Metadata-Version: 2.1
Name: bert_deid
Version: 0.2.3
Summary: Remove identifiers from data using BERT
Home-page: https://github.com/alistairewj/bert-deid
Author: Alistair Johnson
Author-email: aewj@mit.edu
License: Apache 2.0
Description: # bert-deid
        
        Code to fine-tune BERT on a medical note de-identification task.
        
        ## Install
        
        * **(Recommended)** Create an environment called `deid`
            * `conda env create -f environment.yml`
        <!-- * conda: `conda install bert-deid` -->
        * pip install locally
            * `pip install bert-deid`
        
        ## Download
        
        To download the model, we have provided a helper script in bert-deid:
        
        ```sh
        # note: MODEL_DIR environment variable used by download
        # by default, we download to bert_deid_model in the current directory
        export MODEL_DIR="bert_deid_model"
        bert_deid download
        ```
        
        ## Usage
        
        The model can be imported and used directly within Python.
        
        ```python
        from bert_deid.model import Transformer
        
        # load in a trained model
        model_path = 'bert_deid_model'
        deid_model = Transformer(model_path)
        
        with open('tests/example_note.txt', 'r') as fp:
            text = ''.join(fp.readlines())
        
        print(deid_model.apply(text, repl='___'))
        
        # we can also get the original predictions
        preds = deid_model.predict(text)
        
        # print out the identified entities
        for p, pred in enumerate(preds):
            prob = pred[0]
            label = pred[1]
            start, stop = pred[2:]
        
            # print the prediction labels out
            print(f'{text[start:stop]:15s} {label} ({prob:0.3f})')
        ```
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
