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returning model raw outputs as well #8

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12 changes: 7 additions & 5 deletions gliner/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,9 +129,10 @@ def prepare_model_inputs(self, texts: str, labels: str):
return model_input, raw_batch

def predict_entities(self, text, labels, flat_ner=True, threshold=0.5, multi_label=False):
return self.batch_predict_entities(
all_entities, model_output = self.batch_predict_entities(
[text], labels, flat_ner=flat_ner, threshold=threshold, multi_label=multi_label
)[0]
)
Comment on lines +132 to +134

@gemini-code-assist gemini-code-assist Bot Feb 17, 2026

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medium

The model_output variable is assigned but never used. To signal that it's intentionally being ignored, you can use an underscore _ as the variable name. This improves code clarity and adheres to common Python conventions for unused variables.

Suggested change
all_entities, model_output = self.batch_predict_entities(
[text], labels, flat_ner=flat_ner, threshold=threshold, multi_label=multi_label
)[0]
)
all_entities, _ = self.batch_predict_entities(
[text], labels, flat_ner=flat_ner, threshold=threshold, multi_label=multi_label
)

return all_entities[0]

@torch.no_grad()
def batch_predict_entities(self, texts, labels, flat_ner=True, threshold=0.5, multi_label=False):
Expand All @@ -144,7 +145,8 @@ def batch_predict_entities(self, texts, labels, flat_ner=True, threshold=0.5, mu

model_input, raw_batch = self.prepare_model_inputs(texts, labels)

model_output = self.model(**model_input)[0]
model_output_raw = self.model(**model_input)
model_output = model_output_raw[0]

if not isinstance(model_output, torch.Tensor):
model_output = torch.from_numpy(model_output)
Expand All @@ -169,7 +171,7 @@ def batch_predict_entities(self, texts, labels, flat_ner=True, threshold=0.5, mu
})
all_entities.append(entities)

return all_entities
return all_entities, model_output_raw

@chatgpt-codex-connector chatgpt-codex-connector Bot Feb 17, 2026

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P1 Badge Preserve batch_predict_entities return contract

Changing batch_predict_entities to always return (all_entities, model_output_raw) breaks existing callers that were written against the previous contract (entities = model.batch_predict_entities(...) returning only entity lists). In downstream inference pipelines this can immediately raise type errors or silently corrupt post-processing when code iterates/serializes the returned value, so this should be made backward-compatible (for example via an opt-in flag for raw outputs) rather than a hard return-type change.

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def evaluate(self, test_data, flat_ner=False, multi_label=False, threshold=0.5, batch_size=12, entity_types=None):
"""
Expand Down Expand Up @@ -427,4 +429,4 @@ def _from_pretrained(
if (config.class_token_index==-1 or config.vocab_size == -1) and resize_token_embeddings:
gliner.data_processor.transformer_tokenizer.add_tokens(add_tokens)

return gliner
return gliner