Minimal code to train ELMo models in TensorFlow.
Heavily based on https://github.com/allenai/bilm-tf .
Most changes are simplifications and updating the code to the recent versions of TensorFlow 1. See also our repository with simple code to infer contextualized word vectors from pre-trained ELMo models.
python3 bilm/train_elmo.py --train_prefix $DATA --size $SIZE --vocab_file $VOCAB --save_dir $OUT
where
$DATA is a path to the directory containing 2 or more of (possibly gzipped) plain text files: your training corpus.
$SIZE if the number of word tokens in $DATA (necessary to properly construct and log batches).
$VOCAB is a (possibly gzipped) one-word-per-line vocabulary file to be used for language modeling; it should always contain at least , and .
$OUT is a directory where the TensorFlow checkpoints will be saved.
Before training, please review the settings in bilm/train_elmo.py. The most important are:
- batch_size (default 128)
- n_gpus (default 2; if no GPU, all available CPU cores are used)
- LSTM dimensionality (default 2048; the original paper used 4096)
- n_epochs (default 3; optimal value depends on the size of your corpus)
- n_negative_samples_batch (default 4096; the original paper used 8192)
After the training, use the bilm/dump_weights.py script to convert the checkpoints to and HDF5 model.
python3 bilm/dump_weights.py --save_dir $MODEL_DIR --outfile $MODEL_DIR/model.hdf5
Save your vocabulary file in the same directory.
Change the n_characters value in the options.json file from 261 to 262 to use the saved model for inference.
More details at https://github.com/allenai/bilm-tf
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent