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 flax_model.msgpack from manueldeprada/FactCC: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/manueldeprada/FactCC/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://manueldeprada/FactCC/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/manueldeprada/FactCC/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- 54da53319d0d7ca69cc3cd0b4035d3b41035ea4c62cdd3e462ac7400c856dbdb
- Size of remote file:
- 438 MB
- SHA256:
- 4722609b56ed46485be337cead5c03f71943097a8eef2635174d0eeb9a77a978
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