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Do not softmax when num_labels==1 (#7726)

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* Do not softmax when num_labels==1

* Update src/transformers/pipelines.py

Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>

Co-authored-by: Funtowicz Morgan <mfuntowicz@users.noreply.github.com>
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Lysandre Debut 2020-10-13 15:42:27 +02:00 committed by GitHub
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src/transformers/pipelines.py
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@ -943,6 +943,9 @@ class TextClassificationPipeline(Pipeline):
task identifier: :obj:`"sentiment-analysis"` (for classifying sequences according to positive or negative
sentiments).
If multiple classification labels are available (:obj:`model.config.num_labels >= 2`), the pipeline will run
a softmax over the results. If there is a single label, the pipeline will run a sigmoid over the result.
The models that this pipeline can use are models that have been fine-tuned on a sequence classification task.
See the up-to-date list of available models on
`huggingface.co/models <https://huggingface.co/models?filter=text-classification>`__.
@ -977,7 +980,11 @@ class TextClassificationPipeline(Pipeline):
If ``self.return_all_scores=True``, one such dictionary is returned per label.
"""
outputs = super().__call__(*args, **kwargs)
scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
if self.model.config.num_labels == 1:
scores = 1.0 / (1.0 + np.exp(-outputs))
else:
scores = np.exp(outputs) / np.exp(outputs).sum(-1, keepdims=True)
if self.return_all_scores:
return [
[{"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(item)]