Zero-Shot Classification
Transformers
Safetensors
Arabic
bert
feature-extraction
arabic
prompt-routing
router
encoder
tiny-model
Instructions to use oddadmix/Nawah-Router-BERT-6M-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-Router-BERT-6M-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="oddadmix/Nawah-Router-BERT-6M-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("oddadmix/Nawah-Router-BERT-6M-v2") model = AutoModel.from_pretrained("oddadmix/Nawah-Router-BERT-6M-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from oddadmix/Nawah-Router-BERT-6M-v2: direct link, hf CLI and curl.
- Browser
- Download file 268 Bytes
-
https://huggingface.co/oddadmix/Nawah-Router-BERT-6M-v2/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://oddadmix/Nawah-Router-BERT-6M-v2/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/oddadmix/Nawah-Router-BERT-6M-v2/resolve/main/tokenizer_config.json
268 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 2048, | |
| "pad_token": "<pad>", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>" | |
| } | |