Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ArchitJamb/Result_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArchitJamb/Result_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArchitJamb/Result_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ArchitJamb/Result_Model") model = AutoModelForSequenceClassification.from_pretrained("ArchitJamb/Result_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdd0c2976c4708d65942f76253cc7c4e6ae727f31fff8a223e4e8d26c2827722
- Size of remote file:
- 4.6 kB
- SHA256:
- 82a20eddcb31e9c34e511a8650ed3c3af861f28f753fab1d896a6e7bf7504c2f
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