---
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
---

<!-- 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-ner-conll2003

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: 0.0992
- Precision: 0.8349
- Recall: 0.8563
- F1: 0.8455
- Accuracy: 0.9752

## 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: 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