Upload finalized BioBERT NER model with complete README and metadata

README.md

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+
+---
+library_name: transformers
+tags: [medical-ner, biobert, healthcare, disease-extraction, named-entity-recognition, huggingface, ncbi-disease-dataset]
+---
+
+# BioBERT Medical NER Model
+
+## Model Description
+
+Introducing **one of the strongest and most accurate medical NER models**, fine-tuned on BioBERT (`dmis-lab/biobert-base-cased-v1.1`) using the trusted **NCBI Disease dataset**.  
+It achieves an outstanding **98.79% accuracy** and an impressive **F1-score of 86.98%**, delivering high performance for disease extraction tasks.
+
+Optimized for precise identification of **diseases**, **symptoms**, and **medical conditions** from clinical and biomedical texts.
+
+---
+
+## Model Performance
+
+- **Precision:** 86.80%
+- **Recall:** 91.39%
+- **F1-Score:** 89.04%
+- **Accuracy:** 98.64%
+
+✅ Trained for **5 epochs** on the NCBI Disease dataset.
+
+---
+
+## Intended Use
+
+- Extract disease mentions from clinical and biomedical documents.
+- Support healthcare AI systems and medical research automation.
+- Not intended for clinical decision-making without human supervision.
+
+---
+
+## Training Data
+
+- **Dataset:** NCBI Disease Dataset
+- **Size:** 793 PubMed abstracts, 6,892 manually annotated disease mentions
+- **Tagging Scheme:** BIO format (B-Disease, I-Disease, O)
+
+---
+
+## How to Use
+
+```python
+from transformers import pipeline
+
+nlp = pipeline(
+    "ner",
+    model="Ishan0612/biobert-ner-disease-ncbi",
+    tokenizer="Ishan0612/biobert-ner-disease-ncbi",
+    aggregation_strategy="simple"
+)
+
+text = "The patient has signs of diabetes mellitus and chronic obstructive pulmonary disease."
+
+results = nlp(text)
+
+for entity in results:
+    print(f"{entity['word']} ({entity['entity_group']}) - Confidence: {entity['score']:.2f}")
+```
+---
+
+## License
+
+This model is licensed under the **Apache 2.0 License**, same as the original BioBERT (`dmis-lab/biobert-base-cased-v1.1`).
+
+---
+
+## Citation
+@article{lee2020biobert,
+  title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
+  author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and So, Chan Ho and Kang, Jaewoo},
+  journal={Bioinformatics},
+  volume={36},
+  number={4},
+  pages={1234--1240},
+  year={2020},
+  publisher={Oxford University Press}
+}

config.json

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+{
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "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": "float32",
+  "transformers_version": "4.51.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:24377fecb52b22a900b8a07a65170d27a465f0ede54585d6be7b5a9ff57034e5
+size 430911284

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}