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:
- 78fdb5ce6de46f0214232df7826a9794e7d4684a051ae0995f1af85f9e326ee0
- Size of remote file:
- 4.03 kB
- SHA256:
- a8efde9c132e353676ba8c9537a50f9c21b512b49479fd764beb26d150ca3d13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.