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")# 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
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 128, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 512, | |
| "is_decoder": false, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 2048, | |
| "model_type": "bert", | |
| "num_attention_heads": 2, | |
| "num_hidden_layers": 8, | |
| "pad_token_id": 1, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "type_vocab_size": 2, | |
| "use_cache": false, | |
| "vocab_size": 32000 | |
| } | |