---
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-finetuned-pos
  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: 0.9028893991580559
    - name: Recall
      type: recall
      value: 0.91583569886212
    - name: F1
      type: f1
      value: 0.9093164709424872
    - name: Accuracy
      type: accuracy
      value: 0.9267220257724449
---

<!-- 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-finetuned-pos

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: 0.2935
- Precision: 0.9029
- Recall: 0.9158
- F1: 0.9093
- Accuracy: 0.9267

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6485        | 1.0   | 878  | 0.3352          | 0.8911    | 0.9007 | 0.8959 | 0.9159   |
| 0.1997        | 2.0   | 1756 | 0.2890          | 0.9031    | 0.9110 | 0.9070 | 0.9246   |
| 0.1274        | 3.0   | 2634 | 0.2935          | 0.9029    | 0.9158 | 0.9093 | 0.9267   |


### Framework versions

- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0