---
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-base-conll2003-english-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2003
      type: conll2003
      config: conll2003
      split: test
      args: conll2003
    metrics:
    - name: Precision
      type: precision
      value: 0.7553173672751633
    - name: Recall
      type: recall
      value: 0.7985127478753541
    - name: F1
      type: f1
      value: 0.776314657027283
    - name: Accuracy
      type: accuracy
      value: 0.9627651555938409
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# modernbert-base-conll2003-english-ner

This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1457
- Precision: 0.7553
- Recall: 0.7985
- F1: 0.7763
- Accuracy: 0.9628

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 439  | 0.1737          | 0.6772    | 0.7236 | 0.6996 | 0.9521   |
| 0.2272        | 2.0   | 878  | 0.1518          | 0.7403    | 0.7840 | 0.7615 | 0.9605   |
| 0.1047        | 3.0   | 1317 | 0.1459          | 0.7522    | 0.7937 | 0.7724 | 0.9625   |
| 0.0835        | 4.0   | 1756 | 0.1460          | 0.7514    | 0.7964 | 0.7733 | 0.9626   |
| 0.076         | 5.0   | 2195 | 0.1457          | 0.7553    | 0.7985 | 0.7763 | 0.9628   |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0