Joint Learn: A python toolkit for task-specific weight sharing for sequence classification

Transfer Learning has achieved state-of-the-art results recently in Machine Learning and specially, Natural Language Processing tasks. However, for low resource corporas where there is a lack of pre-trained model checkpoints available. We propose Joint Learn which leverages task specific weight-sharing for training multiple sequence classification tasks simulataneously and has empirically showed resulting in more generalizable models. Joint Learn is a PyTorch based comprehensive toolkit for weight-sharing in text classification settings.

Joint Learn LSTM Self Attention	Joint Learn Transformer LSTM Self Attention

Usage

LSTM

Full Example

    ## init jl lstm
    jl_lstm = JLLSTMClassifier(
        batch_size=batch_size,
        hidden_size=hidden_size,
        lstm_layers=lstm_layers,
        embedding_size=embedding_size,
        dataset_hyperparams=dataset_hyperparams,
        device=device,
    )

    ## define optimizer and loss function
    optimizer = torch.optim.Adam(params=jl_lstm.parameters())

    train_model(
        model=jl_lstm,
        optimizer=optimizer,
        dataloaders=jl_dataloaders,
        max_epochs=max_epochs,
        config_dict={"device": device, "model_name": "jl_lstm"},
    )

LSTM Transformer Encoder

Full Example

    ## init jl transformer lstm
    jl_lstm = JLLSTMTransformerClassifier(
        batch_size=batch_size,
        hidden_size=hidden_size,
        lstm_layers=lstm_layers,
        embedding_size=embedding_size,
        nhead=nhead,
        transformer_hidden_size=transformer_hidden_size,
        transformer_layers=transformer_layers,
        dataset_hyperparams=dataset_hyperparams,
        device=device,
        max_seq_length=max_seq_length,
    )

    ## define optimizer and loss function
    optimizer = torch.optim.Adam(params=jl_lstm.parameters())

    train_model(
        model=jl_lstm,
        optimizer=optimizer,
        dataloaders=jl_dataloaders,
        max_epochs=max_epochs,
        config_dict={"device": device, "model_name": "jl_lstm"},
    )

LSTM Self-Attention

Full Example

    ## init jl lstm self-attention
    jl_lstm = JLLSTMAttentionClassifier(
        batch_size=batch_size,
        hidden_size=hidden_size,
        lstm_layers=lstm_layers,
        embedding_size=embedding_size,
        dataset_hyperparams=dataset_hyperparams,
        bidirectional=bidirectional,
        fc_hidden_size=fc_hidden_size,
        self_attention_config=self_attention_config,
        device=device,
    )

    ## define optimizer and loss function
    optimizer = torch.optim.Adam(params=jl_lstm.parameters())

    train_model(
        model=jl_lstm,
        optimizer=optimizer,
        dataloaders=jl_dataloaders,
        max_epochs=max_epochs,
        config_dict={
            "device": device,
            "model_name": "jl_lstm_attention",
            "self_attention_config": self_attention_config,
        },
    )

LSTM Self-Attention with Transformer Encoder

Full Example

 ## init jl lstm Self-Attention with Transformer Encoder
    jl_lstm = JLLSTMTransformerAttentionClassifier(
        batch_size=batch_size,
        hidden_size=hidden_size,
        lstm_layers=lstm_layers,
        embedding_size=embedding_size,
        nhead=nhead,
        transformer_hidden_size=transformer_hidden_size,
        transformer_layers=transformer_layers,
        dataset_hyperparams=dataset_hyperparams,
        bidirectional=bidirectional,
        fc_hidden_size=fc_hidden_size,
        self_attention_config=self_attention_config,
        device=device,
        max_seq_length=max_seq_length,
    )

Citing & Authors

If you find this repository helpful, feel free to cite our publication Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention:

@article{khan2022hindi,
  title={Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention},
  author={Khan, Shahrukh and Shahid, Mahnoor},
  journal={BOHR International Journal of Research on Natural Language Computing (BIJRNLC)},
  year={2022}
}