This is a sample implementation of the TEASPN server supporting neural auto-completion and paraphrasing.
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Build an image from the Dockerfile and pull images from the registry. Note that this may take a while (10-30 mins) depending on your environment.
docker-compose build docker-compose pull
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Download and build the Elasticsearch index. This also takes a while.
mkdir elasticsearch_indexes curl -sLJ --output elasticsearch_indexes/tatoeba_ja.index 'https://teaspn.s3.amazonaws.com/server/0.0.1/assets/tatoeba_ja.index' docker-compose up -d elasticsearch docker run --net=host --rm -ti -v $PWD/elasticsearch_indexes:/tmp taskrabbit/elasticsearch-dump --input=/tmp/tatoeba_ja.index --output=http://localhost:9200/tatoeba_ja docker-compose stop
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Set the PATH environment variable.
echo "export PATH='$PWD:$PATH'" >> ~/.bash_profile
- If you are using zsh, modify your
~/.zshenv
file instead of~/.bash_profile
.
- If you are using zsh, modify your
Powered by the spaCy dependency parser.
Head tokens with specific dependency (ROOT, nsubj, nsubjpass, and dobj in the CLEAR style tag set) relation are highlighted in different colors.
Powered by LanguageTool and its python wrapper
This implementation provides two types of completion:
- Suggesting the likely next phrases given the context using DistilGPT2 developed by HuggingFace.
- Suggesting a set of words consistent with the characters being typed.
This provides paraphrase suggestions for the selected text.
We built a paraphrase model trained on PARANMT-50M using fairseq.
Provide full-text search feature using Tatoeba and Elasticsearch. Currently, this only supports Japanese-to-English search.
This lets you jupm from a selected expression to its antecedent. Powered by NeuralCoref.
Show the definition of a hovered word using WordNet.