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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: answerdotai/ModernBERT-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- conll2003 |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: modernbert-ner-conll2003 |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: conll2003 |
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type: conll2003 |
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config: conll2003 |
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split: validation |
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args: conll2003 |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8349195930423368 |
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- name: Recall |
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type: recall |
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value: 0.856277347694379 |
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- name: F1 |
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type: f1 |
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value: 0.8454636091724825 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9751567306569059 |
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language: |
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- en |
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pipeline_tag: token-classification |
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--- |
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# ModernBERT NER (CoNLL2003) |
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This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the conll2003 dataset for Named Entity Recognition (NER). |
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Robust performance on tasks involving the recognition of `Persons`, `Organizations`, and `Locations`. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0992 |
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- Precision: 0.8349 |
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- Recall: 0.8563 |
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- F1: 0.8455 |
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- Accuracy: 0.9752 |
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## Model Details |
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- **Base Model:** ModernBERT: [https://doi.org/10.48550/arXiv.2412.13663](https://doi.org/10.48550/arXiv.2412.13663). |
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- **Fine-tuning Dataset:** CoNLL2003: [https://huggingface.co/datasets/eriktks/conll2003](https://huggingface.co/datasets/eriktks/conll2003). |
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- **Task:** Named Entity Recognition (NER) |
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## Training Data |
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The model is fine-tuned on the CoNLL2003 dataset, a well-known benchmark for NER. |
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This dataset provides a solid foundation for the model to generalize on general English text. |
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## Example Usage |
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Below is an example of how to use the model with the Hugging Face Transformers library: |
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```python |
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from transformers import pipeline |
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ner = pipeline(task="token-classification", model="IsmaelMousa/modernbert-ner-conll2003", aggregation_strategy="max") |
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results = ner("Hi, I'm Ismael Mousa from Palestine working for NVIDIA inc.") |
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for entity in results: |
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for key, value in entity.items(): |
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if key == "entity_group": |
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print(f"{entity['word']} => {entity[key]}") |
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``` |
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Results: |
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``` |
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Ismael Mousa => PER |
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Palestine => LOC |
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NVIDIA => ORG |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.2306 | 1.0 | 1756 | 0.2243 | 0.6074 | 0.6483 | 0.6272 | 0.9406 | |
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| 0.1415 | 2.0 | 3512 | 0.1583 | 0.7258 | 0.7536 | 0.7394 | 0.9583 | |
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| 0.1143 | 3.0 | 5268 | 0.1335 | 0.7731 | 0.7989 | 0.7858 | 0.9657 | |
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| 0.0913 | 4.0 | 7024 | 0.1145 | 0.7958 | 0.8256 | 0.8104 | 0.9699 | |
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| 0.0848 | 5.0 | 8780 | 0.1079 | 0.8120 | 0.8408 | 0.8261 | 0.9720 | |
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| 0.0728 | 6.0 | 10536 | 0.1036 | 0.8214 | 0.8452 | 0.8331 | 0.9730 | |
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| 0.0623 | 7.0 | 12292 | 0.1032 | 0.8258 | 0.8487 | 0.8371 | 0.9737 | |
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| 0.0599 | 8.0 | 14048 | 0.0990 | 0.8289 | 0.8527 | 0.8406 | 0.9745 | |
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| 0.0558 | 9.0 | 15804 | 0.0998 | 0.8331 | 0.8541 | 0.8434 | 0.9750 | |
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| 0.0559 | 10.0 | 17560 | 0.0992 | 0.8349 | 0.8563 | 0.8455 | 0.9752 | |
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### Framework versions |
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- Transformers 4.48.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
