Comments (1)
Furthermore, it'd be helpful if the vocab could be generated on demand during training time. This means that we'd need to create a related embedding table that would grow during training time, as need be.
With such a system, we wouldn't need to initially load and tokenize the data.
In order to accomplish this, either the Embedding
or the TokenEnum
would need to also handle updating the optimizer and any related object that depends on model parameters.
from pytorch-nlp.
Related Issues (20)
- SpacyEncoder: TypeError: __init__() got an unexpected keyword argument 'language' HOT 2
- Could this be improved?
- Fix `fork_rng_wrap`
- handling large-scale datasets with distributed dataloaders for iterative datasets
- Make `check_files` more generic HOT 1
- MaxTokensBatchSampler HOT 1
- Wrong number of classes is derived from `label_encoder.vocab_size` HOT 5
- Gating for inputs
- wmt_dataset download failed HOT 2
- Error in SpacyEncoder when language argument is passed
- PackagesNotFoundError in anaconda
- torchnlp ERROR: No matching distribution found for torch==1.0.0
- SpacyWordSplitter: module not found - AllenNLP v1.5.0
- AttributeError: 'WeightDropGRU' object has no attribute '_flat_weights' HOT 2
- Inefficient embedding loading code in README.md HOT 3
- Fix simple typo: donwload -> download
- Why there is a huge result difference between Awd-Lstm Weight_Drop and this one HOT 1
- ONNX support for Encoders HOT 1
- OSError on second usage of FastText() HOT 1
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from pytorch-nlp.