Text Classification
Transformers
Safetensors
distilbert
intent-classification
fitness
health
Eval Results (legacy)
text-embeddings-inference
Instructions to use harshmakwana/distilbert-fitness-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshmakwana/distilbert-fitness-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harshmakwana/distilbert-fitness-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harshmakwana/distilbert-fitness-intent") model = AutoModelForSequenceClassification.from_pretrained("harshmakwana/distilbert-fitness-intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- Accuracy: 0.975
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- Macro F1: 0.9753
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## Model description
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- Accuracy: 0.975
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- Macro F1: 0.9753
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### Training hyperparameters
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The following hyperparameters were used during training:
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