Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 750 Bytes
9ce74ca | 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 | {
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.2,
"classifier_dropout": 0.2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.2,
"hidden_size": 384,
"id2label": {
"0": "O",
"1": "B-Anatomy",
"2": "I-Anatomy"
},
"initializer_range": 0.02,
"intermediate_size": 1536,
"label2id": {
"B-Anatomy": 1,
"I-Anatomy": 2,
"O": 0
},
"layer_norm_eps": 1e-07,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "bfloat16",
"transformers_version": "4.53.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|