Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use mtyrrell/CPU_Mitigation_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtyrrell/CPU_Mitigation_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtyrrell/CPU_Mitigation_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Mitigation_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Mitigation_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 869b7fb8bd34eef13555e08eeb491c4789d3ac376646a286c74f272127f0dc76
- Size of remote file:
- 438 MB
- SHA256:
- d4acb2c32e2e44c9e96a53dc77bb1975ef73d890068fc19fead2e35c34f6c161
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.