cosecant-csc / bilm-tf

Tensorflow implementation of contextualized word representations from bi-directional language models

bilm-tf

Tensorflow implementation of the pretrained biLM used to compute ELMo representations from "Deep contextualized word representations".

We also have a pytorch implementation available in AllenNLP.

Installing

Install python version 3.5 or later, tensorflow version 1.2 and h5py:

pip install tensorflow-gpu==1.2 h5py
python setup.py install

Ensure the tests pass in your environment by running:

python -m unittest discover tests/

Download the pretrained model consisting of an options file and the weight file:

• options file
• weight file

Installing with Docker

To run the image, you must use nvidia-docker, because this repository requires GPUs.

sudo nvidia-docker run -t allenai/bilm-tf:acc2f81d97fb2edd5174c797ae8ca415501deab1-GPU

Usage overview

There are three ways to integrate ELMo representations into a downstream task, depending on your use case.

1. Compute representations on the fly from raw text using character input. This is the most general method and will handle any input text. It is also the most computationally expensive.
2. Precompute and cache the context independent token representations, then compute context dependent representations using the biLSTMs for input data. This method is less computationally expensive then #1, but is only applicable with a fixed, prescribed vocabulary.
3. Precompute the representations for your entire dataset and save to a file.

We have used all of these methods in the past for various use cases. #1 is necessary for evaluating at test time on unseen data (e.g. public SQuAD leaderboard). #2 is a good compromise for large datasets where the size of the file in #3 is unfeasible (SNLI, SQuAD). #3 is a good choice for smaller datasets or in cases where you'd like to use ELMo in other frameworks.

In all cases, the process roughly follows the same steps. First, create a Batcher (or TokenBatcher for #2) to translate tokenized strings to numpy arrays of character (or token) ids. Then, load the pretrained ELMo model (class BidirectionalLanguageModel). Finally, for steps #1 and #2 use weight_layers to compute the final ELMo representations. For #3, use BidirectionalLanguageModel to write all the intermediate layers to a file.

Shape conventions

Each tokenized sentence is a list of str, with a batch of sentences a list of tokenized sentences (List[List[str]]).

The Batcher packs these into a shape (n_sentences, max_sentence_length + 2, 50) numpy array of character ids, padding on the right with 0 ids for sentences less then the maximum length. The first and last tokens for each sentence are special begin and end of sentence ids added by the Batcher.

The input character id placeholder can be dimensioned (None, None, 50), with both the batch dimension (axis=0) and time dimension (axis=1) determined for each batch, up the the maximum batch size specified in the BidirectionalLanguageModel constructor.

After running inference with the batch, the return biLM embeddings are a numpy array with shape (n_sentences, 3, max_sentence_length, 1024), after removing the special begin/end tokens.

Vocabulary file

The Batcher takes a vocabulary file as input for efficency. This is a text file, with one token per line, separated by newlines (\n). Each token in the vocabulary is cached as the appropriate 50 character id sequence once. Since the model is completely character based, tokens not in the vocabulary file are handled appropriately at run time, with a slight decrease in run time. It is recommended to always include the special <S> and </S> tokens (case sensitive) in the vocabulary file.

ELMo with character input

See usage_character.py for a detailed usage example.

ELMo with pre-computed and cached context independent token representations

To speed up model inference with a fixed, specified vocabulary, it is possible to pre-compute the context independent token representations, write them to a file, and re-use them for inference. Note that we don't support falling back to character inputs for out-of-vocabulary words, so this should only be used when the biLM is used to compute embeddings for input with a fixed, defined vocabulary.

To use this option:

1. First create a vocabulary file with all of the unique tokens in your dataset and add the special <S> and </S> tokens.
2. Run dump_token_embeddings with the full model to write the token embeddings to a hdf5 file.
3. Use TokenBatcher (instead of Batcher) with your vocabulary file, and pass use_token_inputs=False and the name of the output file from step 2 to the BidirectonalLanguageModel constructor.

See usage_token.py for a detailed usage example.

Dumping biLM embeddings for an entire dataset to a single file.

To take this option, create a text file with your tokenized dataset. Each line is one tokenized sentence (whitespace separated). Then use dump_bilm_embeddings.

The output file is hdf5 format. Each sentence in the input data is stored as a dataset with key str(sentence_id) where sentence_id is the line number in the dataset file (indexed from 0). The embeddings for each sentence are a shape (3, n_tokens, 1024) array.

See usage_cached.py for a detailed example.