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Datasets:
Kriyans
/
ner

Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K<n<10K
License:
Dataset card Files Community
4
Kriyans commited on
Commit
8a48f8c
•
1 Parent(s): 1de13a4

Update indian_names.py

Browse files
Files changed (1) hide show
  1. indian_names.py +82 -82
indian_names.py CHANGED
@@ -47,91 +47,91 @@ class indian_names(datasets.GeneratorBasedBuilder):
47
  supervised_keys=None,
48
  )
49
 
50
- def _split_generators(self, dl_manager):
51
- """Returns SplitGenerators."""
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- urls_to_download = {
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- "train": f"{_URL}",
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- }
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- downloaded_files = dl_manager.download_and_extract(urls_to_download)
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57
- return [
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- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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- ]
60
 
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- def _generate_examples(self, filepath):
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- logger.info("⏳ Generating examples from = %s", filepath)
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- with open(filepath, encoding="utf-8") as f:
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- current_tokens = []
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- current_labels = []
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- sentence_counter = 0
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- for row in f:
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- row = row.rstrip()
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- if row:
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- # Check if the delimiter ("\t") is present in the row
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- if "\t" in row:
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- token, label = row.split("\t")
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- current_tokens.append(token)
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- current_labels.append(label)
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- else:
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- # New sentence
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- if not current_tokens:
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- # Consecutive empty lines will cause empty sentences
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- continue
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- assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
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- sentence = (
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- sentence_counter,
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- {
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- "id": str(sentence_counter),
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- "tokens": current_tokens,
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- "ner_tags": current_labels,
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- },
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- )
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- sentence_counter += 1
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- current_tokens = []
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- current_labels = []
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- yield sentence
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- # Don't forget the last sentence in the dataset 🧐
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- if current_tokens:
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- yield sentence_counter, {
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- "id": str(sentence_counter),
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- "tokens": current_tokens,
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- "ner_tags": current_labels,
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- }
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-
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- # def _generate_examples(self, filepath):
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- # logger.info("⏳ Generating examples from = %s", filepath)
103
  # with open(filepath, encoding="utf-8") as f:
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- # current_tokens = []
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- # current_labels = []
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- # sentence_counter = 0
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- # for row in f:
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- # row = row.rstrip()
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- # if row:
 
 
110
  # token, label = row.split("\t")
111
  # current_tokens.append(token)
112
  # current_labels.append(label)
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- # else:
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- # # New sentence
115
- # if not current_tokens:
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- # # Consecutive empty lines will cause empty sentences
117
- # continue
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- # assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
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- # sentence = (
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- # sentence_counter,
121
- # {
122
- # "id": str(sentence_counter),
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- # "tokens": current_tokens,
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- # "ner_tags": current_labels,
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- # },
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- # )
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- # sentence_counter += 1
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- # current_tokens = []
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- # current_labels = []
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- # yield sentence
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- # # Don't forget last sentence in dataset 🧐
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- # if current_tokens:
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- # yield sentence_counter, {
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- # "id": str(sentence_counter),
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- # "tokens": current_tokens,
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- # "ner_tags": current_labels,
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- # }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
47
  supervised_keys=None,
48
  )
49
 
50
+ # def _split_generators(self, dl_manager):
51
+ # """Returns SplitGenerators."""
52
+ # urls_to_download = {
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+ # "train": f"{_URL}",
54
+ # }
55
+ # downloaded_files = dl_manager.download_and_extract(urls_to_download)
56
 
57
+ # return [
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+ # datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
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+ # ]
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61
+ # def _generate_examples(self, filepath):
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+ # logger.info("⏳ Generating examples from = %s", filepath)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
63
  # with open(filepath, encoding="utf-8") as f:
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+ # current_tokens = []
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+ # current_labels = []
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+ # sentence_counter = 0
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+ # for row in f:
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+ # row = row.rstrip()
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+ # if row:
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+ # # Check if the delimiter ("\t") is present in the row
71
+ # if "\t" in row:
72
  # token, label = row.split("\t")
73
  # current_tokens.append(token)
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  # current_labels.append(label)
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+ # else:
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+ # # New sentence
77
+ # if not current_tokens:
78
+ # # Consecutive empty lines will cause empty sentences
79
+ # continue
80
+ # assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
81
+ # sentence = (
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+ # sentence_counter,
83
+ # {
84
+ # "id": str(sentence_counter),
85
+ # "tokens": current_tokens,
86
+ # "ner_tags": current_labels,
87
+ # },
88
+ # )
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+ # sentence_counter += 1
90
+ # current_tokens = []
91
+ # current_labels = []
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+ # yield sentence
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+ # # Don't forget the last sentence in the dataset 🧐
94
+ # if current_tokens:
95
+ # yield sentence_counter, {
96
+ # "id": str(sentence_counter),
97
+ # "tokens": current_tokens,
98
+ # "ner_tags": current_labels,
99
+ # }
100
+
101
+ def _generate_examples(self, filepath):
102
+ logger.info("⏳ Generating examples from = %s", filepath)
103
+ with open(filepath, encoding="utf-8") as f:
104
+ current_tokens = []
105
+ current_labels = []
106
+ sentence_counter = 0
107
+ for row in f:
108
+ row = row.rstrip()
109
+ if row:
110
+ token, label = row.split("\t")
111
+ current_tokens.append(token)
112
+ current_labels.append(label)
113
+ else:
114
+ # New sentence
115
+ if not current_tokens:
116
+ # Consecutive empty lines will cause empty sentences
117
+ continue
118
+ assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
119
+ sentence = (
120
+ sentence_counter,
121
+ {
122
+ "id": str(sentence_counter),
123
+ "tokens": current_tokens,
124
+ "ner_tags": current_labels,
125
+ },
126
+ )
127
+ sentence_counter += 1
128
+ current_tokens = []
129
+ current_labels = []
130
+ yield sentence
131
+ # Don't forget last sentence in dataset 🧐
132
+ if current_tokens:
133
+ yield sentence_counter, {
134
+ "id": str(sentence_counter),
135
+ "tokens": current_tokens,
136
+ "ner_tags": current_labels,
137
+ }