Training

Train your entity extraction model using PyTorch.

First, activate the virtual environment as explained here.

Train an Entity Extraction Model

Configure the necessary hyperparameters in the config.json file. The defaults are:

{
    "model_name": "bert-base-cased",
    "crf": false,
    "dataset_path": "tabiya/job_ner_dataset",   
    "label_list": ["O", "B-Skill", "B-Qualification", "I-Domain", "I-Experience", "I-Qualification", "B-Occupation", "B-Domain", "I-Occupation", "I-Skill", "B-Experience"],
    "model_max_length": 128,
    "batch_size": 32,
    "learning_rate": 1e-4,
    "epochs": 4,
    "weight_decay": 0.01,
    "save": false,
    "output_path": "bert_job_ner"
}

To train the model, run the following script in the train directory:

python train.py

The training script is based on the official HuggingFace token classification tutorial.

Train an Entity Similarity Model

Configure the necessary hyperparameters in the sbert_train function in the sbert_train.py file:

sbert_train(model_id='all-MiniLM-L6-v2', dataset_path='your/dataset/path', output_path='your/output/path')

To train the similarity model, run the following script in the train directory:

python sbert_train.py

The dataset should be formatted as a CSV file with two columns, such as 'title' and 'esco_label', where each row contains a pair of related textual data points to be used during the training process. Make sure there are no missing values in your dataset to ensure successful training of the model. Here's an example of how your CSV file might look:

title	esco_label
Senior Conflict Manager	public institution director
etc	etc

More information can be found here.