Create README.md

README.md

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+---
+license: mit
+datasets:
+- ontonotes/conll2012_ontonotesv5
+language:
+- en
+base_model:
+- google-bert/bert-base-cased
+pipeline_tag: token-classification
+---
+# BERT-base-cased fine-tuned on OntoNotes 5.0
+
+This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the English subset of the **OntoNotes 5.0** (CoNLL-2012) dataset. It is designed for Named Entity Recognition (NER) and can identify 18 types of entities.
+
+## 📊 Performance
+The model achieves the following results on the OntoNotes 5.0 test set:
+
+| **Entity** | **Precision** | **Recall** | **F1-Score** | **Support** |
+| :--- | :---: | :---: | :---: | :---: |
+| CARDINAL | 0.7776 | 0.8070 | 0.7920 | 1005 |
+| DATE | 0.7943 | 0.8628 | 0.8272 | 1786 |
+| EVENT | 0.5000 | 0.6235 | 0.5550 | 85 |
+| FAC | 0.6081 | 0.6040 | 0.6061 | 149 |
+| GPE | 0.9243 | 0.9156 | 0.9199 | 2546 |
+| LANGUAGE | 0.7500 | 0.6818 | 0.7143 | 22 |
+| LAW | 0.5200 | 0.5909 | 0.5532 | 44 |
+| LOC | 0.6478 | 0.7442 | 0.6926 | 215 |
+| MONEY | 0.8760 | 0.9155 | 0.8953 | 355 |
+| NORP | 0.8956 | 0.9182 | 0.9067 | 990 |
+| ORDINAL | 0.7252 | 0.7778 | 0.7506 | 207 |
+| ORG | 0.8621 | 0.8991 | 0.8802 | 2002 |
+| PERCENT | 0.8575 | 0.9017 | 0.8790 | 407 |
+| PERSON | 0.9080 | 0.9161 | 0.9121 | 2134 |
+| PRODUCT | 0.5918 | 0.6444 | 0.6170 | 90 |
+| QUANTITY | 0.7042 | 0.6536 | 0.6780 | 153 |
+| TIME | 0.5906 | 0.6667 | 0.6263 | 225 |
+| WORK_OF_ART | 0.6022 | 0.6450 | 0.6229 | 169 |
+| **micro avg** | **0.8413** | **0.8710** | **0.8559** | **12584** |
+| **macro avg** | **0.7297** | **0.7649** | **0.7460** | **12584** |
+| **weighted avg** | **0.8440** | **0.8710** | **0.8570** | **12584** |
+
+## 🛠 Training Details
+- **Architecture**: `BertForTokenClassification`
+- **Tokenizer**: `BertTokenizerFast` (using `is_split_into_words=True` for alignment)
+- **Epochs**: 5
+- **Learning Rate**: 2e-5
+- **Batch Size**: 16 per device (Total 32 on 2x V100 GPUs)
+- **Max Sequence Length**: 128
+- **Weight Decay**: 0.01
+- **Mixed Precision (FP16)**: Enabled
+
+## 📂 Labels Mapping
+The model was trained with the following label mapping (18 OntoNotes entities + BIO tags):
+`CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART`.
+
+## 📂 Project Assets
+- **GitHub Repository**: https://github.com/Learnrr/ontonotes5_ner_evaluation.git
+| **Asset** | **File** | **Description** |
+| :--- | :--- | :--- |
+| **Model Weights** | `model.safetensors` | Main checkpoint in Safetensors format (safe, fast loading, ~496 MB). |
+| **Configuration** | `config.json` | Model architecture settings and `id2label` entity mapping. |
+| **Vocabulary** | `vocab.json` / `merges.txt` | Byte-level BPE vocabulary files used for tokenization. |
+| **Tokenizer** | `tokenizer.json` | Full fast tokenizer configuration for production inference. |
+| **Special Tokens** | `special_tokens_map.json` | Mapping for tokens like `[CLS]`, `[SEP]`, `<mask>`, etc. |
+| **Training Args** | `training_args.bin` | Binary file containing all hyperparameter settings used during training. |
+
+## 🚀 Usage
+You can use this model directly with a pipeline for token classification:
+```python
+from transformers import pipeline
+
+model_checkpoint = "learnrr/bert-base-ontonotes5-ner"
+token_classifier = pipeline(
+    "token-classification", 
+    model=model_checkpoint, 
+    aggregation_strategy="simple"
+)
+
+text = "Apple was founded by Steve Jobs in Cupertino."
+results = token_classifier(text)
+
+for entity in results:
+    print(f"Entity: {entity['word']} | Label: {entity['entity_group']} | Score: {entity['score']:.4f}")
+```