Siamese-nn-semantic-text-similarity - A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task

Overview

Siamese Deep Neural Networks for Semantic Text Similarity PyTorch

A repository containing comprehensive Neural Networks based PyTorch implementations for the semantic text similarity task, including architectures such as:

  • Siamese LSTM
  • Siamese BiLSTM with Attention
  • Siamese Transformer
  • Siamese BERT.

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Usage

  • install dependencies
pip install -r requirements.txt
  • download spacy en model for tokenization
python -m spacy download en

Siamese LSTM

Siamese LSTM Example

 ## init siamese lstm
    siamese_lstm = SiameseLSTM(
        batch_size=batch_size,
        output_size=output_size,
        hidden_size=hidden_size,
        vocab_size=vocab_size,
        embedding_size=embedding_size,
        embedding_weights=embedding_weights,
        lstm_layers=lstm_layers,
        device=device,
    )

    ## define optimizer
    optimizer = torch.optim.Adam(params=siamese_lstm.parameters())
   
   ## train model
    train_model(
        model=siamese_lstm,
        optimizer=optimizer,
        dataloader=sick_dataloaders,
        data=sick_data,
        max_epochs=max_epochs,
        config_dict={"device": device, "model_name": "siamese_lstm"},
    )

Siamese BiLSTM with Attention

Siamese BiLSTM with Attention Example

     ## init siamese lstm
     siamese_lstm_attention = SiameseBiLSTMAttention(
        batch_size=batch_size,
        output_size=output_size,
        hidden_size=hidden_size,
        vocab_size=vocab_size,
        embedding_size=embedding_size,
        embedding_weights=embedding_weights,
        lstm_layers=lstm_layers,
        self_attention_config=self_attention_config,
        fc_hidden_size=fc_hidden_size,
        device=device,
        bidirectional=bidirectional,
    )
    
    ## define optimizer
    optimizer = torch.optim.Adam(params=siamese_lstm_attention.parameters())
   
   ## train model
    train_model(
        model=siamese_lstm_attention,
        optimizer=optimizer,
        dataloader=sick_dataloaders,
        data=sick_data,
        max_epochs=max_epochs,
        config_dict={
            "device": device,
            "model_name": "siamese_lstm_attention",
            "self_attention_config": self_attention_config,
        },
    )

Siamese Transformer

Siamese Transformer Example

    ## init siamese bilstm with attention
    siamese_transformer = SiameseTransformer(
        batch_size=batch_size,
        vocab_size=vocab_size,
        embedding_size=embedding_size,
        nhead=attention_heads,
        hidden_size=hidden_size,
        transformer_layers=transformer_layers,
        embedding_weights=embedding_weights,
        device=device,
        dropout=dropout,
        max_sequence_len=max_sequence_len,
    )

    ## define optimizer
    optimizer = torch.optim.Adam(params=siamese_transformer.parameters())
   
   ## train model
    train_model(
        model=siamese_transformer,
        optimizer=optimizer,
        dataloader=sick_dataloaders,
        data=sick_data,
        max_epochs=max_epochs,
        config_dict={"device": device, "model_name": "siamese_transformer"},
    )

Siamese BERT

Siamese BERT Example

    from siamese_sts.siamese_net.siamese_bert import BertForSequenceClassification
    ## init siamese bert
    siamese_bert = BertForSequenceClassification.from_pretrained(model_name)

    ## train model
    trainer = transformers.Trainer(
        model=siamese_bert,
        args=transformers.TrainingArguments(
            output_dir="./output",
            overwrite_output_dir=True,
            learning_rate=1e-5,
            do_train=True,
            num_train_epochs=num_epochs,
            # Adjust batch size if this doesn't fit on the Colab GPU
            per_device_train_batch_size=batch_size,
            save_steps=3000,
        ),
        train_dataset=sick_dataloader,
    )
    trainer.train()
Issues
  • Bump nltk from 3.4.5 to 3.6.6

    Bump nltk from 3.4.5 to 3.6.6

    Bumps nltk from 3.4.5 to 3.6.6.

    Changelog

    Sourced from nltk's changelog.

    Version 3.7 2022-02-09

    • Improve and update the NLTK team page on nltk.org (#2855, #2941)
    • Drop support for Python 3.6, support Python 3.10 (#2920)

    Version 3.6.7 2021-12-28

    • Resolve IndexError in sent_tokenize and word_tokenize (#2922)

    Version 3.6.6 2021-12-21

    • Refactor gensim.doctest to work for gensim 4.0.0 and up (#2914)
    • Add Precision, Recall, F-measure, Confusion Matrix to Taggers (#2862)
    • Added warnings if .zip files exist without any corresponding .csv files. (#2908)
    • Fix FileNotFoundError when the download_dir is a non-existing nested folder (#2910)
    • Rename omw to omw-1.4 (#2907)
    • Resolve ReDoS opportunity by fixing incorrectly specified regex (#2906)
    • Support OMW 1.4 (#2899)
    • Deprecate Tree get and set node methods (#2900)
    • Fix broken inaugural test case (#2903)
    • Use Multilingual Wordnet Data from OMW with newer Wordnet versions (#2889)
    • Keep NLTKs "tokenize" module working with pathlib (#2896)
    • Make prettyprinter to be more readable (#2893)
    • Update links to the nltk book (#2895)
    • Add CITATION.cff to nltk (#2880)
    • Resolve serious ReDoS in PunktSentenceTokenizer (#2869)
    • Delete old CI config files (#2881)
    • Improve Tokenize documentation + add TokenizerI as superclass for TweetTokenizer (#2878)
    • Fix expected value for BLEU score doctest after changes from #2572
    • Add multi Bleu functionality and tests (#2793)
    • Deprecate 'return_str' parameter in NLTKWordTokenizer and TreebankWordTokenizer (#2883)
    • Allow empty string in CFG's + more (#2888)
    • Partition tree.py module into tree package + pickle fix (#2863)
    • Fix several TreebankWordTokenizer and NLTKWordTokenizer bugs (#2877)
    • Rewind Wordnet data file after each lookup (#2868)
    • Correct init call for SyntaxCorpusReader subclasses (#2872)
    • Documentation fixes (#2873)
    • Fix levenstein distance for duplicated letters (#2849)
    • Support alternative Wordnet versions (#2860)
    • Remove hundreds of formatting warnings for nltk.org (#2859)
    • Modernize nltk.org/howto pages (#2856)
    • Fix Bleu Score smoothing function from taking log(0) (#2839)
    • Update third party tools to newer versions and removing MaltParser fixed version (#2832)
    • Fix TypeError: _pretty() takes 1 positional argument but 2 were given in sem/drt.py (#2854)
    • Replace http with https in most URLs (#2852)

    Thanks to the following contributors to 3.6.6 Adam Hawley, BatMrE, Danny Sepler, Eric Kafe, Gavish Poddar, Panagiotis Simakis, RnDevelover, Robby Horvath, Tom Aarsen, Yuta Nakamura, Mohaned Mashaly

    ... (truncated)

    Commits
    • 4862b09 updates for 3.6.6
    • 6b60213 Refactor gensim.doctest to work for gensim 4.0.0 and up (#2914)
    • 59aa3fb Fix decode error for bllip parser (#2897)
    • a28d256 Add Precision, Recall, F-measure, Confusion Matrix to Taggers (#2862)
    • 72d9885 Added warnings if .zip files exist without any corresponding .csv files. (#2908)
    • dea7b44 Fix FileNotFoundError when the download_dir is a non-existing nested fold...
    • abbe86b Undo #2909 due to unexpected test failure
    • c075dab Allow commits with /nocache to not use the cache (#2909)
    • d6d513d Renamed omw to omw-1.4 (#2907)
    • 2a50a3e Resolve ReDoS opportunity by fixing incorrectly specified regex (#2906)
    • Additional commits viewable in compare view

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Owner
Shahrukh Khan
CS Grad Student @ Saarland University
Shahrukh Khan
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