Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-PharmaDetect-SuperMedical-125M
0d92d82 verified Download config.json from OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M: direct link, hf CLI and curl.
- Browser
- Download file 786 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M/resolve/main/config.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M/config.json
-
curl -L -o config.json https://huggingface.co/OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-125M/resolve/main/config.json
786 Bytes
| { | |
| "architectures": [ | |
| "RobertaForTokenClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.2, | |
| "bos_token_id": 0, | |
| "classifier_dropout": 0.2, | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.2, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "O", | |
| "1": "B-CHEM", | |
| "2": "I-CHEM" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "B-CHEM": 1, | |
| "I-CHEM": 2, | |
| "O": 0 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.51.2", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |