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save initial hidden states in sequence tagger #3010

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Dec 6, 2022
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save initial hidden states in sequence tagger #3010

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13969f2
save initial hidden states in sequence tagger
Dec 5, 2022
41cbaad
set train_initial_hidden_state in sequence_tagger even if no lstm is …
Dec 5, 2022
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3 changes: 3 additions & 0 deletions flair/models/sequence_tagger_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,6 +188,7 @@ def __init__(
self.linear = torch.nn.Linear(hidden_output_dim, len(self.label_dictionary))
else:
self.linear = torch.nn.Linear(embedding_dim, len(self.label_dictionary))
self.train_initial_hidden_state = False

# the loss function is Viterbi if using CRF, else regular Cross Entropy Loss
self.loss_function = (
Expand Down Expand Up @@ -606,6 +607,7 @@ def _get_state_dict(self):
"rnn_type": self.rnn_type,
"reproject_embeddings": self.reproject_embeddings,
"weight_dict": self.weight_dict,
"train_initial_hidden_state": self.train_initial_hidden_state,
}

return model_state
Expand Down Expand Up @@ -635,6 +637,7 @@ def _init_model_with_state_dict(cls, state, **kwargs):
reproject_embeddings=state.get("reproject_embeddings", True),
loss_weights=state.get("weight_dict"),
init_from_state_dict=True,
train_initial_hidden_state=state.get("train_initial_hidden_state", False),
**kwargs,
)

Expand Down
19 changes: 19 additions & 0 deletions tests/models/test_sequence_tagger.py
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Expand Up @@ -85,6 +85,25 @@ def test_train_load_use_tagger_flair_embeddings(self, results_base_path, corpus,
loaded_model.predict([self.empty_sentence])
del loaded_model

@pytest.mark.integration
def test_train_load_use_tagger_with_trainable_hidden_state(
self, embeddings, results_base_path, corpus, example_sentence
):
tag_dictionary = corpus.make_label_dictionary("ner", add_unk=False)

model = self.build_model(embeddings, tag_dictionary, train_initial_hidden_state=True)
trainer = ModelTrainer(model, corpus)

trainer.train(results_base_path, shuffle=False, **self.training_args)

del trainer, model, tag_dictionary, corpus
loaded_model = self.model_cls.load(results_base_path / "final-model.pt")

loaded_model.predict(example_sentence)
loaded_model.predict([example_sentence, self.empty_sentence])
loaded_model.predict([self.empty_sentence])
del loaded_model

@pytest.mark.integration
def test_train_load_use_tagger_disjunct_tags(
self, results_base_path, tasks_base_path, embeddings, example_sentence
Expand Down