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System for the IberLEF 2019 NER Task

Project description

Installation instructions

  1. Download and install the latest Anaconda distribution from here

  2. Start the Anaconda Powershell Prompt if running Windows, or a terminal otherwise. Make sure conda is available in the command line path.

  3. Create a conda environment running conda create -n pvcastro-iberlef python=3.6

  4. Update Anaconda running conda update -n base -c defaults conda

  5. Install pytorch running conda install pytorch-cpu -c pytorch -n pvcastro-iberlef

  6. Activate the created conda environment using conda activate pvcastro-iberlef

  7. Install the AllenNLP framework running pip install -U allennlp

  8. Download the spacy model running python -m spacy download en_core_web_sm

  9. Install the pvcastro-iberlef module running pip install -U pvcastro-iberlef


Execution instructions

Run the NER prediction with a command as python -m pvcastro_iberlef.predict_ner --document-path path_to_the_input_document --out-path path_to_the_output_file

Parameters:

  • document-path: path to the document containing the text to be predicted for NER, with one token per line, with sentences separated by blank lines.
  • out-path: path to the document where the predictions results will be written. Must specify a filename. Example: C:\iberlef\predictions.txt

Observations

Since the IberLEF NER model uses two language models based on ELMo, the trained model ended up quite big, with 1.4Gb aproximately. It takes a while to download it the first time, but the model is cached, so following executions after the first will be quicker.

Project details


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