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README.md

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----
-license: apache-2.0
----
+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+  - token-classification
+  - ner
+  - modernbert
+datasets:
+  - eriktks/conll2003
+metrics:
+  - precision
+  - recall
+  - f1
+  - accuracy
+model-index:
+  - name: ModernBERT-large-conll2003-ner
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          name: CoNLL-2003
+          type: eriktks/conll2003
+          split: test
+        metrics:
+          - name: Precision
+            type: precision
+            value: 0.8721
+          - name: Recall
+            type: recall
+            value: 0.8985
+          - name: F1
+            type: f1
+            value: 0.8851
+          - name: Accuracy
+            type: accuracy
+            value: 0.9711
+base_model:
+  - answerdotai/ModernBERT-base
+---
+
+# ModernBERT-large Fine-tuned on CoNLL-2003 for NER
+
+This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [CoNLL-2003](https://huggingface.co/datasets/eriktks/conll2003) dataset for Named Entity Recognition (NER).
+
+ModernBERT's architecture allows for efficient processing of long sequences and features optimized attention mechanisms, making it an excellent backbone for dense token-classification tasks like NER.
+
+## Model Description
+
+- **Developed by:** Rúben Garrido
+- **Model type:** ModernBERT (Encoder-only Transformer)
+- **Task:** Named Entity Recognition (NER)
+- **Labels:** O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC
+
+## Intended Uses & Limitations
+
+This model is intended for identifying entities (Persons, Organizations, Locations, and Miscellaneous) in English text.
+
+### How to use
+
+```python
+from transformers import pipeline
+
+ner_pipeline = pipeline("ner", model="RGarrido03/modernbert-conll2003-ner", aggregation_strategy="simple")
+text = "The CERN headquarters are located in Geneva, Switzerland."
+results = ner_pipeline(text)
+
+for entity in results:
+    print(f"Entity: {entity['word']}, Label: {entity['entity_group']}, Score: {entity['score']:.4f}")
+```
+
+## Training Data
+
+The model was trained on the **CoNLL-2003** dataset, which consists of Reuters news stories from 1996 and 1997.
+
+- **Train samples:** 14,041
+- **Validation samples:** 3,250
+- **Test samples:** 3,453
+
+## Training Procedure
+
+### Training Hyperparameters
+
+The following hyperparameters were used during training:
+
+- **Learning rate:** 5e-5 (with AdamW optimizer)
+- **Batch size:** 8
+- **Epochs:** 3.0
+- **Weight decay:** 0.01
+- **Warmup ratio:** 0.1
+- **Max sequence length:** 256
+- **Label all tokens:** True (subword pieces inherit parent labels)
+
+### Training Results (Evaluation on Test Split)
+
+| Metric        | Value  |
+| :------------ | :----- |
+| **Accuracy**  | 0.9711 |
+| **F1 Score**  | 0.8851 |
+| **Precision** | 0.8721 |
+| **Recall**    | 0.8985 |
+| **Loss**      | 0.1873 |
+
+## Evaluation on Validation Split
+
+| Metric        | Value  |
+| :------------ | :----- |
+| **Accuracy**  | 0.9871 |
+| **F1 Score**  | 0.9416 |
+| **Precision** | 0.9357 |
+| **Recall**    | 0.9475 |
+| **Loss**      | 0.0625 |
+
+## Environmental Impact
+
+- **Runtime:** ~11.5 minutes (694 seconds)
+- **Hardware:** MacBook Pro, M5 Pro 24GB (Training speed: ~62 samples/sec)
+
+## Citation
+
+If you use this model, please cite the original CoNLL-2003 paper and the ModernBERT work.
+
+```bibtex
+@inproceedings{tjong-kim-sang-de-meulder-2003-introduction,
+    title = "Introduction to the {CoNLL}-2003 Shared Task: Language-Independent Named Entity Recognition",
+    author = "Tjong Kim Sang, Erik F.  and De Meulder, Fien",
+    booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003",
+    year = "2003",
+    url = "https://aclanthology.org/W03-0419",
+    pages = "142--147",
+}
+```