(self, hparams, **kwargs)
| 60 | val_metric = "rouge2" |
| 61 | |
| 62 | def __init__(self, hparams, **kwargs): |
| 63 | super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs) |
| 64 | use_task_specific_params(self.model, "summarization") |
| 65 | save_git_info(self.hparams.output_dir) |
| 66 | self.metrics_save_path = Path(self.output_dir) / "metrics.json" |
| 67 | self.hparams_save_path = Path(self.output_dir) / "hparams.pkl" |
| 68 | pickle_save(self.hparams, self.hparams_save_path) |
| 69 | self.step_count = 0 |
| 70 | self.metrics = defaultdict(list) |
| 71 | |
| 72 | self.dataset_kwargs: dict = dict( |
| 73 | data_dir=self.hparams.data_dir, |
| 74 | max_source_length=self.hparams.max_source_length, |
| 75 | prefix=self.model.config.prefix or "", |
| 76 | ) |
| 77 | n_observations_per_split = { |
| 78 | "train": self.hparams.n_train, |
| 79 | "val": self.hparams.n_val, |
| 80 | "test": self.hparams.n_test, |
| 81 | } |
| 82 | self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()} |
| 83 | |
| 84 | self.target_lens = { |
| 85 | "train": self.hparams.max_target_length, |
| 86 | "val": self.hparams.val_max_target_length, |
| 87 | "test": self.hparams.test_max_target_length, |
| 88 | } |
| 89 | assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}" |
| 90 | assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}" |
| 91 | |
| 92 | if self.hparams.freeze_embeds: |
| 93 | self.freeze_embeds() |
| 94 | if self.hparams.freeze_encoder: |
| 95 | freeze_params(self.model.model.encoder) # TODO: this will break for t5 |
| 96 | self.hparams.git_sha = get_git_info()["repo_sha"] |
| 97 | self.num_workers = hparams.num_workers |
| 98 | |
| 99 | def freeze_embeds(self): |
| 100 | """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5.""" |
nothing calls this directly
no test coverage detected