Text Classification
Transformers
Safetensors
English
modernbert
llm-as-a-judge
rag
hallucination-detection
faithfulness
nli
fact-checking
calibration
typed-decisions
Instructions to use Nefelim584/jev-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nefelim584/jev-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nefelim584/jev-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nefelim584/jev-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder/tokenizer_config.json from Nefelim584/jev-lite: direct link, hf CLI and curl.
- Browser
- Download file 380 Bytes
-
https://huggingface.co/Nefelim584/jev-lite/resolve/main/encoder/tokenizer_config.json
- Command line
-
hf download hf://Nefelim584/jev-lite/encoder/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Nefelim584/jev-lite/resolve/main/encoder/tokenizer_config.json
380 Bytes
| { | |
| "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": 8192, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |