A Tensorflow implementation of Alexis Conneau's Supervised Learning of Universal Sentence Representations from Natural Language Inference Data from EMNLP 2017.
InferSent is a sentence embeddings method that provides semantic representations for English sentences. It is trained on natural language inference data and generalizes well to many different tasks.
- Save sentence embeddings permanently.
The dataset used for this task is Stanford Natural Language Inference (SNLI). Pretrained GloVe embeddings obtained from common crawl with 840B tokens used for words.
- Python>=3
- NumPy
- TensorFlow>=1.8
Download dataset from Stanford Natural Language Inference, then move snli_1.0_train.jsonl, snli_1.0_dev.jsonl, snli_1.0_test.jsonl into ./SNLI/raw data.
# move dataset to the right place
mkdir -p ./SNLI/raw\ data
mv snli_1.0_*.jsonl ./SNLI/raw\ data
Data preprocessing for convert source data into an easy-to-use format.
python3 Utils.pyDefault hyper-parameters have been stored in config file in the path of ./config/config.yaml.
Training model:
python3 Train.py