System for the IberLEF 2019 NER Task
Download and install the latest Anaconda distribution from here
Start the Anaconda Powershell Prompt if running Windows, or a terminal otherwise. Make sure conda is available in the command line path.
Create a conda environment running
conda create -n pvcastro-iberlef python=3.6
Update Anaconda running
conda update -n base -c defaults conda
Install pytorch running
conda install pytorch-cpu -c pytorch -n pvcastro-iberlef
Activate the created conda environment using
conda activate pvcastro-iberlef
Install the AllenNLP framework running
pip install -U allennlp
Download the spacy model running
python -m spacy download en_core_web_sm
Install the pvcastro-iberlef module running
pip install -U pvcastro-iberlef
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
- 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
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.
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for pvcastro_iberlef-0.4.1-py3-none-any.whl