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 tf_model.h5 from manueldeprada/FactCC: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/manueldeprada/FactCC/resolve/cd9b4f9bc87064007d37d420b4236d99d4d554bc/tf_model.h5
- Command line
-
hf download hf://manueldeprada/FactCC@cd9b4f9bc87064007d37d420b4236d99d4d554bc/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/manueldeprada/FactCC/resolve/cd9b4f9bc87064007d37d420b4236d99d4d554bc/tf_model.h5
438 MB
- Xet hash:
- c6b0fbc1e6e1f2bc7b603175785b09d61644bee3b1347765f5572f4ec56a4a14
- Size of remote file:
- 438 MB
- SHA256:
- 4b9af26d2f69aeb1511830e222f2b1a7bc84440395bdcc76f792486e54475a4f
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