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fix tests

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thomwolf
2019-09-26 01:59:53 +02:00
parent d6dde438ea
commit 7c9f8f93f9
2 changed files with 35 additions and 29 deletions
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38
examples/run_tf_glue.py
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@@ -1,31 +1,37 @@
import tensorflow as tf
import tensorflow_datasets
from pytorch_transformers import BertTokenizer, BertForSequenceClassification, TFBertForSequenceClassification, glue_convert_examples_to_features
from transformers import *
# Load tokenizer, model, dataset
# Load dataset, tokenizer, model from pretrained model/vocabulary
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
tf_model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
dataset = tensorflow_datasets.load("glue/mrpc")
dataset = tensorflow_datasets.load('glue/mrpc')
model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
# Prepare dataset for GLUE
train_dataset = glue_convert_examples_to_features(dataset['train'], tokenizer, task='mrpc', max_length=128)
valid_dataset = glue_convert_examples_to_features(dataset['validation'], tokenizer, task='mrpc', max_length=128)
# Prepare dataset for GLUE as a tf.data.Dataset instance
train_dataset = glue_convert_examples_to_features(dataset['train'], tokenizer, task='mrpc')
valid_dataset = glue_convert_examples_to_features(dataset['validation'], tokenizer, task='mrpc')
train_dataset = train_dataset.shuffle(100).batch(32).repeat(3)
valid_dataset = valid_dataset.batch(64)
# Compile tf.keras model for training
# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
learning_rate = tf.keras.optimizers.schedules.PolynomialDecay(2e-5, 345, end_learning_rate=0)
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
tf_model.compile(optimizer=optimizer, loss=loss, metrics=['sparse_categorical_accuracy'])
model.compile(optimizer=optimizer, loss=loss, metrics=['sparse_categorical_accuracy'])
# Train and evaluate using tf.keras.Model.fit()
tf_model.fit(train_dataset, epochs=3, steps_per_epoch=115, validation_data=valid_dataset, validation_steps=7)
model.fit(train_dataset, epochs=3, steps_per_epoch=115,
validation_data=valid_dataset, validation_steps=7)
# Save the model and load it in PyTorch
tf_model.save_pretrained('./runs/')
pt_model = BertForSequenceClassification.from_pretrained('./runs/', from_tf=True)
# Save the TensorFlow model and load it in PyTorch
model.save_pretrained('./save/')
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
# Quickly inspect a few predictions
inputs = tokenizer.encode_plus("I said the company is doing great", "The company has good results", add_special_tokens=True, return_tensors='pt')
pred = pt_model(torch.tensor(tokens))
# Quickly inspect a few predictions - MRPC is a paraphrasing task
inputs = tokenizer.encode_plus("The company is doing great",
"The company has good results",
add_special_tokens=True,
return_tensors='pt')
pred = pytorch_model(**inputs)
print("Paraphrase" if pred.argmax().item() == 0 else "Not paraphrase")