Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ArchitJamb/Encoded_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArchitJamb/Encoded_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArchitJamb/Encoded_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ArchitJamb/Encoded_Model") model = AutoModelForSequenceClassification.from_pretrained("ArchitJamb/Encoded_Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f809cbf12fff3f838b3b5b16defa6a1e20b987751b28ffdac939cb6e932eb1dd
- Size of remote file:
- 268 MB
- SHA256:
- 12d4ece43d403565689770fa8c42351be3779b6b3aaccbb162b155d2ddc7ffe6
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