This repository contains the code to vectorise contexts to use in models such as MTCue and LMCue.

To run the software, you need to install the following packages (preferably in an Anaconda environment or similar):

• sentence_transformers: pip install sentence-transformers

1. Clone the repo

   git clone https://github.com/st-vincent1/cxt2vec.git

2. Navigate to the repository

   cd cxt2vec

3. Run the embedding code:

   python main.py --path [path to data] --dest_path [path to output embeddings] --suffixes [comma-separated suffixes of data to embed] --model [context model]

For a data folder which looks like this:

data
 |- en-pl
     |- train.en
     |- train.pl
     |- valid.en
     |- valid.pl
     |- test.en
     |- test.pl
     |- context
         |- valid.speaker_gender.cxt
         |- valid.formality.cxt
         |- valid.writer_names.cxt
         |- ...
         |- valid.0.en
         |- valid.1.en
         |- ...
         |- test.0.en
         |- ...
         |- train.speaker_gender.cxt
         |- train.plot.cxt
         |- ...
 |- en-de
 |- ...

The following code

python main.py --paths data/en-pl --dest_path data/en-pl/embeddings --suffixes cxt,pl --model minilm

Finds all contexts at data/en-pl/context which match the given suffixes in order (cxt, then pl), embeds all found contexts into a single file per split (train/valid/test) per suffix. A .json file is also produced which contains metadata necessary to later interpret the files in training code.

The result will therefore look like so:

data
 |- en-pl
     |- embeddings
         |- train.minilm.cxt.bin
         |- train.minilm.cxt.idx
         |- train.minilm.en.bin
         |- train.minilm.en.idx
         |- train.minilm.json
         |- valid.minilm.cxt.bin
         |- valid.minilm.cxt.idx
         |- valid.minilm.en.bin
         |- valid.minilm.en.idx
         |- valid.minilm.json
         |- test.minilm.cxt.bin
         |- test.minilm.cxt.idx
         |- test.minilm.en.bin
         |- test.minilm.en.idx
         |- test.minilm.json

See examples in e.g. [github.com/st-vincent1/MTCue](MTCue repository) for how to use the embeddings to train models.

Distributed under the MIT License. See LICENSE.txt for more information.