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