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
- Xet hash:
- 4885728681c4046b26c5f745a2b9c4fe59005ba717ae56895269f8a1f2518f5b
- Size of remote file:
- 5.37 kB
- SHA256:
- e2750d6c0e7225ef377d84ac5387d76984adb63c5cc1145eda3c5f93e84f286e
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