Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use bitdribble/adp_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bitdribble/adp_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bitdribble/adp_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bitdribble/adp_model") model = AutoModelForSequenceClassification.from_pretrained("bitdribble/adp_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3606a721bcecae89c23978742a04a070db58dcbea3f013fa070c1bfcf961155a
- Size of remote file:
- 268 MB
- SHA256:
- 9be1804e495f4cbcefaa8b9dc0c3d448076bba0e6e51d3cc8dde507f41fbd394
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