Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use fe2plus/bert-fine-tuned-cola with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fe2plus/bert-fine-tuned-cola with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fe2plus/bert-fine-tuned-cola")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fe2plus/bert-fine-tuned-cola") model = AutoModelForSequenceClassification.from_pretrained("fe2plus/bert-fine-tuned-cola", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76236129e3be7ded29f015ddac4009cbc99a9f95c43ab4a3e48ca521943225a3
- Size of remote file:
- 3.52 kB
- SHA256:
- 520eadf663d65a545b08a26a865678100859a83137fd0b2686fd8ea8011ce1db
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.