Instructions to use manueldeprada/FactCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manueldeprada/FactCC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="manueldeprada/FactCC")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("manueldeprada/FactCC") model = AutoModelForSequenceClassification.from_pretrained("manueldeprada/FactCC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from manueldeprada/FactCC: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/manueldeprada/FactCC/resolve/b020d9ad4b4a092b2f162158954c13fb5f9fb680/pytorch_model.bin
- Command line
-
hf download hf://manueldeprada/FactCC@b020d9ad4b4a092b2f162158954c13fb5f9fb680/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/manueldeprada/FactCC/resolve/b020d9ad4b4a092b2f162158954c13fb5f9fb680/pytorch_model.bin
438 MB
- Xet hash:
- a2be0ff755e791650d667693d5eacc01e68f98b0a4395f327d7b119c3403f80d
- Size of remote file:
- 438 MB
- SHA256:
- 4b040c69b68537c82a71601e24c75187494699b41fc3d8ae6565cb2678dba9ed
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