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more details in GPT-2 usage example

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Thomas Wolf 2019-02-18 14:37:41 +01:00 committed by GitHub
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@ -400,12 +400,15 @@ logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# Encode input
text = "Who was Jim Henson ? Jim Henson was a puppeteer"
indexed_tokens = tokenizer.encode(text)
# Encode some inputs
text_1 = "Who was Jim Henson ?"
text_2 = "Jim Henson was a puppeteer"
indexed_tokens_1 = tokenizer.encode(text_1)
indexed_tokens_2 = tokenizer.encode(text_2)
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
tokens_tensor_1 = torch.tensor([indexed_tokens_1])
tokens_tensor_2 = torch.tensor([indexed_tokens_2])
```
Let's see how to use `GPT2Model` to get hidden states
@ -416,12 +419,16 @@ model = GPT2Model.from_pretrained('gpt2')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
tokens_tensor_1 = tokens_tensor_1.to('cuda')
tokens_tensor_2 = tokens_tensor_2.to('cuda')
model.to('cuda')
# Predict hidden states features for each layer
with torch.no_grad():
hidden_states = model(tokens_tensor)
hidden_states_1, past = model(tokens_tensor_1)
# past can be used to reuse precomputed hidden state in a subsequent predictions
# (see beam-search examples in the run_gpt2.py example
hidden_states-2, past = model(tokens_tensor_2, past=past)
```
And how to use `GPT2LMHeadModel`
@ -432,15 +439,18 @@ model = GPT2LMHeadModel.from_pretrained('gpt2')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
tokens_tensor_1 = tokens_tensor.to('cuda')
model.to('cuda')
# Predict all tokens
with torch.no_grad():
predictions = model(tokens_tensor)
predictions_1, past = model(tokens_tensor_1)
# past can be used to reuse precomputed hidden state in a subsequent predictions
# (see beam-search examples in the run_gpt2.py example
predictions_2, past = model(tokens_tensor_2, past=past)
# get the predicted last token
predicted_index = torch.argmax(predictions[0, -1, :]).item()
predicted_index = torch.argmax(predictions_2[0, -1, :]).item()
predicted_token = tokenizer.decode([predicted_index])
```