Zeb commited on
Commit
eeb99b3
1 Parent(s): 6ee996c

Update tagging and subconfigs

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Files changed (50) hide show
  1. BabyLM.py +37 -39
  2. en-ptb.map +68 -0
  3. tag_data.py +26 -12
  4. tagged/100M/aochildes.txt +0 -3
  5. tagged/100M/bnc_spoken.txt +0 -3
  6. tagged/100M/cbt.txt +0 -3
  7. tagged/100M/children_stories.txt +0 -3
  8. tagged/100M/gutenberg.txt +0 -3
  9. tagged/100M/open_subtitles.txt +0 -3
  10. tagged/100M/qed.txt +0 -3
  11. tagged/100M/simple_wikipedia.txt +0 -3
  12. tagged/100M/switchboard.txt +0 -3
  13. tagged/100M/wikipedia.txt +0 -3
  14. tagged/10M/aochildes.txt +0 -0
  15. tagged/10M/bnc_spoken.txt +0 -3
  16. tagged/10M/cbt.txt +0 -3
  17. tagged/10M/children_stories.txt +0 -0
  18. tagged/10M/gutenberg.txt +0 -3
  19. tagged/10M/open_subtitles.txt +0 -3
  20. tagged/10M/qed.txt +0 -3
  21. tagged/10M/simple_wikipedia.txt +0 -3
  22. tagged/10M/switchboard.txt +0 -0
  23. tagged/10M/wikipedia.txt +0 -3
  24. tagged/dev/aochildes.txt +0 -0
  25. tagged/dev/bnc_spoken.txt +0 -3
  26. tagged/dev/cbt.txt +0 -0
  27. tagged/dev/children_stories.txt +0 -0
  28. tagged/dev/gutenberg.txt +0 -3
  29. tagged/dev/open_subtitles.txt +0 -3
  30. tagged/dev/qed.txt +0 -3
  31. tagged/dev/simple_wikipedia.txt +0 -3
  32. tagged/dev/switchboard.txt +0 -0
  33. tagged/dev/wikipedia.txt +0 -3
  34. tagged/test/aochildes.txt +0 -0
  35. tagged/test/bnc_spoken.txt +0 -3
  36. tagged/test/cbt.txt +0 -0
  37. tagged/test/children_stories.txt +0 -0
  38. tagged/test/gutenberg.txt +0 -3
  39. tagged/test/open_subtitles.txt +0 -3
  40. tagged/test/qed.txt +0 -3
  41. tagged/test/simple_wikipedia.txt +0 -3
  42. tagged/test/switchboard.txt +0 -0
  43. tagged/test/wikipedia.txt +0 -3
  44. tagged_gold/100M/aochildes.txt +0 -3
  45. tagged_gold/100M/bnc_spoken.txt +0 -3
  46. tagged_gold/100M/cbt.txt +0 -3
  47. tagged_gold/100M/children_stories.txt +0 -3
  48. tagged_gold/100M/gutenberg.txt +0 -3
  49. tagged_gold/100M/open_subtitles.txt +0 -3
  50. tagged_gold/100M/qed.txt +0 -3
BabyLM.py CHANGED
@@ -22,40 +22,49 @@ filenames = [
22
  "switchboard.txt",
23
  "wikipedia.txt"
24
  ]
25
-
26
  class BabyLM(datasets.GeneratorBasedBuilder):
27
 
28
  BUILDER_CONFIGS = [
29
  datasets.BuilderConfig(
30
  name="original_strict_small",
31
- description="Small version of the dataset with 10M words, not cleaned",
32
  version="1.0.0",
33
  ),
34
  datasets.BuilderConfig(
35
  name="strict_small",
36
- description="Small version of the dataset with 10M words, cleaned",
37
  version="1.0.0",
38
  ),
39
  datasets.BuilderConfig(
40
  name="original_strict",
41
- description="Full version of the dataset with 10M words, not cleaned",
42
  version="1.0.0",
43
  ),
44
  datasets.BuilderConfig(
45
  name="strict",
46
- description="Full version of the dataset with 100M words, cleaned",
 
 
 
 
 
47
  version="1.0.0",
48
  ),
49
  datasets.BuilderConfig(
50
  name="strict_small_gold",
51
- description="Small version of the dataset with 10M words and gold POS tags",
 
 
 
 
 
52
  version="1.0.0",
53
  ),
54
  datasets.BuilderConfig(
55
  name="strict_gold",
56
- description="Full version of the dataset with 100M words and gold POS tags",
57
  version="1.0.0",
58
- )
59
  ]
60
 
61
  DEFAULT_CONFIG_NAME = "strict_small"
@@ -85,13 +94,10 @@ class BabyLM(datasets.GeneratorBasedBuilder):
85
  train_data_dir = "10M"
86
  else:
87
  train_data_dir = "100M"
88
- if 'gold' in self.config.name:
89
- folder = 'tagged_gold'
90
- elif 'original' in self.config.name:
91
- folder = 'original'
92
- else:
93
- folder = 'tagged'
94
 
 
 
 
95
  urls_to_download = {
96
  "train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames],
97
  "dev": [f"{folder}/dev/{fn}" for fn in filenames],
@@ -132,28 +138,20 @@ class BabyLM(datasets.GeneratorBasedBuilder):
132
 
133
  global_idx = 0
134
 
135
- if 'original' in self.config.name:
136
- for filepath in filepaths:
137
- with open(filepath, encoding="utf-8") as f:
138
- for line in f:
139
- yield global_idx, {"text": line.strip(), "filename": '', 'tagged_text': line.strip()}
140
- global_idx += 1
141
-
142
- else:
143
- for filepath in filepaths:
144
- with open(filepath, encoding="utf-8") as f:
145
- is_tags = False
146
- text = ""
147
- filename = ""
148
- # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching)
149
- for row in f:
150
- if filename == "":
151
- filename = row.strip()
152
- continue
153
- if is_tags:
154
- yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename}
155
- global_idx += 1
156
- is_tags = False
157
- else:
158
- text = row
159
- is_tags = True
 
22
  "switchboard.txt",
23
  "wikipedia.txt"
24
  ]
 
25
  class BabyLM(datasets.GeneratorBasedBuilder):
26
 
27
  BUILDER_CONFIGS = [
28
  datasets.BuilderConfig(
29
  name="original_strict_small",
30
+ description="Original dataset, 10M words, no POS tags",
31
  version="1.0.0",
32
  ),
33
  datasets.BuilderConfig(
34
  name="strict_small",
35
+ description="Cleaned version of the dataset, 10M words, unsupervised POS tags",
36
  version="1.0.0",
37
  ),
38
  datasets.BuilderConfig(
39
  name="original_strict",
40
+ description="Original dataset, 100M words, no POS tags",
41
  version="1.0.0",
42
  ),
43
  datasets.BuilderConfig(
44
  name="strict",
45
+ description="Cleaned version of the dataset, 100M words, unsupervised POS tags",
46
+ version="1.0.0",
47
+ ),
48
+ datasets.BuilderConfig(
49
+ name="original_strict_small_gold",
50
+ description="Original dataset, 10M words, gold POS tags",
51
  version="1.0.0",
52
  ),
53
  datasets.BuilderConfig(
54
  name="strict_small_gold",
55
+ description="Cleaned version of the dataset, 10M words, gold POS tags",
56
+ version="1.0.0",
57
+ ),
58
+ datasets.BuilderConfig(
59
+ name="original_strict_gold",
60
+ description="Original dataset, 100M words, gold POS tags",
61
  version="1.0.0",
62
  ),
63
  datasets.BuilderConfig(
64
  name="strict_gold",
65
+ description="Cleaned version of the dataset, 100M words, gold POS tags",
66
  version="1.0.0",
67
+ ),
68
  ]
69
 
70
  DEFAULT_CONFIG_NAME = "strict_small"
 
94
  train_data_dir = "10M"
95
  else:
96
  train_data_dir = "100M"
 
 
 
 
 
 
97
 
98
+ folder = 'original_tagged' if 'original' in self.config.name else 'clean_tagged'
99
+ folder = folder + '_gold' if 'gold' in self.config.name else folder
100
+
101
  urls_to_download = {
102
  "train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames],
103
  "dev": [f"{folder}/dev/{fn}" for fn in filenames],
 
138
 
139
  global_idx = 0
140
 
141
+ for filepath in filepaths:
142
+ with open(filepath, encoding="utf-8") as f:
143
+ is_tags = False
144
+ text = ""
145
+ filename = ""
146
+ # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching)
147
+ for row in f:
148
+ if filename == "":
149
+ filename = row.strip()
150
+ continue
151
+ if is_tags:
152
+ yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename}
153
+ global_idx += 1
154
+ is_tags = False
155
+ else:
156
+ text = row
157
+ is_tags = True
 
 
 
 
 
 
 
 
en-ptb.map ADDED
@@ -0,0 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ! .
2
+ # .
3
+ $ .
4
+ '' .
5
+ ( .
6
+ ) .
7
+ , .
8
+ -LRB- .
9
+ -RRB- .
10
+ . .
11
+ : .
12
+ ? .
13
+ CC CONJ
14
+ CD NUM
15
+ CD|RB X
16
+ DT DET
17
+ EX DET
18
+ FW X
19
+ IN ADP
20
+ IN|RP ADP
21
+ JJ ADJ
22
+ JJR ADJ
23
+ JJRJR ADJ
24
+ JJS ADJ
25
+ JJ|RB ADJ
26
+ JJ|VBG ADJ
27
+ LS X
28
+ MD VERB
29
+ NN NOUN
30
+ NNP NOUN
31
+ NNPS NOUN
32
+ NNS NOUN
33
+ NN|NNS NOUN
34
+ NN|SYM NOUN
35
+ NN|VBG NOUN
36
+ NP NOUN
37
+ PDT DET
38
+ POS PRT
39
+ PRP PRON
40
+ PRP$ PRON
41
+ PRP|VBP PRON
42
+ PRT PRT
43
+ RB ADV
44
+ RBR ADV
45
+ RBS ADV
46
+ RB|RP ADV
47
+ RB|VBG ADV
48
+ RN X
49
+ RP PRT
50
+ SYM X
51
+ TO PRT
52
+ UH X
53
+ VB VERB
54
+ VBD VERB
55
+ VBD|VBN VERB
56
+ VBG VERB
57
+ VBG|NN VERB
58
+ VBN VERB
59
+ VBP VERB
60
+ VBP|TO VERB
61
+ VBZ VERB
62
+ VP VERB
63
+ WDT DET
64
+ WH X
65
+ WP PRON
66
+ WP$ PRON
67
+ WRB ADV
68
+ `` .
tag_data.py CHANGED
@@ -6,6 +6,8 @@ from transformers import AutoTokenizer
6
 
7
  import nltk, sys
8
 
 
 
9
  UNSUPERVISED_POS_TAG_MAP = {
10
  "and" : 'CONJ',
11
  "|" : 'NOUN',
@@ -103,23 +105,26 @@ def write_tagged_lines(filename, text, tagged_lines):
103
  f.write(line)
104
  f.write(' '.join([f'{token}__<label>__{tag}' for token, tag in tagged]) + '\n')
105
 
106
- tokenizer = AutoTokenizer.from_pretrained("CamBabyTrainers/CamBabyTokenizer-8192")
107
 
108
  FOLDERS = ['10M', '100M', 'dev', 'test']
 
 
109
 
110
  if __name__ == "__main__":
111
 
 
 
112
  # Read all text files from directory "BabyLM"
113
  all_files = []
114
  for folder in FOLDERS:
115
- for root, dirs, files in os.walk(f"clean/{folder}"):
116
  for file in files:
117
  if file.endswith(".txt"):
118
  all_files.append(os.path.join(root, file))
119
 
120
  # Get map from PTB tags to universal tags
121
  en_ptb_map = {}
122
- with open('../pos_tagging/en-ptb.map', 'r') as f:
123
  for line in f.readlines():
124
  (key, val) = line.split()
125
  en_ptb_map[key] = val
@@ -128,28 +133,37 @@ if __name__ == "__main__":
128
  print(file)
129
  with open(file, 'r') as f:
130
  lines = f.readlines()[1:]
 
 
 
 
131
 
132
  # 1. Tokenize the lines in the text, tag with universal tags and write to tmp file
133
  tokenized = tokenize_lines(lines, tokenizer)
134
  tagged = tag_with_nltk(tokenized, en_ptb_map)
135
- write_to_file(tagged, 'tmp.txt')
 
 
 
 
 
 
136
 
137
  # 2. Run the unsupervised tagger on the tmp file
 
138
  os.system(f'./../anchor/hmm --output ../pos_tagging/10M_train_30_extended --data tmp.txt --pred tmp_tagged.txt')
139
 
140
  # 3. Get the gold tags and predicted tags
141
  gold_tagged_lines, pred_tagged_lines = get_tags_from_file('tmp_tagged.txt')
 
 
142
 
143
  assert len(gold_tagged_lines) == len(pred_tagged_lines) == len(lines)
144
 
145
  # 4. Write the tagged lines to the original file
146
- new_file = file.replace('clean', 'tagged')
147
- os.makedirs(os.path.dirname(new_file), exist_ok=True)
148
- write_tagged_lines(new_file, lines, pred_tagged_lines)
 
149
 
150
- new_file = file.replace('clean', 'tagged_gold')
151
- os.makedirs(os.path.dirname(new_file), exist_ok=True)
152
- write_tagged_lines(new_file, lines, gold_tagged_lines)
153
 
154
- os.remove('tmp.txt')
155
- os.remove('tmp_tagged.txt')
 
6
 
7
  import nltk, sys
8
 
9
+ TOKENIZER_NAME = 'cambridge-climb/CamBabyTokenizer-8192'
10
+
11
  UNSUPERVISED_POS_TAG_MAP = {
12
  "and" : 'CONJ',
13
  "|" : 'NOUN',
 
105
  f.write(line)
106
  f.write(' '.join([f'{token}__<label>__{tag}' for token, tag in tagged]) + '\n')
107
 
 
108
 
109
  FOLDERS = ['10M', '100M', 'dev', 'test']
110
+ SECTION = "original"
111
+ RUN_UNSUPERVISED_TAGGER = True
112
 
113
  if __name__ == "__main__":
114
 
115
+ tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
116
+
117
  # Read all text files from directory "BabyLM"
118
  all_files = []
119
  for folder in FOLDERS:
120
+ for root, dirs, files in os.walk(f"{SECTION}/{folder}"):
121
  for file in files:
122
  if file.endswith(".txt"):
123
  all_files.append(os.path.join(root, file))
124
 
125
  # Get map from PTB tags to universal tags
126
  en_ptb_map = {}
127
+ with open('en-ptb.map', 'r') as f:
128
  for line in f.readlines():
129
  (key, val) = line.split()
130
  en_ptb_map[key] = val
 
133
  print(file)
134
  with open(file, 'r') as f:
135
  lines = f.readlines()[1:]
136
+ lines = [line.strip()+'\n' for line in lines if line.strip() != '']
137
+
138
+ tagged_file = file.replace(f'{SECTION}', f'{SECTION}_tagged')
139
+ gold_tagged_file = file.replace(f'{SECTION}', f'{SECTION}_tagged_gold')
140
 
141
  # 1. Tokenize the lines in the text, tag with universal tags and write to tmp file
142
  tokenized = tokenize_lines(lines, tokenizer)
143
  tagged = tag_with_nltk(tokenized, en_ptb_map)
144
+
145
+ if not RUN_UNSUPERVISED_TAGGER:
146
+ # Save the gold tags
147
+ gold_tagged_lines = tagged
148
+ os.makedirs(os.path.dirname(gold_tagged_file), exist_ok=True)
149
+ write_tagged_lines(gold_tagged_file, lines, tagged)
150
+ continue
151
 
152
  # 2. Run the unsupervised tagger on the tmp file
153
+ write_to_file(tagged, 'tmp.txt')
154
  os.system(f'./../anchor/hmm --output ../pos_tagging/10M_train_30_extended --data tmp.txt --pred tmp_tagged.txt')
155
 
156
  # 3. Get the gold tags and predicted tags
157
  gold_tagged_lines, pred_tagged_lines = get_tags_from_file('tmp_tagged.txt')
158
+ os.remove('tmp.txt')
159
+ os.remove('tmp_tagged.txt')
160
 
161
  assert len(gold_tagged_lines) == len(pred_tagged_lines) == len(lines)
162
 
163
  # 4. Write the tagged lines to the original file
164
+ os.makedirs(os.path.dirname(tagged_file), exist_ok=True)
165
+ write_tagged_lines(tagged_file, lines, pred_tagged_lines)
166
+ os.makedirs(os.path.dirname(gold_tagged_file), exist_ok=True)
167
+ write_tagged_lines(gold_tagged_file, lines, gold_tagged_lines)
168
 
 
 
 
169
 
 
 
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tagged/dev/aochildes.txt DELETED
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