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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 20269a2999e95bd32f4b9af90bcd6b16f0dc52300ca43ea7567d93ab937922b2
- Size of remote file:
- 3.31 kB
- SHA256:
- 9c614349d8654c5f4a4dd8b740d3acbe93d1860ccebeb427f191541e81099f92
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