Text Classification
Transformers
ONNX
Safetensors
English
modernbert
grounding
hallucination-detection
fact-verification
nli
zero-shot-classification
document-ai
cross-encoder
text-embeddings-inference
Instructions to use nutrientdocs/grounding-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nutrientdocs/grounding-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nutrientdocs/grounding-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nutrientdocs/grounding-en") model = AutoModelForSequenceClassification.from_pretrained("nutrientdocs/grounding-en") - Notebooks
- Google Colab
- Kaggle
File size: 379 Bytes
d22bdfa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"backend": "tokenizers",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"tokenizer_class": "TokenizersBackend",
"unk_token": "[UNK]"
}
|