Instructions to use kumakur/intentclassificationcommand-llm-jp-modernbert-base-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumakur/intentclassificationcommand-llm-jp-modernbert-base-sft with Transformers:
# Load model directly from transformers import AutoTokenizer, ModernBertForIntentClassificationCommand tokenizer = AutoTokenizer.from_pretrained("kumakur/intentclassificationcommand-llm-jp-modernbert-base-sft") model = ModernBertForIntentClassificationCommand.from_pretrained("kumakur/intentclassificationcommand-llm-jp-modernbert-base-sft") - Notebooks
- Google Colab
- Kaggle
File size: 1,156 Bytes
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"architectures": [
"ModernBertForIntentClassificationCommand"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 5,
"classifier_activation": "gelu",
"classifier_bias": false,
"classifier_dropout": 0.0,
"classifier_pooling": "cls",
"cls_token_id": 5,
"decoder_bias": true,
"deterministic_flash_attn": false,
"embedding_dropout": 0.0,
"eos_token_id": 6,
"global_attn_every_n_layers": 3,
"global_rope_theta": 10000.0,
"hidden_activation": "gelu",
"hidden_size": 768,
"initializer_cutoff_factor": 2.0,
"initializer_range": 0.02,
"intermediate_size": 1152,
"local_attention": 128,
"local_rope_theta": 10000.0,
"max_position_embeddings": 8192,
"mlp_bias": false,
"mlp_dropout": 0.0,
"model_type": "modernbert",
"norm_bias": false,
"norm_eps": 1e-05,
"num_attention_heads": 12,
"num_hidden_layers": 22,
"pad_token_id": 4,
"repad_logits_with_grad": false,
"sep_token_id": 6,
"sparse_pred_ignore_index": -100,
"sparse_prediction": false,
"torch_dtype": "float32",
"transformers_version": "4.52.4",
"vocab_size": 99574
}
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