THe origin of LLMs is BERT

.gitattributes

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+*json* filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text

0_google-reserach-bert/README.md

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+# Origin
+
+Based upon upstream bert from google-research github

0_google-reserach-bert/evaluate-v1.1.py

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+""" Official evaluation script for v1.1 of the SQuAD dataset. """
+from __future__ import print_function
+from collections import Counter
+import string
+import re
+import argparse
+import json
+import sys
+
+
+def normalize_answer(s):
+    """Lower text and remove punctuation, articles and extra whitespace."""
+    def remove_articles(text):
+        return re.sub(r'\b(a|an|the)\b', ' ', text)
+
+    def white_space_fix(text):
+        return ' '.join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return ''.join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+def exact_match_score(prediction, ground_truth):
+    return (normalize_answer(prediction) == normalize_answer(ground_truth))
+
+
+def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
+    scores_for_ground_truths = []
+    for ground_truth in ground_truths:
+        score = metric_fn(prediction, ground_truth)
+        scores_for_ground_truths.append(score)
+    return max(scores_for_ground_truths)
+
+
+def evaluate(dataset, predictions):
+    f1 = exact_match = total = 0
+    for article in dataset:
+        for paragraph in article['paragraphs']:
+            for qa in paragraph['qas']:
+                total += 1
+                if qa['id'] not in predictions:
+                    message = 'Unanswered question ' + qa['id'] + \
+                              ' will receive score 0.'
+                    print(message, file=sys.stderr)
+                    continue
+                ground_truths = list(map(lambda x: x['text'], qa['answers']))
+                prediction = predictions[qa['id']]
+                exact_match += metric_max_over_ground_truths(
+                    exact_match_score, prediction, ground_truths)
+                f1 += metric_max_over_ground_truths(
+                    f1_score, prediction, ground_truths)
+
+    exact_match = 100.0 * exact_match / total
+    f1 = 100.0 * f1 / total
+
+    return {'exact_match': exact_match, 'f1': f1}
+
+
+if __name__ == '__main__':
+    expected_version = '1.1'
+    parser = argparse.ArgumentParser(
+        description='Evaluation for SQuAD ' + expected_version)
+    parser.add_argument('dataset_file', help='Dataset file')
+    parser.add_argument('prediction_file', help='Prediction File')
+    args = parser.parse_args()
+    with open(args.dataset_file) as dataset_file:
+        dataset_json = json.load(dataset_file)
+        if (dataset_json['version'] != expected_version):
+            print('Evaluation expects v-' + expected_version +
+                  ', but got dataset with v-' + dataset_json['version'],
+                  file=sys.stderr)
+        dataset = dataset_json['data']
+    with open(args.prediction_file) as prediction_file:
+        predictions = json.load(prediction_file)
+    print(json.dumps(evaluate(dataset, predictions)))

0_google-reserach-bert/run.sh

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+export SQUAD_DIR=./squad_datasets
+
+python run_squad.py \
+  --bert_model bert-base-uncased \
+  --do_train \
+  --do_predict \
+  --train_file $SQUAD_DIR/train-v1.1.json \
+  --predict_file $SQUAD_DIR/dev-v1.1.json \
+  --train_batch_size 12 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2.0 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir $SQUAD_DIR/debug_squad/