Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ProceduralTree/final-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProceduralTree/final-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ProceduralTree/final-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ProceduralTree/final-model") model = AutoModelForSequenceClassification.from_pretrained("ProceduralTree/final-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 723bfd7eae393c6c761db0b04fd88d9cfaf35b597b322de94b1ef8128c0940b6
- Size of remote file:
- 438 MB
- SHA256:
- 2e95a503e0ac3412caf8304a8af3f1fae4ed01bc3d9d6834d2a7436bc800fac1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.