https://github.com/jalammar/ecco

Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
https://github.com/jalammar/ecco

explorables language-models natural-language-processing nlp pytorch visualization

Last synced: 29 days ago
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Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).

• Host: GitHub
• URL: https://github.com/jalammar/ecco
• Owner: jalammar
• License: bsd-3-clause
• Created: 2020-11-07T10:06:34.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-08-15T19:08:06.000Z (3 months ago)
• Last Synced: 2024-10-01T21:41:37.301Z (about 1 month ago)
• Topics: explorables, language-models, natural-language-processing, nlp, pytorch, visualization
• Language: Jupyter Notebook
• Homepage: https://ecco.readthedocs.io
• Size: 4.06 MB
• Stars: 1,973
• Watchers: 24
• Forks: 168
• Open Issues: 37
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • Contributing: CONTRIBUTING.rst
  • License: LICENSE
  • Authors: AUTHORS.rst

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README

        

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    :alt: Ecco Logo

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    :alt: PyPI Package latest release

    :target: https://pypi.org/project/ecco

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    :alt: Supported versions

    :target: https://pypi.org/project/ecco

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Ecco is a python library for explaining Natural Language Processing models using interactive visualizations.

It provides multiple interfaces to aid the explanation and intuition of `Transformer

`_-based language models. Read: `Interfaces for Explaining Transformer Language Models `_.

Ecco runs inside Jupyter notebooks. It is built on top of `pytorch

`_ and `transformers

`_.

The library is currently an alpha release of a research project. Not production ready. You're welcome to contribute to make it better!

Installation

============

.. code-block:: python

    # Assuming you had PyTorch previously installed

    pip install ecco

Documentation

=============

To use the project:

.. code-block:: python

    import ecco

    # Load pre-trained language model. Setting 'activations' to True tells Ecco to capture neuron activations.

    lm = ecco.from_pretrained('distilgpt2', activations=True)

    # Input text

    text = "The countries of the European Union are:\n1. Austria\n2. Belgium\n3. Bulgaria\n4."

    # Generate 20 tokens to complete the input text.

    output = lm.generate(text, generate=20, do_sample=True)

    

    # Ecco will output each token as it is generated.

    

    # 'output' now contains the data captured from this run, including the input and output tokens

    # as well as neuron activations and input saliency values. 

    

    # To view the input saliency

    output.saliency()

This does the following:

1. It loads a pretrained Huggingface DistilGPT2 model. It wraps it an ecco ``LM`` object that does useful things (e.g. it calculates input saliency, can collect neuron activations).

2. We tell the model to generate 20 tokens.

3. The model returns an ecco ``OutputSeq`` object. This object holds the output sequence, but also a lot of data generated by the generation run, including the input sequence and input saliency values. If we set ``activations=True`` in ``from_pretrained()``, then this would also contain neuron activation values.

4. ``output`` can now produce various interactive explorables. Examples include:

- ``output.saliency()`` to generate input saliency explorable [`Input Saliency Colab Notebook `_]

- ``output.run_nmf()`` to to explore non-negative matrix factorization of neuron activations  [`Neuron Activation Colab Notebook `_]

.. code-block:: python

    # To view the input saliency explorable

    output.saliency()

    

    # to view input saliency with more details (a bar and % value for each token)

    output.saliency(style="detailed")

    

    # output.activations contains the neuron activation values. it has the shape: (layer, neuron, token position)

    

    # We can run non-negative matrix factorization using run_nmf. We pass the number of factors/components to break down into

    nmf_1 = output.run_nmf(n_components=10) 

    # nmf_1 now contains the necessary data to create the interactive nmf explorable:

    nmf_1.explore()