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
- Xet hash:
- 3b9ac7006f06eef850d17118e6c6eea3d03a0fe6ad71730b5f6edfd94cd6ca0b
- Size of remote file:
- 438 MB
- SHA256:
- 6c1aba07fcd82683fd360910245fa58128e6e46521df1cacc1b1ef8c5b2039d6
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