Tweet2Vec

Please Cite: Soroush Vosoughi, Prashanth Vijayaraghavan and Deb Roy. (2016). Tweet2Vec: Learning Tweet Embeddings using Character-level CNN-LSTM Encoder-Decoder. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2016). Pisa, Italy. ##Requirements

numpy
Theano
joblib

##Training: Create subdirectories to store logs and models. However you can specify your own dir from command line: logs/ all_data/

Use the following command to see all the options & hyperparameters for training that can be specified from command line:

THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 PYTHONPATH=. python model/train.py -h

Using default parameters, training can be performed by : THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 PYTHONPATH=. python model/train.py -t -i ./train_file.json -m "new_model" -E 3

Training data format is provided in sample.json. However, the tweet1 & tweet2 need to be a modified version. It could either be replies to tweets which have similar meaning or just synonym modified version of the tweet.

NOTE: Training takes a long time! The cost is the total sum of the log probabilities across each batch, timestep and decoder. Note that the Cost will fluxuate a lot. You could experiment with other loss functions as in loss.py. It takes more than a week to get good vectors.

##Embeddings

Use the following command to see all the options & parameters for getting embeddings that can be specified from command line:

THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 PYTHONPATH=. python model/embeddings.py -h

Get embedding using the following command: THEANO_FLAGS=mode=FAST_RUN,device=gpu1,floatX=float32 PYTHONPATH=. python model/embeddings.py -R -i /res/tweets.json -m "new_model" -e 3 -o "/res/emb.npy"

surafelml/Tweet2Vec

Tweet2Vec