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ner-asr's Introduction

ner-asr

This repository contains a named entity recognition system for Finnish language.

Requirements

  1. pytorch
  2. pytorch-crf
  3. gensim
  4. morfessor
  5. fasttext

Download resources

The pretrained word embeddings can be downloaded from the following link: https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.fi.300.bin.gz .

You need to place the embeddings in the data/embeddings directory.

Usage

There are two models trained: model_lower and model_upper. The first one is trained on lower case data and without punctuation. The second one is trained on data that contains both lower and upper case letter together with punctuation.

To switch between model, change the flag lowercase_model in config/params.py file.

Use evaluate_document.py in order to annotate a new document. This script takes two input arguments:

--input - input file to be evaluated

--output - path where the output will be stored

The format of the input document is described in the script

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ner-asr's Issues

RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False

Hey,

Apparently while loading the model in evaluate_document.py there is a mistake
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False

You can update the script this way to make it work for those loading the model with CPU :)

# load the model
    if lowercase_model == False:
        model.load_state_dict(torch.load('weights/model_upper.pt', map_location=torch.device('cpu')))
    else:
        model.load_state_dict(torch.load('weights/model_lower.pt', map_location=torch.device('cpu')))

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