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https://github.com/apcode/tensorflow_fasttext

Simple embedding based text classifier inspired by fastText, implemented in tensorflow
https://github.com/apcode/tensorflow_fasttext

fasttext language-identification tensorflow text-classifier

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Simple embedding based text classifier inspired by fastText, implemented in tensorflow

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README 

        
# FastText in Tensorflow



This project is based on the ideas in Facebook's [FastText](https://github.com/facebookresearch/fastText) but implemented in

Tensorflow. However, it is not an exact replica of fastText.



Classification is done by embedding each word, taking the mean

embedding over the full text and classifying that using a linear

classifier. The embedding is trained with the classifier.  You can

also specify to use 2+ character ngrams. These ngrams get hashed then

embedded in a similar manner to the orginal words. Note, ngrams make

training much slower but only make marginal improvements in

performance, at least in English.



I may implement skipgram and cbow training later. Or preloading

embedding tables.



<< Still WIP >>



You can use [Horovod](https://github.com/uber/horovod) to distribute

training across multiple GPUs, on one or multiple servers. See usage

section below.



## FastText Language Identification



I have added utilities to train a classifier to detect languages, as

described in [Fast and Accurate Language Identification using

FastText](https://fasttext.cc/blog/2017/10/02/blog-post.html)



See usage below. It basically works in the same way as default usage.



## Implemented:

- classification of text using word embeddings

- char ngrams, hashed to n bins

- training and prediction program

- serve models on tensorflow serving

- preprocess facebook format, or text input into tensorflow records



## Not Implemented:

- separate word vector training (though can export embeddings)

- heirarchical softmax.

- quantize models (supported by tensorflow, but I haven't tried it yet)



# Usage



The following are examples of how to use the applications. Get full help with

`--help` option on any of the programs.



To transform input data into tensorflow Example format:



    process_input.py --facebook_input=queries.txt --output_dir=. --ngrams=2,3,4



Or, using a text file with one example per line with an extra file for labels:



    process_input.py --text_input=queries.txt --labels=labels.txt --output_dir=.



To train a text classifier:



    classifier.py \

      --train_records=queries.tfrecords \

      --eval_records=queries.tfrecords \

      --label_file=labels.txt \

      --vocab_file=vocab.txt \

      --model_dir=model \

      --export_dir=model



To predict classifications for text, use a saved_model from

classifier. `classifier.py --export_dir` stores a saved model in a

numbered directory below `export_dir`. Pass this directory to the

following to use that model for predictions:



    predictor.py

      --saved_model=model/12345678

      --text="some text to classify"

      --signature_def=proba



To export the embedding layer you can export from predictor. Note,

this will only be the text embedding, not the ngram embeddings.



    predictor.py

      --saved_model=model/12345678

      --text="some text to classify"

      --signature_def=embedding



Use the provided script to train easily:



    train_classifier.sh path-to-data-directory



# Language Identification



To implement something similar to the method described in [Fast and

Accurate Language Identification using

FastText](https://fasttext.cc/blog/2017/10/02/blog-post.html) you need to download the data:



    lang_dataset.sh [datadir]



You can then process the training and validation data using

`process_input.py` and `classifier.py` as described above.



There is a utility script to do this for you:



    train_langdetect.sh datadir



It reaches about 96% accuracy using word embeddings and this increases to nearly 99% when 

adding `--ngrams=2,3,4`



# Distributed Training



You can run training across multiple GPUs either on one or multiple

servers. To do so you need to install MPI and

[Horovod](https://github.com/uber/horovod) then add the `--horovod`

option. It runs very close to the GPU multiple in terms of

performance. I.e. if you have 2 GPUs on your server, it should run

close to 2x the speed.



    NUM_GPUS=2

    mpirun -np $NUM_GPUS python classifier.py \

      --horovod \

      --train_records=queries.tfrecords \

      --eval_records=queries.tfrecords \

      --label_file=labels.txt \

      --vocab_file=vocab.txt \

      --model_dir=model \

      --export_dir=model



The training script has this option added: `train_classifier.sh`.



# Tensorflow Serving



As well as using `predictor.py` to run a saved model to provide

predictions, it is easy to serve a saved model using Tensorflow

Serving with a client server setup. There is a supplied simple rpc client (`predictor_client.py`)

that provides predictions by using tensorflow server.



First make sure you install the tensorflow serving binaries. Instructions are [here](https://github.com/tensorflow/serving/blob/master/tensorflow_serving/g3doc/setup.md#installing-the-modelserver).



You then serve the latest saved model by supplying the base export

directory where you exported saved models to. This directory will

contain the numbered model directories:



    tensorflow_model_server --port=9000 --model_base_path=model



Now you can make requests to the server using gRPC calls. An example

simple client is provided in `predictor_client.py`:



    predictor_client.py --text="Some text to classify"



# Facebook Examples



<< NOT IMPLEMENTED YET >>



You can compare with Facebook's fastText by running similar examples

to what's provided in their repository.



    ./classification_example.sh

    ./classification_results.sh