examples": 1000, "dataset_name": "limit"}}, "download_checksums": {"https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data/train.json": {"num_bytes": 4036108, "checksum": "2d4c1ffe768526c9ad2d9f04da4b29c590901705968c6d775b5e64881870520d"}, "https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data/test.json": {"num_bytes": 178817, "checksum": "be0ce77065ee641673f3a6ecc3b94b98f5b60f7516c17f9afb4af2e46c7a7db6"}}, "download_size": 4214925, "post_processing_size": null, "dataset_size": 3203950, "size_in_bytes": 7418875}}
+{"default": {"description": "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.\n", "citation": "@inproceedings{manotas-etal-2020-limit,\n    title = \"{L}i{M}i{T}: The Literal Motion in Text Dataset\",\n    author = \"Manotas, Irene  and\n      Vo, Ngoc Phuoc An  and\n      Sheinin, Vadim\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n    month = nov,\n    year = \"2020\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/2020.findings-emnlp.88\",\n    doi = \"10.18653/v1/2020.findings-emnlp.88\",\n    pages = \"991--1000\",\n    abstract = \"Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.\",\n}\n", "homepage": "https://github.com/ilmgut/limit_dataset", "license": "", "features": {"id": {"dtype": "int32", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "motion": {"dtype": "string", "id": null, "_type": "Value"}, "motion_entities": [{"entity": {"dtype": "string", "id": null, "_type": "Value"}, "start_index": {"dtype": "int32", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "limit", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3064208, "num_examples": 23559, "dataset_name": "limit"}, "test": {"name": "test", "num_bytes": 139742, "num_examples": 1000, "dataset_name": "limit"}}, "download_checksums": {"https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data/train.json": {"num_bytes": 4036108, "checksum": "2d4c1ffe768526c9ad2d9f04da4b29c590901705968c6d775b5e64881870520d"}, "https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data/test.json": {"num_bytes": 178817, "checksum": "be0ce77065ee641673f3a6ecc3b94b98f5b60f7516c17f9afb4af2e46c7a7db6"}}, "download_size": 4214925, "post_processing_size": null, "dataset_size": 3203950, "size_in_bytes": 7418875}}

limit.py

@@ -48,7 +48,7 @@ describing physical occurrence of motion, with annotated physical entities in mo
 
 _HOMEPAGE = "https://github.com/ilmgut/limit_dataset"
 
-_BASE_URL = "https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data"
+_BASE_URL = "https://raw.githubusercontent.com/ilmgut/limit_dataset/0707d3989cd8848f0f11527c77dcf168fefd2b23/data"
 
 _URLS = {
     "train": f"{_BASE_URL}/train.json",