---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-ner
  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: 0.9300033411293017
    - name: Recall
      type: recall
      value: 0.9368899360484685
    - name: F1
      type: f1
      value: 0.9334339369550636
    - name: Accuracy
      type: accuracy
      value: 0.9889023013122542
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert-ner

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0454
- Precision: 0.9300
- Recall: 0.9369
- F1: 0.9334
- Accuracy: 0.9889

## 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 OptimizerNames.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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.182         | 1.0   | 878  | 0.0564          | 0.9023    | 0.9111 | 0.9067 | 0.9843   |
| 0.038         | 2.0   | 1756 | 0.0504          | 0.9253    | 0.9298 | 0.9276 | 0.9876   |
| 0.0208        | 3.0   | 2634 | 0.0454          | 0.9300    | 0.9369 | 0.9334 | 0.9889   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1