Comments (2)
Are you using the CPU or GPU? The model uses a BiLSTM with a large number of hidden units and thus involves heavy matrix computation which are much faster on the GPU. On a GPU (e.g K40), encoding 20k sentences should take around 25 seconds.
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Please re-open if you still have the issue.
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Related Issues (20)
- RuntimeError: Expected object of backend CUDA but got backend CPU for argument #3 'index' HOT 2
- Error in "model.load_state_dict" : IncompatibleKeys HOT 6
- Which is better? this project vs Universal Sentence Encoder HOT 2
- Can not train from scratch? HOT 1
- link for the pre-trained models? HOT 2
- Decode sentence embedding to sentence HOT 1
- How can I train a model or modify the network to do it?
- adding train_nli back
- Error while loading the pretrained model HOT 1
- Usage on Google Collab HOT 2
- Same sentence, different encoding! HOT 1
- Can you save a model with vocab? HOT 1
- RuntimeError: [enforce fail at CPUAllocator.cpp:64] . DefaultCPUAllocator: can't allocate memory: you tried to allocate 24776638464 bytes. Error code 12 (Cannot allocate memory)
- Cuda by Default
- Does this work anymore? HOT 2
- Vector Concatenation Question
- InferSent for Spanish
- ModuleNotFoundError: No module named 'models' HOT 1
- Infersent for Indian language
- How we fine tune inferSent for calassification problem like sentiment analysisi?
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