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 tokenizer_config.json from manueldeprada/FactCC: direct link, hf CLI and curl.
- Browser
- Download file 174 Bytes
-
https://huggingface.co/manueldeprada/FactCC/resolve/cd9b4f9bc87064007d37d420b4236d99d4d554bc/tokenizer_config.json
- Command line
-
hf download hf://manueldeprada/FactCC@cd9b4f9bc87064007d37d420b4236d99d4d554bc/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/manueldeprada/FactCC/resolve/cd9b4f9bc87064007d37d420b4236d99d4d554bc/tokenizer_config.json
174 Bytes
| {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "model_max_length": 512, "name_or_path": "bert-base-uncased"} |