Convert dataset to Parquet

README.md

@@ -19,6 +19,7 @@ task_ids:
 - parsing
 pretty_name: AMTTL
 dataset_info:
+  config_name: amttl
   features:
   - name: id
     dtype: string
@@ -32,19 +33,28 @@ dataset_info:
           '1': I
           '2': E
           '3': S
-  config_name: amttl
   splits:
   - name: train
-    num_bytes: 1132212
+    num_bytes: 1132196
     num_examples: 3063
   - name: validation
-    num_bytes: 324374
+    num_bytes: 324358
     num_examples: 822
   - name: test
-    num_bytes: 328525
+    num_bytes: 328509
     num_examples: 908
-  download_size: 685534
-  dataset_size: 1785111
+  download_size: 274351
+  dataset_size: 1785063
+configs:
+- config_name: amttl
+  data_files:
+  - split: train
+    path: amttl/train-*
+  - split: validation
+    path: amttl/validation-*
+  - split: test
+    path: amttl/test-*
+  default: true
 ---
 
 # Dataset Card for AMTTL

amttl.py

@@ -1,147 +0,0 @@
-# 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 datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_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):
-        logger.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,
-            }

amttl/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:e162219c3d2e9a4b234407072169e58475c70f69a1118c4c92c1cc8bdb7fddcf
+size 51311

amttl/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:93ff0e728fa5bf6cf4c32805ac01529c1b022f29b39f28406a5e7fd28b9b6342
+size 172615

amttl/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e7992b50bd6d87521937260ed7ebce5a986b8eb52ad0905373fe94d6b155c53e
+size 50425

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}