Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Cheng98/bert-large-qnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cheng98/bert-large-qnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Cheng98/bert-large-qnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Cheng98/bert-large-qnli") model = AutoModelForSequenceClassification.from_pretrained("Cheng98/bert-large-qnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Upload folder using huggingface_hub
Verified signature56132f6
-
Upload ./trainer_state.json with huggingface_hub
Verified signaturedca570c -
Upload ./train_results.json with huggingface_hub
Verified signature1a51a04 -
Upload ./all_results.json with huggingface_hub
Verified signature6279f72 -
Upload ./eval_results.json with huggingface_hub
Verified signature9b8c03f -
Upload folder using huggingface_hub
Verified signature39a05b5 -
Upload ./pytorch_model.bin with huggingface_hub
Verified signaturef03b26d -
Upload tokenizer
Verified signature7c28973 -
Upload config
Verified signature795e8de
-
End of training
ca512e1 -
update model card README.md
28c65dc -
Model save
e58f4c5 -
initial commit
Verified signaturec6e87a0