File size: 4,455 Bytes
17a04d9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
import json
import logging
import sys
import flair
import torch
from typing import List
from flair.data import MultiCorpus
from flair.datasets import ColumnCorpus, NER_HIPE_2022, NER_ICDAR_EUROPEANA
from flair.embeddings import (
TokenEmbeddings,
StackedEmbeddings,
TransformerWordEmbeddings
)
from flair import set_seed
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
from utils import prepare_ajmc_corpus, prepare_clef_2020_corpus, prepare_newseye_fi_sv_corpus, prepare_newseye_de_fr_corpus
logger = logging.getLogger("flair")
logger.setLevel(level="INFO")
def run_experiment(seed: int, batch_size: int, epoch: int, learning_rate: float, subword_pooling: str,
hipe_datasets: List[str], json_config: dict):
hf_model = json_config["hf_model"]
context_size = json_config["context_size"]
layers = json_config["layers"] if "layers" in json_config else "-1"
use_crf = json_config["use_crf"] if "use_crf" in json_config else False
# Set seed for reproducibility
set_seed(seed)
corpus_list = []
# Dataset-related
for dataset in hipe_datasets:
dataset_name, language = dataset.split("/")
# E.g. topres19th needs no special preprocessing
preproc_fn = None
if dataset_name == "ajmc":
preproc_fn = prepare_ajmc_corpus
elif dataset_name == "hipe2020":
preproc_fn = prepare_clef_2020_corpus
elif dataset_name == "newseye" and language in ["fi", "sv"]:
preproc_fn = prepare_newseye_fi_sv_corpus
elif dataset_name == "newseye" and language in ["de", "fr"]:
preproc_fn = prepare_newseye_de_fr_corpus
if dataset_name == "icdar":
corpus_list.append(NER_ICDAR_EUROPEANA(language=language))
else:
corpus_list.append(NER_HIPE_2022(dataset_name=dataset_name, language=language, preproc_fn=preproc_fn,
add_document_separator=True))
if context_size == 0:
context_size = False
logger.info("FLERT Context: {}".format(context_size))
logger.info("Layers: {}".format(layers))
logger.info("Use CRF: {}".format(use_crf))
corpora: MultiCorpus = MultiCorpus(corpora=corpus_list, sample_missing_splits=False)
label_dictionary = corpora.make_label_dictionary(label_type="ner")
logger.info("Label Dictionary: {}".format(label_dictionary.get_items()))
embeddings = TransformerWordEmbeddings(
model=hf_model,
layers=layers,
subtoken_pooling=subword_pooling,
fine_tune=True,
use_context=context_size,
)
tagger: SequenceTagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=label_dictionary,
tag_type="ner",
use_crf=use_crf,
use_rnn=False,
reproject_embeddings=False,
)
# Trainer
trainer: ModelTrainer = ModelTrainer(tagger, corpora)
datasets = "-".join([dataset for dataset in hipe_datasets])
trainer.fine_tune(
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}",
learning_rate=learning_rate,
mini_batch_size=batch_size,
max_epochs=epoch,
shuffle=True,
embeddings_storage_mode='none',
weight_decay=0.,
use_final_model_for_eval=False,
)
# Finally, print model card for information
tagger.print_model_card()
if __name__ == "__main__":
filename = sys.argv[1]
with open(filename, "rt") as f_p:
json_config = json.load(f_p)
seeds = json_config["seeds"]
batch_sizes = json_config["batch_sizes"]
epochs = json_config["epochs"]
learning_rates = json_config["learning_rates"]
subword_poolings = json_config["subword_poolings"]
hipe_datasets = json_config["hipe_datasets"] # Do not iterate over them
cuda = json_config["cuda"]
flair.device = f'cuda:{cuda}'
for seed in seeds:
for batch_size in batch_sizes:
for epoch in epochs:
for learning_rate in learning_rates:
for subword_pooling in subword_poolings:
run_experiment(seed, batch_size, epoch, learning_rate, subword_pooling, hipe_datasets,
json_config) # pylint: disable=no-value-for-parameter
|