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verbose message to increase size_embeddings_count #11

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verbose message to increase size_embeddings_count #11

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15 changes: 10 additions & 5 deletions jerex/data_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,8 @@ def __init__(self, tokenizer: BertTokenizer, task_type: str, types_path: str = N
sampling_processes: int = 4, neg_mention_count: int = 50,
neg_relation_count: int = 50, neg_coref_count: int = 50,
max_span_size: int = 10, neg_mention_overlap_ratio: float = 0.5,
final_valid_evaluate: bool = False):
final_valid_evaluate: bool = False,
size_embeddings_count: int = 30):
super().__init__()

if types_path is not None:
Expand Down Expand Up @@ -56,6 +57,7 @@ def __init__(self, tokenizer: BertTokenizer, task_type: str, types_path: str = N
self._neg_coref_count = neg_coref_count
self._max_span_size = max_span_size
self._neg_mention_overlap_ratio = neg_mention_overlap_ratio
self._size_embeddings_count = size_embeddings_count

self._train_path = train_path
self._valid_path = valid_path
Expand All @@ -81,7 +83,8 @@ def setup(self, stage=None):
neg_rel_count=self._neg_relation_count,
max_span_size=self._max_span_size,
neg_mention_overlap_ratio=self._neg_mention_overlap_ratio,
tokenizer=self._tokenizer)
tokenizer=self._tokenizer,
size_embeddings_count=self._size_embeddings_count)

self._train_dataset.switch_task(self._task_type)
self._train_dataset.switch_mode(DocREDDataset.TRAIN_MODE)
Expand All @@ -91,7 +94,8 @@ def setup(self, stage=None):
entity_types=self._entity_types,
relation_types=self._relation_types,
max_span_size=self._max_span_size,
tokenizer=self._tokenizer)
tokenizer=self._tokenizer,
size_embeddings_count=self._size_embeddings_count)

self._valid_dataset.switch_task(self._task_type)
self._valid_dataset.switch_mode(DocREDDataset.INFERENCE_MODE)
Expand All @@ -104,15 +108,16 @@ def setup(self, stage=None):
entity_types=self._entity_types,
relation_types=self._relation_types,
max_span_size=self._max_span_size,
tokenizer=self._tokenizer)
tokenizer=self._tokenizer,
size_embeddings_count=self._size_embeddings_count)
else:
self._test_dataset = self._valid_dataset

self._test_dataset.switch_task(self._task_type)
self._test_dataset.switch_mode(DocREDDataset.INFERENCE_MODE)

def train_dataloader(self):
return DataLoader(self._train_dataset, batch_size=self._train_batch_size, shuffle=True, drop_last=True,
return DataLoader(self._train_dataset, batch_size=self._train_batch_size, shuffle=False, drop_last=True,
num_workers=self._sampling_processes,
collate_fn=collate_fn_padding)

Expand Down
7 changes: 5 additions & 2 deletions jerex/datasets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,8 @@ class DocREDDataset(TorchDataset):
INFERENCE_MODE = 'inference'

def __init__(self, dataset_path, entity_types, relation_types, tokenizer, neg_mention_count=200,
neg_rel_count=200, neg_coref_count=200, max_span_size=10, neg_mention_overlap_ratio=0.5):
neg_rel_count=200, neg_coref_count=200, max_span_size=10, neg_mention_overlap_ratio=0.5,
size_embeddings_count=30):
self._dataset_path = dataset_path
self._entity_types = entity_types
self._relation_types = relation_types
Expand All @@ -29,6 +30,7 @@ def __init__(self, dataset_path, entity_types, relation_types, tokenizer, neg_me
self._max_span_size = max_span_size
self._neg_mention_overlap_ratio = neg_mention_overlap_ratio
self._tokenizer = tokenizer
self._size_embeddings_count = size_embeddings_count

self._mode = DocREDDataset.TRAIN_MODE
self._task = None
Expand Down Expand Up @@ -204,7 +206,8 @@ def __getitem__(self, index: int):
return sampling_joint.create_joint_train_sample(doc, self._neg_mention_count, self._neg_rel_count,
self._neg_coref_count,
self._max_span_size, self._neg_mention_overlap_ratio,
len(self._relation_types))
len(self._relation_types),
self._size_embeddings_count)
elif self._task == TaskType.MENTION_LOCALIZATION:
return sampling_classify.create_mention_classify_train_sample(doc, self._neg_mention_count,
self._max_span_size,
Expand Down
3 changes: 2 additions & 1 deletion jerex/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -286,7 +286,8 @@ def train(cfg: TrainConfig):
max_span_size=cfg.sampling.max_span_size,
neg_mention_overlap_ratio=cfg.sampling.neg_mention_overlap_ratio,
final_valid_evaluate=cfg.misc.final_valid_evaluate
and cfg.datasets.test_path is None)
and cfg.datasets.test_path is None,
size_embeddings_count=cfg.model.size_embeddings_count)

data_module.setup('fit')

Expand Down
4 changes: 3 additions & 1 deletion jerex/sampling/sampling_joint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,8 @@


def create_joint_train_sample(doc: Document, neg_mention_count: int, neg_rel_count: int, neg_coref_count: int,
max_span_size: int, neg_mention_overlap_ratio: float, rel_type_count: int):
max_span_size: int, neg_mention_overlap_ratio: float, rel_type_count: int,
size_embeddings_count: int):
encodings = doc.encodings # document sub-word encoding
context_size = len(encodings)

Expand Down Expand Up @@ -89,6 +90,7 @@ def create_joint_train_sample(doc: Document, neg_mention_count: int, neg_rel_cou
assert len(coref_mention_pairs) == len(coref_sample_masks) == len(coref_types) == len(coref_eds)
assert len(entities) == len(entity_types)
assert len(rel_entity_pairs) == len(rel_types)
assert mention_sizes.max().item() < size_embeddings_count, f"You should increase the `size_embeddings_count` config.model section atleast to {mention_sizes.max()}"

return dict(encodings=encodings, context_masks=context_masks, mention_masks=mention_masks,
mention_sizes=mention_sizes, mention_types=mention_types, mention_sample_masks=mention_sample_masks,
Expand Down