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import json |
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import logging |
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import sys |
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import flair |
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import torch |
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from typing import List |
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from flair.data import MultiCorpus |
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from flair.datasets import ColumnCorpus, NER_HIPE_2022, NER_ICDAR_EUROPEANA |
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from flair.embeddings import ( |
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TokenEmbeddings, |
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StackedEmbeddings, |
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TransformerWordEmbeddings |
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) |
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from flair import set_seed |
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from flair.models import SequenceTagger |
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from flair.trainers import ModelTrainer |
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from utils import prepare_ajmc_corpus, prepare_clef_2020_corpus, prepare_newseye_fi_sv_corpus, prepare_newseye_de_fr_corpus |
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logger = logging.getLogger("flair") |
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logger.setLevel(level="INFO") |
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def run_experiment(seed: int, batch_size: int, epoch: int, learning_rate: float, subword_pooling: str, |
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hipe_datasets: List[str], json_config: dict): |
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hf_model = json_config["hf_model"] |
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context_size = json_config["context_size"] |
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layers = json_config["layers"] if "layers" in json_config else "-1" |
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use_crf = json_config["use_crf"] if "use_crf" in json_config else False |
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set_seed(seed) |
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corpus_list = [] |
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for dataset in hipe_datasets: |
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dataset_name, language = dataset.split("/") |
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preproc_fn = None |
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if dataset_name == "ajmc": |
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preproc_fn = prepare_ajmc_corpus |
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elif dataset_name == "hipe2020": |
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preproc_fn = prepare_clef_2020_corpus |
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elif dataset_name == "newseye" and language in ["fi", "sv"]: |
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preproc_fn = prepare_newseye_fi_sv_corpus |
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elif dataset_name == "newseye" and language in ["de", "fr"]: |
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preproc_fn = prepare_newseye_de_fr_corpus |
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if dataset_name == "icdar": |
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corpus_list.append(NER_ICDAR_EUROPEANA(language=language)) |
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else: |
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corpus_list.append(NER_HIPE_2022(dataset_name=dataset_name, language=language, preproc_fn=preproc_fn, |
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add_document_separator=True)) |
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if context_size == 0: |
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context_size = False |
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logger.info("FLERT Context: {}".format(context_size)) |
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logger.info("Layers: {}".format(layers)) |
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logger.info("Use CRF: {}".format(use_crf)) |
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corpora: MultiCorpus = MultiCorpus(corpora=corpus_list, sample_missing_splits=False) |
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label_dictionary = corpora.make_label_dictionary(label_type="ner") |
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logger.info("Label Dictionary: {}".format(label_dictionary.get_items())) |
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embeddings = TransformerWordEmbeddings( |
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model=hf_model, |
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layers=layers, |
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subtoken_pooling=subword_pooling, |
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fine_tune=True, |
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use_context=context_size, |
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) |
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tagger: SequenceTagger = SequenceTagger( |
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hidden_size=256, |
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embeddings=embeddings, |
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tag_dictionary=label_dictionary, |
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tag_type="ner", |
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use_crf=use_crf, |
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use_rnn=False, |
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reproject_embeddings=False, |
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) |
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trainer: ModelTrainer = ModelTrainer(tagger, corpora) |
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datasets = "-".join([dataset for dataset in hipe_datasets]) |
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trainer.fine_tune( |
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f"hmbench-{datasets}-{hf_model}-bs{batch_size}-ws{context_size}-e{epoch}-lr{learning_rate}-pooling{subword_pooling}-layers{layers}-crf{use_crf}-{seed}", |
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learning_rate=learning_rate, |
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mini_batch_size=batch_size, |
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max_epochs=epoch, |
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shuffle=True, |
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embeddings_storage_mode='none', |
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weight_decay=0., |
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use_final_model_for_eval=False, |
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) |
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tagger.print_model_card() |
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if __name__ == "__main__": |
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filename = sys.argv[1] |
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with open(filename, "rt") as f_p: |
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json_config = json.load(f_p) |
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seeds = json_config["seeds"] |
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batch_sizes = json_config["batch_sizes"] |
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epochs = json_config["epochs"] |
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learning_rates = json_config["learning_rates"] |
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subword_poolings = json_config["subword_poolings"] |
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hipe_datasets = json_config["hipe_datasets"] |
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cuda = json_config["cuda"] |
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flair.device = f'cuda:{cuda}' |
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for seed in seeds: |
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for batch_size in batch_sizes: |
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for epoch in epochs: |
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for learning_rate in learning_rates: |
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for subword_pooling in subword_poolings: |
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run_experiment(seed, batch_size, epoch, learning_rate, subword_pooling, hipe_datasets, |
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json_config) |
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