xl-bert 0.0.3

This lets you find sentence embedding using word embedding from XLNet and Bert

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Documentation

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This is the repository of the xl_bert package. This library helps you find sentence embedding for your sentence using SOTA language models such as Bert and XLNET.

This library takes in an input seven paramters :

**sentence_list,model_name,model_dir,token_model_dir,n_layers,strategy,max_len**. Each parameter has been explained below.

**sentence_list** : List of sentences you want to get embedding of. 

**model_name** : Name of model which you want to use, currently can be 'bert' or 'xlnet'.

**model_dir** : Directory path of pretrained/finetuned Bert/XLNet language model. Default is 'xlnet-base-cased'. Pretrained language models can be seen from here:
https://huggingface.co/transformers/pretrained_models.html

**token_model_dir** : Directory path of tokenizer. Default is 'xlnet-base-cased'


**n_layers** :  Number of layers you want to use to get sentence embedding.Default is 1

**Strategy** : This is where it gets interesting. Strategy is categorised in four choices.

    'avg': We average each layer individually and then average n_layers.
    'cat': We concatenate each layer individually, then we concatenate n_layers
    'avgcat': We average each layer individually and then concat n_layers
    'catavg': We concat each individual layer and then average n_layers. 

**max_len** : Maximum length of sentence you want. Default 50

================ Installation

pip install xl_bert

Usage with Bert as well as XLNet

get_sentence_embedding(['I am playing','let me dance'],model_name='xlnet',model_dir='xlnet- 
large-cased',token_model_dir='xlnet-large-cased',n_layers=2,strategy='avg',max_len=50) 

Contribution

Package author and current maintainer is Shivam Panwar ([e-mail]); You are more than welcome to approach him for help. Contributions are very welcomed, especially since this package is very much in its infancy.

Created by Shivam Panwar (panwar.shivam199@gmai.