Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
English
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use JeremiahZ/bert-base-uncased-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JeremiahZ/bert-base-uncased-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JeremiahZ/bert-base-uncased-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JeremiahZ/bert-base-uncased-cola") model = AutoModelForSequenceClassification.from_pretrained("JeremiahZ/bert-base-uncased-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07704e8299c7dc188ea45950cf228086aa9bbe16794e2100f5053689d8c6502d
- Size of remote file:
- 438 MB
- SHA256:
- 3d921510e9f58e7cf44e2d248902db1fc82970ac09e5fe83c02d77b2a1bfd816
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