Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use jonas-luehrs/bert-base-uncased-textCLS-RHEOLOGY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonas-luehrs/bert-base-uncased-textCLS-RHEOLOGY with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jonas-luehrs/bert-base-uncased-textCLS-RHEOLOGY")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jonas-luehrs/bert-base-uncased-textCLS-RHEOLOGY") model = AutoModelForSequenceClassification.from_pretrained("jonas-luehrs/bert-base-uncased-textCLS-RHEOLOGY", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
update model card README.md
352c5fa -
End of training
de63ec7 -
Training in progress, epoch 5
7f0d6cb -
Training in progress, epoch 3
ebd9a64 -
Training in progress, epoch 1
22144f2 -
initial commit
e87aa17