Update files from the datasets library (from 1.2.0)

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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+languages:
+- zh
+licenses:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- parsing
+---
+
+# Dataset Card for AMTTL
+
+## 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:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
+- **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
+- **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307)
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+```bibtex
+@inproceedings{xing2018adaptive,
+  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
+  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
+  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
+  pages={3619--3630},
+  year={2018}
+}
+```

amttl.py

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+# coding=utf-8
+# Copyright 2020 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.
+
+# Lint as: python3
+"""Introduction to AMTTL CWS Dataset"""
+
+import logging
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{xing2018adaptive,
+  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
+  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
+  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
+  pages={3619--3630},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """\
+Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
+when dealing with domain text, especially for a domain with lots of special terms and diverse
+writing styles, such as the biomedical domain. However, building domain-specific CWS requires
+extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
+knowledge from high resource to low resource domains. Extensive experiments show that our mode
+achieves consistently higher accuracy than the single-task CWS and other transfer learning
+baselines, especially when there is a large disparity between source and target domains.
+
+This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
+The tags are in BIES scheme.
+
+For more details see https://www.aclweb.org/anthology/C18-1307/
+"""
+
+_URL = "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/"
+_TRAINING_FILE = "forum_train.txt"
+_DEV_FILE = "forum_dev.txt"
+_TEST_FILE = "forum_test.txt"
+
+
+class AmttlConfig(datasets.BuilderConfig):
+    """BuilderConfig for AMTTL"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for AMTTL.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(AmttlConfig, self).__init__(**kwargs)
+
+
+class Amttl(datasets.GeneratorBasedBuilder):
+    """AMTTL Chinese Word Segmentation dataset."""
+
+    BUILDER_CONFIGS = [
+        AmttlConfig(
+            name="amttl",
+            version=datasets.Version("1.0.0"),
+            description="AMTTL medical Chinese word segmentation dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "B",
+                                "I",
+                                "E",
+                                "S",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://www.aclweb.org/anthology/C18-1307/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        logging.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            tags = []
+            for line in f:
+                line_stripped = line.strip()
+                if line_stripped == "":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "tags": tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        tags = []
+                else:
+                    splits = line_stripped.split("\t")
+                    if len(splits) == 1:
+                        splits.append("O")
+                    tokens.append(splits[0])
+                    tags.append(splits[1])
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "tags": tags,
+            }

dataset_infos.json

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+{"amttl": {"description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n", "citation": "@inproceedings{xing2018adaptive,\n  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n  pages={3619--3630},\n  year={2018}\n}\n", "homepage": "https://www.aclweb.org/anthology/C18-1307/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tags": {"feature": {"num_classes": 4, "names": ["B", "I", "E", "S"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "amttl", "config_name": "amttl", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1132212, "num_examples": 3063, "dataset_name": "amttl"}, "validation": {"name": "validation", "num_bytes": 324374, "num_examples": 822, "dataset_name": "amttl"}, "test": {"name": "test", "num_bytes": 328525, "num_examples": 908, "dataset_name": "amttl"}}, "download_checksums": {"https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_train.txt": {"num_bytes": 434357, "checksum": "9819373963ea04d1d28844d5bc83b6b0332fad8b5f2e73092bcfc58dc6d6292a"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_dev.txt": {"num_bytes": 124973, "checksum": "1a2eb461b98d2a9160baad7f76d003cc0917b998e8283bcffa52b71224dd9d17"}, "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/forum_test.txt": {"num_bytes": 126204, "checksum": "aea1a8cf244cd565e94bd193a1eef7a10b16eeb0b6fbb6ed1d2fefbd55360dd6"}}, "download_size": 685534, "post_processing_size": null, "dataset_size": 1785111, "size_in_bytes": 2470645}}

dummy/amttl/1.0.0/dummy_data.zip

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+size 568