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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mtyrrell/CPU_Economywide_Classifier") model = AutoModelForSequenceClassification.from_pretrained("mtyrrell/CPU_Economywide_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mtyrrell/CPU_Economywide_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/mtyrrell/CPU_Economywide_Classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mtyrrell/CPU_Economywide_Classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mtyrrell/CPU_Economywide_Classifier/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6b2687019571df6196a8d096da918951c2d39bfcb4e4465f0ed4ae4326b989c0
- Size of remote file:
- 438 MB
- SHA256:
- 58c6e41954ae582a57f5e0f30ff073ca7b1557d207276540597ee80b7c46a6be
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