Create README.md

README.md

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+---
+license: mit
+datasets:
+- ontonotes/conll2012_ontonotesv5
+language:
+- en
+base_model:
+- FacebookAI/roberta-large
+pipeline_tag: token-classification
+---
+
+# RoBERTa-large fine-tuned on OntoNotes 5.0
+
+This model is a fine-tuned version of [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the English subset of the **OntoNotes 5.0** (CoNLL-2012) dataset. RoBERTa-large features 24 layers and ~355M parameters, providing enhanced semantic understanding for complex Named Entity Recognition (NER) tasks compared to the base architecture.
+
+## 📊 Performance
+The following results were achieved on the OntoNotes 5.0 (v12) test set:
+
+| **Entity** | **Precision** | **Recall** | **F1-Score** | **Support** |
+| :--- | :---: | :---: | :---: | :---: |
+| CARDINAL | 0.7769 | 0.7900 | 0.7834 | 1005 |
+| DATE | 0.8211 | 0.8533 | 0.8369 | 1786 |
+| EVENT | 0.5702 | 0.7647 | 0.6533 | 85 |
+| FAC | 0.7123 | 0.6980 | 0.7051 | 149 |
+| GPE | 0.9262 | 0.9470 | 0.9365 | 2546 |
+| LANGUAGE | 0.7500 | 0.6818 | 0.7143 | 22 |
+| LAW | 0.5000 | 0.6364 | 0.5600 | 44 |
+| LOC | 0.6597 | 0.7302 | 0.6932 | 215 |
+| MONEY | 0.8730 | 0.9099 | 0.8910 | 355 |
+| NORP | 0.9029 | 0.9485 | 0.9251 | 990 |
+| ORDINAL | 0.6936 | 0.7874 | 0.7376 | 207 |
+| ORG | 0.8870 | 0.9101 | 0.8984 | 2002 |
+| PERCENT | 0.8703 | 0.9066 | 0.8881 | 407 |
+| PERSON | 0.9250 | 0.9246 | 0.9248 | 2134 |
+| PRODUCT | 0.7356 | 0.7111 | 0.7232 | 90 |
+| QUANTITY | 0.6933 | 0.6797 | 0.6865 | 153 |
+| TIME | 0.6211 | 0.6267 | 0.6239 | 225 |
+| WORK_OF_ART | 0.6686 | 0.6923 | 0.6802 | 169 |
+| **micro avg** | **0.8581** | **0.8831** | **0.8704** | **12584** |
+| **macro avg** | **0.7548** | **0.7888** | **0.7701** | **12584** |
+| **weighted avg** | **0.8596** | **0.8831** | **0.8710** | **12584** |
+
+## 🛠 Training Details
+To optimize the 24-layer transformer on 2xNVIDIA V100 GPUs:
+- **Architecture**: `RobertaForTokenClassification`
+- **Tokenizer**: `RobertaTokenizerFast` (with `add_prefix_space=True`)
+- **Learning Rate**: 1e-5
+- **Effective Batch Size**: 32 (4 per device × 4 gradient accumulation steps)
+- **Epochs**: 5
+- **Warmup Ratio**: 0.1
+- **Mixed Precision**: FP16 enabled
+- **Optimizer**: AdamW with `weight_decay=0.01`
+
+## 📂 Project Assets
+- **GitHub Repository**: [Learnrr/ontonotes5_ner_evaluation](https://github.com/Learnrr/ontonotes5_ner_evaluation.git)
+
+| **Asset** | **File** | **Description** |
+| :--- | :--- | :--- |
+| **Model Weights** | `model.safetensors` | Fine-tuned Large weights (~1.42 GB). |
+| **Configuration** | `config.json` | 24-layer configuration and `id2label` map. |
+| **Vocabulary** | `vocab.json` / `merges.txt` | BPE vocabulary and byte-level merge rules. |
+| **Tokenizer** | `tokenizer.json` / `tokenizer_config.json` | Complete fast tokenizer setup. |
+| **Special Tokens** | `special_tokens_map.json` | Definitions for BOS, EOS, and Padding tokens. |
+| **Training Args** | `training_args.bin` | Hyperparameters used during the training run. |
+
+## 🚀 Usage
+```python
+from transformers import pipeline
+
+model_checkpoint = "learnrr/roberta-large-ontonotes5-ner"
+token_classifier = pipeline(
+    "token-classification", 
+    model=model_checkpoint, 
+    aggregation_strategy="simple"
+)
+
+text = "The United Nations is headquartered in New York City."
+results = token_classifier(text)
+
+for entity in results:
+    print(f"Entity: {entity['word']} | Label: {entity['entity_group']} | Score: {entity['score']:.4f}")
+```