Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
protein-recognition
gene-recognition
molecular-biology
genomics
dna
rna
cell_line
cell_type
protein
Instructions to use OpenMed/OpenMed-NER-DNADetect-TinyMed-66M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DNADetect-TinyMed-66M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DNADetect-TinyMed-66M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DNADetect-TinyMed-66M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DNADetect-TinyMed-66M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 990 Bytes
b074ffc | 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 | {
"activation": "gelu",
"architectures": [
"DistilBertForTokenClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "O",
"1": "B-DNA",
"2": "B-RNA",
"3": "B-cell_line",
"4": "B-cell_type",
"5": "B-protein",
"6": "I-DNA",
"7": "I-RNA",
"8": "I-cell_line",
"9": "I-cell_type",
"10": "I-protein"
},
"initializer_range": 0.02,
"label2id": {
"B-DNA": 1,
"B-RNA": 2,
"B-cell_line": 3,
"B-cell_type": 4,
"B-protein": 5,
"I-DNA": 6,
"I-RNA": 7,
"I-cell_line": 8,
"I-cell_type": 9,
"I-protein": 10,
"O": 0
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.2",
"vocab_size": 30522
}
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