Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-qnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-qnli") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-qnli") - Notebooks
- Google Colab
- Kaggle
Commit History
Librarian Bot: Add base_model information to model (#1) 4dd95e6
update model card README.md 8fb8bad
Jeremiah Zhou commited on
End of training 155cfa4
Jeremiah Zhou commited on
update model card README.md d67774d
Jeremiah Zhou commited on
Model save 76a3d22
Jeremiah Zhou commited on
Training in progress, epoch 3 5b92d09
Jeremiah Zhou commited on
Training in progress, epoch 2 43558ac
Jeremiah Zhou commited on
Training in progress, epoch 1 45e1f54
Jeremiah Zhou commited on
initial commit a4794b0
Jeremiah Zhou commited on