Instructions to use OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir OpenMed-NER-GenomicDetect-BioClinical-108M-mlx OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 1,331 Bytes
d81dbc8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"transformers_version": "5.5.0",
"architectures": [
"BertForTokenClassification"
],
"output_hidden_states": false,
"return_dict": true,
"dtype": "bfloat16",
"chunk_size_feed_forward": 0,
"is_encoder_decoder": false,
"id2label": {
"0": "B-Cell-line-name",
"1": "I-Cell-line-name",
"2": "O"
},
"label2id": {
"B-Cell-line-name": 0,
"I-Cell-line-name": 1,
"O": 2
},
"problem_type": null,
"vocab_size": 28996,
"hidden_size": 768,
"num_hidden_layers": 12,
"num_attention_heads": 12,
"intermediate_size": 3072,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.2,
"attention_probs_dropout_prob": 0.2,
"max_position_embeddings": 512,
"type_vocab_size": 2,
"initializer_range": 0.02,
"layer_norm_eps": 1e-07,
"pad_token_id": 0,
"use_cache": true,
"classifier_dropout": 0.2,
"is_decoder": false,
"add_cross_attention": false,
"bos_token_id": null,
"eos_token_id": null,
"tie_word_embeddings": true,
"_name_or_path": "OpenMed/OpenMed-NER-GenomicDetect-BioClinical-108M",
"model_type": "bert",
"position_embedding_type": "absolute",
"output_attentions": false,
"_mlx_task": "token-classification",
"_mlx_family": "bert",
"_mlx_position_offset": 0,
"_mlx_model_type": "bert",
"num_labels": 3,
"_mlx_weights_format": "safetensors"
} |