text_summarizer

Example 3 - Text summarization

##Model Set Up

Model Initializing

The sentence_classification\resources folder contains model weights from a fine-tuned model, saved as model-fine-tuned.hdf5(not tracked by GitHub). This file only contains the weights, not the model architecture. Therefore, the model class, is initialized by loading the base model from the Hugging Face model hub. Next, the models' weights are loaded from the sentence_classification\resources. In the last step, the model is wrapped in a transformers pipeline, which conveniently wraps pre-processing, prediction and post-processing in a single object. embeddings of the model.

Pre-processing of inputs

The pipeline object pre-processes the model inputs according to the model' requirements.

Prediction of inputs

The predict method is a simple wrapper for the Hugging Face pipeline object.

Post-processing of model outputs

While the pipeline object converts to numerical outputs in a readable format, the annotations are on a token-level.

Sequence labeling annotations

An exemplary annotation for an input sentence:

annotation = {
                "begin": 0,
                'end': 25,
                "value": "DNA Synthesis in cleavage-stage embryos leads to chromosome breakage and segmental aneuploidies during mitosis, impairing the embryo's developmental potential and ability to acquire long neural genes, which are transcriptionally silent at this stage of development.",
                'label': 'summary',
                'confidence': 0.68,
                'componentId': 'TextSummarizer:0.1.0'
            }