Update tagging and subconfigs

BabyLM.py

@@ -22,40 +22,49 @@ filenames = [
     "switchboard.txt",
     "wikipedia.txt"
 ]
-
 class BabyLM(datasets.GeneratorBasedBuilder):
     
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
             name="original_strict_small",
-            description="Small version of the dataset with 10M words, not cleaned",
+            description="Original dataset, 10M words, no POS tags",
             version="1.0.0",
         ),
         datasets.BuilderConfig(
             name="strict_small",
-            description="Small version of the dataset with 10M words, cleaned",
+            description="Cleaned version of the dataset, 10M words, unsupervised POS tags",
             version="1.0.0",
         ),
         datasets.BuilderConfig(
             name="original_strict",
-            description="Full version of the dataset with 10M words, not cleaned",
+            description="Original dataset, 100M words, no POS tags",
             version="1.0.0",
         ),
         datasets.BuilderConfig(
             name="strict",
-            description="Full version of the dataset with 100M words, cleaned",
+            description="Cleaned version of the dataset, 100M words, unsupervised POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="original_strict_small_gold",
+            description="Original dataset, 10M words, gold POS tags",
             version="1.0.0",
         ),
         datasets.BuilderConfig(
             name="strict_small_gold",
-            description="Small version of the dataset with 10M words and gold POS tags",
+            description="Cleaned version of the dataset, 10M words, gold POS tags",
+            version="1.0.0",
+        ),
+        datasets.BuilderConfig(
+            name="original_strict_gold",
+            description="Original dataset, 100M words, gold POS tags",
             version="1.0.0",
         ),
         datasets.BuilderConfig(
             name="strict_gold",
-            description="Full version of the dataset with 100M words and gold POS tags",
+            description="Cleaned version of the dataset, 100M words, gold POS tags",
             version="1.0.0",
-        )
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = "strict_small"
@@ -85,13 +94,10 @@ class BabyLM(datasets.GeneratorBasedBuilder):
             train_data_dir = "10M"
         else: 
             train_data_dir = "100M"
-        if 'gold' in self.config.name:
-            folder = 'tagged_gold'
-        elif 'original' in self.config.name:
-            folder = 'original'
-        else:
-            folder = 'tagged'
 
+        folder = 'original_tagged' if 'original' in self.config.name else 'clean_tagged'
+        folder = folder + '_gold' if 'gold' in self.config.name else folder
+        
         urls_to_download = {
             "train": [f"{folder}/{train_data_dir}/{fn}" for fn in filenames],
             "dev": [f"{folder}/dev/{fn}" for fn in filenames],
@@ -132,28 +138,20 @@ class BabyLM(datasets.GeneratorBasedBuilder):
         
         global_idx = 0 
 
-        if 'original' in self.config.name:
-            for filepath in filepaths:
-                with open(filepath, encoding="utf-8") as f:
-                    for line in f:
-                        yield global_idx, {"text": line.strip(), "filename": '', 'tagged_text': line.strip()}
-                        global_idx += 1
-
-        else:
-            for filepath in filepaths:
-                with open(filepath, encoding="utf-8") as f:
-                    is_tags = False
-                    text = ""
-                    filename = ""
-                    # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching)
-                    for row in f:
-                        if filename == "":
-                            filename = row.strip()
-                            continue
-                        if is_tags:
-                            yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename}
-                            global_idx += 1 
-                            is_tags = False
-                        else:
-                            text = row
-                            is_tags = True
+        for filepath in filepaths:
+            with open(filepath, encoding="utf-8") as f:
+                is_tags = False
+                text = ""
+                filename = ""
+                # Every other row contains POS tags. First row is the filename (we can't use filepath since the file path changes upon caching)
+                for row in f:
+                    if filename == "":
+                        filename = row.strip()
+                        continue
+                    if is_tags:
+                        yield global_idx, {"text": text.strip(), "tagged_text": row.strip(), "filename": filename}
+                        global_idx += 1 
+                        is_tags = False
+                    else:
+                        text = row
+                        is_tags = True

en-ptb.map

@@ -0,0 +1,68 @@
+!	.
+#	.
+$	.
+''	.
+(	.
+)	.
+,	.
+-LRB-	.
+-RRB-	.
+.	.
+:	.
+?	.
+CC	CONJ
+CD	NUM
+CD|RB	X
+DT	DET
+EX	DET
+FW	X
+IN	ADP
+IN|RP	ADP
+JJ	ADJ
+JJR	ADJ
+JJRJR	ADJ
+JJS	ADJ
+JJ|RB	ADJ
+JJ|VBG	ADJ
+LS	X
+MD	VERB
+NN	NOUN
+NNP	NOUN
+NNPS	NOUN
+NNS	NOUN
+NN|NNS	NOUN
+NN|SYM	NOUN
+NN|VBG	NOUN
+NP	NOUN
+PDT	DET
+POS	PRT
+PRP	PRON
+PRP$	PRON
+PRP|VBP	PRON
+PRT	PRT
+RB	ADV
+RBR	ADV
+RBS	ADV
+RB|RP	ADV
+RB|VBG	ADV
+RN	X
+RP	PRT
+SYM	X
+TO	PRT
+UH	X
+VB	VERB
+VBD	VERB
+VBD|VBN	VERB
+VBG	VERB
+VBG|NN	VERB
+VBN	VERB
+VBP	VERB
+VBP|TO	VERB
+VBZ	VERB
+VP	VERB
+WDT	DET
+WH	X
+WP	PRON
+WP$	PRON
+WRB	ADV
+``	.

tag_data.py

@@ -6,6 +6,8 @@ from transformers import AutoTokenizer
 
 import nltk, sys
 
+TOKENIZER_NAME = 'cambridge-climb/CamBabyTokenizer-8192'
+
 UNSUPERVISED_POS_TAG_MAP = {
    "and" : 'CONJ',
    "|" : 'NOUN',
@@ -103,23 +105,26 @@ def write_tagged_lines(filename, text, tagged_lines):
             f.write(line)
             f.write(' '.join([f'{token}__<label>__{tag}' for token, tag in tagged]) + '\n')
 
-tokenizer = AutoTokenizer.from_pretrained("CamBabyTrainers/CamBabyTokenizer-8192")
 
 FOLDERS = ['10M', '100M', 'dev', 'test']
+SECTION = "original"
+RUN_UNSUPERVISED_TAGGER = True
 
 if __name__ == "__main__":
 
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_NAME)
+
     # Read all text files from directory "BabyLM"
     all_files = []
     for folder in FOLDERS:
-        for root, dirs, files in os.walk(f"clean/{folder}"):
+        for root, dirs, files in os.walk(f"{SECTION}/{folder}"):
             for file in files:
                 if file.endswith(".txt"):
                     all_files.append(os.path.join(root, file))
 
     # Get map from PTB tags to universal tags
     en_ptb_map = {}
-    with open('../pos_tagging/en-ptb.map', 'r') as f:
+    with open('en-ptb.map', 'r') as f:
         for line in f.readlines():
             (key, val) = line.split()
             en_ptb_map[key] = val
@@ -128,28 +133,37 @@ if __name__ == "__main__":
         print(file)
         with open(file, 'r') as f:
             lines = f.readlines()[1:]
+            lines = [line.strip()+'\n' for line in lines if line.strip() != '']
+
+        tagged_file = file.replace(f'{SECTION}', f'{SECTION}_tagged')
+        gold_tagged_file = file.replace(f'{SECTION}', f'{SECTION}_tagged_gold')
 
         # 1. Tokenize the lines in the text, tag with universal tags and write to tmp file
         tokenized = tokenize_lines(lines, tokenizer)
         tagged = tag_with_nltk(tokenized, en_ptb_map)
-        write_to_file(tagged, 'tmp.txt')
+
+        if not RUN_UNSUPERVISED_TAGGER:
+            # Save the gold tags
+            gold_tagged_lines = tagged
+            os.makedirs(os.path.dirname(gold_tagged_file), exist_ok=True)
+            write_tagged_lines(gold_tagged_file, lines, tagged)
+            continue
 
         # 2. Run the unsupervised tagger on the tmp file
+        write_to_file(tagged, 'tmp.txt')
         os.system(f'./../anchor/hmm --output ../pos_tagging/10M_train_30_extended --data tmp.txt --pred tmp_tagged.txt')
 
         # 3. Get the gold tags and predicted tags
         gold_tagged_lines, pred_tagged_lines = get_tags_from_file('tmp_tagged.txt')
+        os.remove('tmp.txt')
+        os.remove('tmp_tagged.txt')
         
         assert len(gold_tagged_lines) == len(pred_tagged_lines) == len(lines)
 
         # 4. Write the tagged lines to the original file
-        new_file = file.replace('clean', 'tagged')
-        os.makedirs(os.path.dirname(new_file), exist_ok=True)
-        write_tagged_lines(new_file, lines, pred_tagged_lines)
+        os.makedirs(os.path.dirname(tagged_file), exist_ok=True)
+        write_tagged_lines(tagged_file, lines, pred_tagged_lines)
+        os.makedirs(os.path.dirname(gold_tagged_file), exist_ok=True)
+        write_tagged_lines(gold_tagged_file, lines, gold_tagged_lines)
 
-        new_file = file.replace('clean', 'tagged_gold')
-        os.makedirs(os.path.dirname(new_file), exist_ok=True)
-        write_tagged_lines(new_file, lines, gold_tagged_lines)
 
-        os.remove('tmp.txt')
-        os.remove('tmp_tagged.txt')

tagged/100M/aochildes.txt

@@ -1,3 +0,0 @@
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-size 99052359

tagged/100M/bnc_spoken.txt

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tagged/100M/cbt.txt

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-size 130181150

tagged/100M/children_stories.txt

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tagged/100M/gutenberg.txt

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-size 231906973

tagged/100M/open_subtitles.txt

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-size 896907196

tagged/100M/qed.txt

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-size 285761360

tagged/100M/simple_wikipedia.txt

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-size 416714254

tagged/100M/switchboard.txt

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tagged/100M/wikipedia.txt

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