pvcastro-iberlef 0.4.1

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.