Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir OpenMed-NER-ChemicalDetect-SuperClinical-434M-mlx OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,501 Bytes
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"transformers_version": "5.5.0",
"architectures": [
"DebertaV2ForTokenClassification"
],
"output_hidden_states": false,
"return_dict": true,
"dtype": "bfloat16",
"chunk_size_feed_forward": 0,
"is_encoder_decoder": false,
"id2label": {
"0": "O",
"1": "B-CHEM",
"2": "I-CHEM"
},
"label2id": {
"B-CHEM": 1,
"I-CHEM": 2,
"O": 0
},
"problem_type": null,
"vocab_size": 128100,
"hidden_size": 1024,
"num_hidden_layers": 24,
"num_attention_heads": 16,
"intermediate_size": 4096,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.2,
"attention_probs_dropout_prob": 0.2,
"max_position_embeddings": 512,
"type_vocab_size": 0,
"initializer_range": 0.02,
"layer_norm_eps": 1e-07,
"relative_attention": true,
"max_relative_positions": -1,
"pad_token_id": 0,
"bos_token_id": null,
"eos_token_id": null,
"position_biased_input": false,
"pos_att_type": [
"p2c",
"c2p"
],
"pooler_dropout": 0,
"pooler_hidden_act": "gelu",
"legacy": true,
"tie_word_embeddings": true,
"pooler_hidden_size": 1024,
"_name_or_path": "OpenMed/OpenMed-NER-ChemicalDetect-SuperClinical-434M",
"model_type": "deberta-v2",
"norm_rel_ebd": "layer_norm",
"position_buckets": 256,
"share_att_key": true,
"output_attentions": false,
"_mlx_task": "token-classification",
"_mlx_family": "deberta-v2",
"_mlx_position_offset": 0,
"_mlx_model_type": "deberta-v2",
"num_labels": 3,
"_mlx_weights_format": "safetensors"
} |