Instructions to use bunsenfeng/mfc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunsenfeng/mfc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bunsenfeng/mfc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bunsenfeng/mfc") model = AutoModelForSequenceClassification.from_pretrained("bunsenfeng/mfc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bunsenfeng/mfc: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/bunsenfeng/mfc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bunsenfeng/mfc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bunsenfeng/mfc/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- 3fc10f894b2970ab63d78b058a0fa1c8aae938a15c43698da70d88277fe9296c
- Size of remote file:
- 2.24 GB
- SHA256:
- ce3addded7f7db5c75e39bf3ca7013c5d1bbd03b42b3632f1e25a064e3daf69f
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