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These libraries aren't always installed in the virtual environment where isort is running. Declaring them properly avoids mixing these third-party imports with local imports.
109 lines
3.9 KiB
Python
109 lines
3.9 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 (
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BertConfig,
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BertForSequenceClassification,
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BertTokenizer,
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TFBertForSequenceClassification,
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glue_convert_examples_to_features,
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glue_processors,
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)
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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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EPOCHS = 3
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TASK = "mrpc"
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if TASK == "sst-2":
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TFDS_TASK = "sst2"
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elif TASK == "sts-b":
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TFDS_TASK = "stsb"
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else:
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TFDS_TASK = TASK
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num_labels = len(glue_processors[TASK]().get_labels())
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print(num_labels)
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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. Specify the number of labels to classify (2+: classification, 1: regression)
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config = BertConfig.from_pretrained("bert-base-cased", num_labels=num_labels)
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tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
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model = TFBertForSequenceClassification.from_pretrained("bert-base-cased", config=config)
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# Load dataset via TensorFlow Datasets
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data, info = tensorflow_datasets.load(f"glue/{TFDS_TASK}", with_info=True)
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train_examples = info.splits["train"].num_examples
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# MNLI expects either validation_matched or validation_mismatched
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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, TASK)
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# MNLI expects either validation_matched or validation_mismatched
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valid_dataset = glue_convert_examples_to_features(data["validation"], tokenizer, 128, TASK)
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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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if num_labels == 1:
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loss = tf.keras.losses.MeanSquaredError()
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else:
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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(
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train_dataset,
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epochs=EPOCHS,
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steps_per_epoch=train_steps,
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validation_data=valid_dataset,
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validation_steps=valid_steps,
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)
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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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if TASK == "mrpc":
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# Load the TensorFlow model in PyTorch for inspection
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# This is to demo the interoperability between the two frameworks, you don't have to
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# do this in real life (you can run the inference on the TF model).
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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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del inputs_1["special_tokens_mask"]
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del inputs_2["special_tokens_mask"]
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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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