Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-wnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-wnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-wnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-wnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-wnli") - Notebooks
- Google Colab
- Kaggle
Commit History
Librarian Bot: Add base_model information to model (#1) 2332cf7
update model card README.md 82608e8
Jeremiah Zhou commited on
End of training f4a9b39
Jeremiah Zhou commited on
update model card README.md cc978cd
Jeremiah Zhou commited on
Model save b38cbaa
Jeremiah Zhou commited on
Training in progress, epoch 3 cf2ae4c
Jeremiah Zhou commited on
Training in progress, epoch 1 a3ea2e8
Jeremiah Zhou commited on
initial commit 7dd7112
Jeremiah Zhou commited on