Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use mtyrrell/CPU_Economywide_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtyrrell/CPU_Economywide_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mtyrrell/CPU_Economywide_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Economywide_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Economywide_Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b2687019571df6196a8d096da918951c2d39bfcb4e4465f0ed4ae4326b989c0
- Size of remote file:
- 438 MB
- SHA256:
- 58c6e41954ae582a57f5e0f30ff073ca7b1557d207276540597ee80b7c46a6be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.