Example scripts of how to use nlpboost for each task

In the examples folder you will find example scripts showing how to fine-tune models for different tasks. These tasks are divided in directories, as you see. In all scripts it is also shown how to use ResultsPlotter to save a metrics comparison figure of the models trained.

• classification

  For classification we have 2 examples. train_classification.py shows how to train a BERTIN model for multi-class classification (emotion detection, check tweet_eval: emotion dataset for more info.). On the other hand, train_multilabel.py shows how to train a model on a multilabel task.

• extractive_qa

  For extractive QA we have only 1 example, as this type of task is very similar in all cases: train_sqac.py shows how to train a MarIA-large (Spanish Roberta-large) model on SQAC dataset, with hyperparameter search.

• NER

  For NER, there is an example script, showing how to train multiple models on multiple NER datasets with different format, where we need to apply a pre_func to one of the datasets. The script is called train_spanish_ner.py.

• seq2seq

  For this task, check out train_maria_encoder_decoder_marimari.py, which shows how to train a seq2seq model when no encoder-decoder architecture is readily available for a certain language, in this case Spanish. On the other hand, check out train_summarization_mlsum.py to learn how to configure training for two multilingual encoder-decoder models for MLSUM summarization task.

Important: For more detailed tutorials in Jupyter-Notebook format, please check nlpboost notebooks. These tutorials have explanations on all the configuration, which is helpful for getting to better know the tool. They are intended to provide a deep understanding on the different configurations that are needed for each of the tasks, so that the user can easily adapt the scripts for their own tasks and needs.