Text Classification
Transformers
Safetensors
English
modernbert
hallucination-detection
grounding
factual-consistency
nli
rag
text-embeddings-inference
Instructions to use ENTUM-AI/FactGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ENTUM-AI/FactGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ENTUM-AI/FactGuard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ENTUM-AI/FactGuard") model = AutoModelForSequenceClassification.from_pretrained("ENTUM-AI/FactGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ENTUM-AI/FactGuard: direct link, hf CLI and curl.
- Browser
- Download file 351 Bytes
-
https://huggingface.co/ENTUM-AI/FactGuard/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ENTUM-AI/FactGuard/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ENTUM-AI/FactGuard/resolve/main/tokenizer_config.json
351 Bytes
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 8192, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |