Delete loading script

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,
-            }