Instructions to use CodeIsNull/ner-rare-disease-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeIsNull/ner-rare-disease-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CodeIsNull/ner-rare-disease-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CodeIsNull/ner-rare-disease-ner") model = AutoModelForTokenClassification.from_pretrained("CodeIsNull/ner-rare-disease-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 739 Bytes
338602a | 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 | {
"activation": "gelu",
"architectures": [
"DistilBertForTokenClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"id2label": {
"0": "O",
"1": "B-DISEASE",
"2": "I-DISEASE",
"3": "B-GENE",
"4": "I-GENE"
},
"initializer_range": 0.02,
"label2id": {
"B-DISEASE": 1,
"B-GENE": 3,
"I-DISEASE": 2,
"I-GENE": 4,
"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": "float32",
"transformers_version": "4.52.3",
"vocab_size": 30522
}
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