flair_train.py

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import random
from typing import List, Optional

import spacy
from flair.data import Corpus
from flair.embeddings import FlairEmbeddings, StackedEmbeddings, TokenEmbeddings, WordEmbeddings
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
from misc.command_line import train_parse_args
from misc.import_annotations import prepare_flair_train_dev_corpus
from ner.model_factory import get_tokenizer


# reproducibility
random.seed(5)


def main(
    data_folder: str,
    model_folder: str,
    dev_size: float,
    nb_epochs: int,
    nb_segment: Optional[int],
    segment: Optional[int],
) -> None:
    nlp = spacy.blank(name="fr")
    nlp.tokenizer = get_tokenizer(nlp)

    corpus: Corpus = prepare_flair_train_dev_corpus(
        spacy_model=nlp, data_folder=data_folder, dev_size=dev_size, nb_segment=nb_segment, segment=segment
    )
    tag_dictionary = corpus.make_tag_dictionary(tag_type="ner")
    print(tag_dictionary.idx2item)

    embedding_types: List[TokenEmbeddings] = [
        WordEmbeddings("fr"),
        FlairEmbeddings("fr-forward"),
        FlairEmbeddings("fr-backward"),
    ]

    embeddings: StackedEmbeddings = StackedEmbeddings(embeddings=embedding_types)

    tagger: SequenceTagger = SequenceTagger(
        hidden_size=256, embeddings=embeddings, use_crf=True, tag_dictionary=tag_dictionary, tag_type="ner"
    )

    trainer: ModelTrainer = ModelTrainer(model=tagger, corpus=corpus, use_tensorboard=False)

    trainer.train(
        model_folder,
        max_epochs=nb_epochs,
        learning_rate=0.1,
        mini_batch_size=32,
        embeddings_storage_mode="cpu",
        checkpoint=False,
    )


if __name__ == "__main__":
    args = train_parse_args(train=True)
    main(
        data_folder=args.input_dir,
        model_folder=args.model_dir,
        dev_size=args.dev_size,
        nb_epochs=args.epoch,
        nb_segment=args.nb_segment,
        segment=args.segment,
    )