mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-16 02:58:23 +06:00
56 lines
2.7 KiB
Python
56 lines
2.7 KiB
Python
import os
|
|
import tensorflow as tf
|
|
import tensorflow_datasets
|
|
from transformers import BertTokenizer, TFBertForSequenceClassification, glue_convert_examples_to_features, BertForSequenceClassification
|
|
|
|
# script parameters
|
|
BATCH_SIZE = 32
|
|
EVAL_BATCH_SIZE = BATCH_SIZE * 2
|
|
|
|
# Load tokenizer and model from pretrained model/vocabulary
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
|
model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
|
|
|
|
# Load dataset via TensorFlow Datasets
|
|
data, info = tensorflow_datasets.load('glue/mrpc', with_info=True)
|
|
train_examples = info.splits['train'].num_examples
|
|
valid_examples = info.splits['validation'].num_examples
|
|
|
|
# Prepare dataset for GLUE as a tf.data.Dataset instance
|
|
train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, 'mrpc')
|
|
valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, 'mrpc')
|
|
train_dataset = train_dataset.shuffle(128).batch(BATCH_SIZE).repeat(-1)
|
|
valid_dataset = valid_dataset.batch(EVAL_BATCH_SIZE)
|
|
|
|
# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
|
|
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08)
|
|
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
|
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
|
|
model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
|
|
|
|
# Train and evaluate using tf.keras.Model.fit()
|
|
train_steps = train_examples//BATCH_SIZE
|
|
valid_steps = valid_examples//EVAL_BATCH_SIZE
|
|
|
|
history = model.fit(train_dataset, epochs=2, steps_per_epoch=train_steps,
|
|
validation_data=valid_dataset, validation_steps=valid_steps)
|
|
|
|
# Save TF2 model
|
|
os.makedirs('./save/', exist_ok=True)
|
|
model.save_pretrained('./save/')
|
|
|
|
# Load the TensorFlow model in PyTorch for inspection
|
|
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
|
|
|
|
# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
|
|
sentence_0 = 'This research was consistent with his findings.'
|
|
sentence_1 = 'His findings were compatible with this research.'
|
|
sentence_2 = 'His findings were not compatible with this research.'
|
|
inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
|
|
inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
|
|
|
|
pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
|
|
pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
|
|
print('sentence_1 is', 'a paraphrase' if pred_1 else 'not a paraphrase', 'of sentence_0')
|
|
print('sentence_2 is', 'a paraphrase' if pred_2 else 'not a paraphrase', 'of sentence_0')
|