Closed znadrich-qf closed 8 months ago
Hi @znadrich-qf, Please try to read the README file, you will find there training section. Let me know if that helps
Thanks @shon-otmazgin for the quick response, I do see this code snippet from the README
from fastcoref import TrainingArgs, CorefTrainer
args = TrainingArgs(
output_dir='test-trainer',
overwrite_output_dir=True,
model_name_or_path='distilroberta-base',
device='cuda:2',
epochs=129,
logging_steps=100,
eval_steps=100
) # you can control other arguments such as learning head and others.
trainer = CorefTrainer(
args=args,
train_file='train_file_with_clusters.jsonlines',
dev_file='path-to-dev-file', # optional
test_file='path-to-test-file', # optional
nlp=nlp # optional, for custom nlp class from spacy
)
trainer.train()
trainer.evaluate(test=True)
trainer.push_to_hub('your-fast-coref-model-path')
it seems like this code snippet is focused on training a new model, if I wanted to use the existing FCoref
model as a base would I just need to change some of the arguments? Such as model_name_or_path='distilroberta-base'
to the path of the FCoref
model
I think I've got it all figured out!
Sorry if this has been asked before, but is there a way to fine tune the
FCoref
model on a custom dataset? If so, is there any documentation or code snippets I can look at? It would also be helpful to know what format my data needs to be in.Awesome library and paper, by the way!