Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use HCKLab/BiBert-MultiTask-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HCKLab/BiBert-MultiTask-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HCKLab/BiBert-MultiTask-2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HCKLab/BiBert-MultiTask-2") model = AutoModelForSequenceClassification.from_pretrained("HCKLab/BiBert-MultiTask-2") - Notebooks
- Google Colab
- Kaggle
change mabel score to probability
Browse files
__pycache__/bert_for_sequence_classification.cpython-37.pyc
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Binary files a/__pycache__/bert_for_sequence_classification.cpython-37.pyc and b/__pycache__/bert_for_sequence_classification.cpython-37.pyc differ
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__pycache__/bibert_multitask_classification.cpython-37.pyc
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Binary files a/__pycache__/bibert_multitask_classification.cpython-37.pyc and b/__pycache__/bibert_multitask_classification.cpython-37.pyc differ
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bibert_multitask_classification.py
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@@ -41,13 +41,13 @@ class BiBert_MultiTaskPipeline(Pipeline):
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scores = softmax(outputs)
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if top_k == 1 and _legacy:
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-
return {"label": self.model.config.id2label[scores.argmax().item()], "
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dict_scores = [
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{"label": self.model.config.id2label[i], "
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]
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if not _legacy:
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-
dict_scores.sort(key=lambda x: x["
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if top_k is not None:
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dict_scores = dict_scores[:top_k]
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return dict_scores
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scores = softmax(outputs)
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if top_k == 1 and _legacy:
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return {"label": self.model.config.id2label[scores.argmax().item()], "probability": scores.max().item()}
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dict_scores = [
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{"label": self.model.config.id2label[i], "probability": score.item()} for i, score in enumerate(scores)
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]
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if not _legacy:
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dict_scores.sort(key=lambda x: x["probability"], reverse=True)
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if top_k is not None:
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dict_scores = dict_scores[:top_k]
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return dict_scores
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