SUMIN/HuggingFace_transformer
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model in evaluation mode by default after from_pretrained

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thomwolf
2019-07-16 15:41:57 +02:00
parent f289e6cfe4
commit 4acaa65068
2 changed files with 7 additions and 10 deletions
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README.md
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@@ -102,10 +102,6 @@ Let's see how we can use `BertModel` to encode our inputs in hidden-states:
# Load pre-trained model (weights)
model = BertModel.from_pretrained('bert-base-uncased')
# Set the model in evaluation mode to desactivate the DropOut modules
# This is IMPORTANT to have reproductible results during evaluation!
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
segments_tensors = segments_tensors.to('cuda')
@@ -129,7 +125,6 @@ And how to use `BertForMaskedLM` to predict a masked token:
```python
# Load pre-trained model (weights)
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
@@ -178,10 +173,6 @@ Let's see how to use `GPT2LMHeadModel` to generate the next token following our
# Load pre-trained model (weights)
model = GPT2LMHeadModel.from_pretrained('gpt2')
# Set the model in evaluation mode to desactivate the DropOut modules
# This is IMPORTANT to have reproductible results during evaluation!
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor = tokens_tensor.to('cuda')
model.to('cuda')