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64 lines
3.0 KiB
Python
64 lines
3.0 KiB
Python
import os
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import tensorflow as tf
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import tensorflow_datasets
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from transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features, BertForSequenceClassification
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# script parameters
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BATCH_SIZE = 32
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EVAL_BATCH_SIZE = BATCH_SIZE * 2
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USE_XLA = False
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USE_AMP = False
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tf.config.optimizer.set_jit(USE_XLA)
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tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
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# Load tokenizer and model from pretrained model/vocabulary
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tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
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model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
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# Load dataset via TensorFlow Datasets
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data, info = tensorflow_datasets.load('glue/mrpc', with_info=True)
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train_examples = info.splits['train'].num_examples
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valid_examples = info.splits['validation'].num_examples
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# Prepare dataset for GLUE as a tf.data.Dataset instance
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train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, 'mrpc')
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valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, 'mrpc')
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train_dataset = train_dataset.shuffle(128).batch(BATCH_SIZE).repeat(-1)
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valid_dataset = valid_dataset.batch(EVAL_BATCH_SIZE)
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# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
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opt = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08)
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if USE_AMP:
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# loss scaling is currently required when using mixed precision
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opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, 'dynamic')
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
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model.compile(optimizer=opt, loss=loss, metrics=[metric])
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# Train and evaluate using tf.keras.Model.fit()
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train_steps = train_examples//BATCH_SIZE
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valid_steps = valid_examples//EVAL_BATCH_SIZE
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history = model.fit(train_dataset, epochs=2, steps_per_epoch=train_steps,
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validation_data=valid_dataset, validation_steps=valid_steps)
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# Save TF2 model
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os.makedirs('./save/', exist_ok=True)
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model.save_pretrained('./save/')
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# Load the TensorFlow model in PyTorch for inspection
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pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
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# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
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sentence_0 = 'This research was consistent with his findings.'
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sentence_1 = 'His findings were compatible with this research.'
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sentence_2 = 'His findings were not compatible with this research.'
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inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
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inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
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pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
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pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
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print('sentence_1 is', 'a paraphrase' if pred_1 else 'not a paraphrase', 'of sentence_0')
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print('sentence_2 is', 'a paraphrase' if pred_2 else 'not a paraphrase', 'of sentence_0')
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