simple-elmo 0.4.0

Handy library to work with pre-trained ELMo embeddings in TensorFlow

Simple_elmo is a Python library to work with pre-trained ELMo embeddings in TensorFlow.

This is a significantly updated wrapper to the original ELMo implementation. The main changes are:

• more convenient and transparent data loading (including from compressed files)
• code adapted to modern TensorFlow versions (including TensorFlow 2).

Installation

pip install --upgrade simple_elmo

Make sure to update the package regularly, we are actively developing.

Usage

from simple_elmo import ElmoModel

model = ElmoModel()

Loading

First, let's load a pretrained model from disk:

model.load(PATH_TO_ELMO)

Required arguments

PATH_TO_ELMO is either a ZIP archive downloaded from the NLPL vector repository, OR a directory containing 2 files (for example, downloaded from AllenNLP):

• model.hdf5, pre-trained ELMo weights in HDF5 format;
• options.json, description of the model architecture in JSON;

Optionally, one can also provide vocab.txt/vocab.txt.gz: a one-word-per-line vocabulary of words to be cached (as character id representations) before inference. Even if it is not present at all, ELMo will still process all words normally. However, providing the vocabulary file can slightly increase inference speed when working with very large corpora (by reducing the amount of word to char ids conversions).

Optional arguments

• top: bool, default False if this parameter is set to True, only the top (last) layer of the model will be used; otherwise, the average of all 3 layers is produced.
• max_batch_size: integer, default 96 the maximum number of sentences/documents in a batch during inference; your input will be automatically split into chunks of the respective size; if your computational resources allow, you might want to increase this value.
• limit: integer, default 100 the number of words from the vocabulary file to actually cache (counted from the first line). Increase the default value if you are sure these words occur in your training data much more often than 1 or 2 times.

Working with models

Currently, we provide two methods for loaded models (will be expanded in the future):

• model.get_elmo_vectors(SENTENCES)

• model.get_elmo_vector_average(SENTENCES)

SENTENCES is a list of input sentences (lists of words).

The get_elmo_vectors() method produces a tensor of contextualized word embeddings. Its shape is (number of sentences, the length of the longest sentence, ELMo dimensionality).

The get_elmo_vector_average() method produces a tensor with one vector per each input sentence, constructed by averaging individual contextualized word embeddings. Its shape is (number of sentences, ELMo dimensionality).

Use these tensors for your downstream tasks.

Example scripts

We provide two example scripts to make it easier to start using simple_elmo right away:

• Inferring token embeddings

python3 get_elmo_vectors.py -i test.txt -e ~/PATH_TO_ELMO/

• Text pairs classification

python3 text_classification.py -i paraphrases_lemm.tsv.gz -e ~/PATH_TO_ELMO/

The second script can be used to perform document pair classification (like in text entailment or paraphrase detection). Simple average of ELMo embeddings for all words in a document is used; then, the cosine similarity between two documents is calculated and used as a classifier feature. Evaluated with macro F1 score and 10-fold cross-validation.

Example paraphrase dataset for English (adapted from MRPC):

• https://rusvectores.org/static/testsets/mrpc.tsv.gz

Example paraphrase datasets for Russian (adapted from http://paraphraser.ru/):

• https://rusvectores.org/static/testsets/paraphrases.tsv.gz
• https://rusvectores.org/static/testsets/paraphrases_lemm.tsv.gz (lemmatized)

Training your own ELMo

Currently we provide ELMo training code (updated and improved in the same way compared to the original implementation) in a separate repository. It will be integrated into the simple_elmo package in the nearest future.