Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/binary-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/binary-classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by lewtun HF Staff - opened
README.md
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- name: Accuracy
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type: accuracy
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value: 0.8967889908256881
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- task:
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name: Binary text classification
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type: text-classification
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dataset:
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type: emotion
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name: Emotion
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.666
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- name: Accuracy
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type: accuracy
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value: 0.8967889908256881
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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