Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use EdgeAIMed/EdgeAIMed-NER-PharmaDetect-BioMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdgeAIMed/EdgeAIMed-NER-PharmaDetect-BioMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EdgeAIMed/EdgeAIMed-NER-PharmaDetect-BioMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EdgeAIMed/EdgeAIMed-NER-PharmaDetect-BioMed-335M") model = AutoModelForTokenClassification.from_pretrained("EdgeAIMed/EdgeAIMed-NER-PharmaDetect-BioMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 805 Bytes
1c6c401 | 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 | {
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.2,
"bos_token_id": 0,
"classifier_dropout": 0.2,
"eos_token_ids": 0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.2,
"hidden_size": 1024,
"id2label": {
"0": "O",
"1": "B-CHEM",
"2": "I-CHEM"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"B-CHEM": 1,
"I-CHEM": 2,
"O": 0
},
"layer_norm_eps": 1e-07,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"output_past": true,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "bfloat16",
"transformers_version": "4.51.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 30522
}
|