NER-BAILII-UK-CCA.py

# 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
"""Waleed-bin-Qamar/NER-BAILII-UK-CCA. Criminal court appeals of uk"""

import csv
import json
import os
import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{title = "A great new dataset",
author = "A great new dataset",
booktitle = "A great new dataset",
month = sep,
year = "2050",
address = "a,b",
publisher = "Association in b",
doi = " ",
pages = " ",
abstract = " ",
}
"""

_DESCRIPTION = """\
A great new dataset
"""
# due privacy issues private addresses are used
_URL = "https://drive.google.com/"
_TRAINING_FILE_URL = "uc?id=1Hn30-V5tm_JxKB7Q09BpZJu1VylinpGu&export=download"
_DEV_FILE_URL = "uc?id=1saGAPzk0zaj6dIo5FKa1rb5SCdHwJnAq&export=download"
_TEST_FILE_URL = "uc?id=1stsN0Iq1guKNH0oJc0ZjqIj57ExR_iYT&export=download"


class NER_BAILIIConfig(datasets.BuilderConfig):
  def __init__(self, **kwargs):
    """BuilderConfig for NER_BAILIIConfig.
    
    Args:
    **kwargs: keyword arguments forwarded to super.
    """
    super(NER_BAILIIConfig, self).__init__(**kwargs)


class NER_BAILII(datasets.GeneratorBasedBuilder):
  """NER-BAILII-UK-CCA. Criminal court appeals of uk"""
  BUILDER_CONFIGS = [
      NER_BAILIIConfig(
          name="NER-BAILII-UK-CCA", version=datasets.Version("1.0.0"), description="The NER-BAILII-UK-CCA Name Entities recognization Dataset"
          )
      ]
  
  def _info(self):
    return datasets.DatasetInfo(
        
        description=_DESCRIPTION,
        features=datasets.Features(
            {"id": datasets.Value("string"),
             "tokens": datasets.Sequence(datasets.Value("string")),
             "ner_tags": datasets.Sequence(
                 datasets.features.ClassLabel(
                     names=[
                         'O',
                         'B-defendant_pleaded_guilty',
                         'I-defendant_pleaded_guilty',
                         'B-appeal_against_sentence',
                         'I-appeal_against_sentence',
                         'B-defendant',
                         'I-defendant',
                         'B-date_of_original_trial_or_conviction',
                         'I-date_of_original_trial_or_conviction',
                         'B-offence',
                         'I-offence',
                         'B-defendant_original_sentence',
                         'I-defendant_original_sentence',
                         'B-decision_granted',
                         'I-decision_granted',
                         'B-date_of_crime',
                         'I-date_of_crime',
                         'B-decision_refused',
                         'I-decision_refused',
                         'B-expert_witness_used_at_original_trail',
                         'I-expert_witness_used_at_original_trail',
                         'B-victim_age',
                         'I-victim_age',
                         'B-defendant_is_female',
                         'I-defendant_is_female',
                         'B-appeal_against_conviction',
                         'I-appeal_against_conviction',
                         'B-defendant_custodial_sentence_years',
                         'I-defendant_custodial_sentence_years',
                         'B-date_of_offence',
                         'I-date_of_offence',
                         'B-decision_quashed',
                         'I-decision_quashed',
                         'B-defendant_gender',
                         'B-victim_is_male',
                         'B-victim_is_female',                                
                         'B-victim_gender',
                         'B-previous_convictions',
                         'I-previous_convictions',
                         'I-s_age',
                     ]
                 )
             )
             
            }
            
        ),
        supervised_keys=None,
        homepage="http://linkedin",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    #_TEST_FILE_URL
    #_DEV_FILE_URL
    #_TRAINING_FILE_URL
    urls_to_download = {
        "train": f"{_URL}{_TRAINING_FILE_URL}",
        "dev": f"{_URL}{_DEV_FILE_URL}",
        "test": f"{_URL}{_TEST_FILE_URL}",
    }
    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:
      current_tokens = []
      current_labels = []
      sentence_counter = 0

      for row in f:
        row = row.rstrip()
        if row:
          token, label = row.split("\t")
          current_tokens.append(token)
          current_labels.append(label)
        else:
          # New sentence
          if not current_tokens:
            # Consecutive empty lines will cause empty sentences
            continue
          assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
          sentence = (
              sentence_counter,
              {
                  "id": str(sentence_counter),
                  "tokens": current_tokens,
                  "ner_tags": current_labels,
              },
          )
          sentence_counter += 1
          current_tokens = []
          current_labels = []
          yield sentence
          
    # Don't forget last sentence in dataset 🧐
    if current_tokens:
      yield sentence_counter, {
          "id": str(sentence_counter),
          "tokens": current_tokens,
          "ner_tags": current_labels,
      }