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 training_args.bin from manueldeprada/FactCC: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
-
https://huggingface.co/manueldeprada/FactCC/resolve/283e3fdb775725a474b1b14fdac8b221ac3abf1f/training_args.bin
- Command line
-
hf download hf://manueldeprada/FactCC@283e3fdb775725a474b1b14fdac8b221ac3abf1f/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/manueldeprada/FactCC/resolve/283e3fdb775725a474b1b14fdac8b221ac3abf1f/training_args.bin
1.36 kB
- Xet hash:
- 4da3440067880c6cfebc1b4ed8c8b0ade931da4b9c3619cde432f56ddf87b3f0
- Size of remote file:
- 1.36 kB
- SHA256:
- 1ac4af6a27b31322442accb8768b48b00242356b8682d11ec2176baf9b811940
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