Update files from the datasets library (from 1.2.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

.gitattributes

@@ -0,0 +1,27 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,189 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages:
+- en
+licenses:
+- cc-by-sa-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- extended|net-activities-captions
+- original
+task_categories:
+- structure-prediction
+- text-classification
+task_ids:
+- multi-class-classification
+- named-entity-recognition
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** -
+- **Repository:** [github](https://github.com/ilmgut/limit_dataset)
+- **Paper:** [LiMiT: The Literal Motion in Text Dataset](https://www.aclweb.org/anthology/2020.findings-emnlp.88/)
+- **Leaderboard:** N/A
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+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.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+The text in the dataset is in English (`en`).
+
+## Dataset Structure
+
+### Data Instances
+
+Example of one instance in the dataset
+
+```
+{
+	"id": 0,
+	"motion": "yes",
+	"motion_entities": [
+		{
+			"entity": "little boy",
+			"start_index": 2
+		},
+		{
+			"entity": "ball",
+			"start_index": 30
+		}
+	],
+	"sentence": " A little boy holding a yellow ball walks by."
+}
+```
+
+### Data Fields
+
+- `id`: intger index of the example
+- `motion`:  indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not
+- `motion_entities`: A `list` of `dicts` with following keys
+    - `entity`: the extracted entity in motion 
+    - `start_index`: index in the sentence for the first char of the entity text
+
+### Data Splits
+
+The dataset is split into a `train`, and `test` split with the following sizes:
+
+|                            | Tain   | Valid |
+| -----                      | ------ | ----- |
+| Number of examples         | 23559  | 1000  |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{manotas-etal-2020-limit,
+    title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
+    author = "Manotas, Irene  and
+      Vo, Ngoc Phuoc An  and
+      Sheinin, Vadim",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
+    doi = "10.18653/v1/2020.findings-emnlp.88",
+    pages = "991--1000",
+    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.",
+}
+```

dataset_infos.json

@@ -0,0 +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}}

dummy/1.0.0/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c8dc71628813df48bae032323f5088d3c36b9537da462e84258281c5a382b75
+size 1054

limit.py

@@ -0,0 +1,114 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LiMiT: The Literal Motion in Text Dataset"""
+
+from __future__ import absolute_import, division, print_function
+
+import json
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{manotas-etal-2020-limit,
+    title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
+    author = "Manotas, Irene  and
+      Vo, Ngoc Phuoc An  and
+      Sheinin, Vadim",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
+    doi = "10.18653/v1/2020.findings-emnlp.88",
+    pages = "991--1000",
+    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.",
+}
+"""
+
+_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.
+"""
+
+_HOMEPAGE = "https://github.com/ilmgut/limit_dataset"
+
+_BASE_URL = "https://raw.githubusercontent.com/ilmgut/limit_dataset/master/data"
+
+_URLS = {
+    "train": f"{_BASE_URL}/train.json",
+    "test": f"{_BASE_URL}/test.json",
+}
+
+
+class Limit(datasets.GeneratorBasedBuilder):
+    """LiMiT: The Literal Motion in Text Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = {
+            "id": datasets.Value("int32"),
+            "sentence": datasets.Value("string"),
+            "motion": datasets.Value("string"),
+            "motion_entities": [
+                {
+                    "entity": datasets.Value("string"),
+                    "start_index": datasets.Value("int32"),
+                }
+            ],
+        }
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": downloaded_files["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": downloaded_files["test"]},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            examples = json.load(f)
+            for idx, example in examples.items():
+                if example["motion_entity"] == "":
+                    motion_entities = []
+                else:
+                    motion_entities = example["motion_entity"].strip().split("\n")
+                    motion_entities = [entity.split(":") for entity in motion_entities]
+                    motion_entities = [
+                        {"entity": entity, "start_index": int(start_idx)} for entity, start_idx in motion_entities
+                    ]
+
+                example.pop("motion_entity")
+                example["motion_entities"] = motion_entities
+                example["id"] = idx
+
+                yield idx, example