# SeqMatchSeq3

**Repository Path**: curtainwang/seq-match-seq3

## Basic Information

- **Project Name**: SeqMatchSeq3
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-12
- **Last Updated**: 2021-04-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SeqMatchSeq in Tensorflow

This is a tensorflow implementation of SeqMatchSeq model in [Learning Natural Language Inference with LSTM](https://arxiv.org/abs/1512.08849).  

![](README/arch.png)

## Environments

* Python 3.x
* TensorFlow 1.2.x

## Pre-trained Word Vectors

I use glove.6B shared by Jeffrey Pennington et al.. It can be found at [link](https://nlp.stanford.edu/projects/glove/).

## Data

The data used is Stanford Natural Language Inference (SNLI) corpus which can be downloaded at [link](https://nlp.stanford.edu/projects/snli/).

## Usage

abandoning useless word vectors

$ python customize_embedding.py --data_dir DATA_DIR --embedding_path EMBEDDING_PATH

training

$ python natural_language_inference.py --ARG=VALUE

evaluating

$ python natural_language_inference.py --forward_only=True --ARG=VALUE

visualizing

$ tensorboard --logdir=DIR

## Results

I achieved 81.7415% correct rate on dev set (\~3 epochs).