Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/multi-class-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and test split of emotion
#42
by lewtun HF Staff - opened
README.md
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- emotion
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metrics:
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- accuracy
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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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- emotion
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metrics:
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- accuracy
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model-index:
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- name: autoevaluate/multi-class-classification
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: emotion
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type: emotion
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config: default
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split: test
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metrics:
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- type: accuracy
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value: 0.9185
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name: Accuracy
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verified: true
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verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDNlY2I5ZTZkOTBmMTQwMjA0ZTFhYzE4NTdiZGIzYmNiOTkyMjBjMDY0MDJhNGY4ZTBiMGJiZjdhNzk3ODFkZCIsInZlcnNpb24iOjF9.JQbXPplJfzTCXHZrnbQLcSKZJrxvqYQh-BlpYlVMsWL6SSAKAlCb7Srqeoxr6u7byLm7QtufHYUKda7b1dKECw
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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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