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finetune.py: specifying generation min_length (#8478)

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Daniel Khashabi 2020-11-25 23:03:02 -08:00 committed by GitHub
parent 30e7f7e5da
commit 5aa361f3e5
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1 changed files with 6 additions and 0 deletions
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examples/seq2seq/finetune.py
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@ -113,6 +113,10 @@ class SummarizationModule(BaseTransformer):
self.eval_max_length = self.hparams.eval_max_gen_length
else:
self.eval_max_length = self.model.config.max_length
if self.hparams.eval_min_gen_length is not None:
self.eval_min_length = self.hparams.eval_min_gen_length
else:
self.eval_min_length = self.model.config.min_length
self.val_metric = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def save_readable_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str, List[str]]:
@ -219,6 +223,7 @@ class SummarizationModule(BaseTransformer):
decoder_start_token_id=self.decoder_start_token_id,
num_beams=self.eval_beams,
max_length=self.eval_max_length,
min_length=self.eval_min_length,
)
gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
preds: List[str] = self.ids_to_clean_text(generated_ids)
@ -346,6 +351,7 @@ class SummarizationModule(BaseTransformer):
"--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
)
parser.add_argument("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
parser.add_argument("--eval_min_gen_length", type=int, default=None, help="never generate shorter than n tokens")
parser.add_argument("--save_top_k", type=int, default=1, required=False, help="How many checkpoints to save")
parser.add_argument(
"--early_stopping_patience",