---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-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: 0.9306692773228907
    - name: Recall
      type: recall
      value: 0.9381841019199713
    - name: F1
      type: f1
      value: 0.9344115807345187
    - name: Accuracy
      type: accuracy
      value: 0.9832666156472597
---

<!-- 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. -->

# distilbert-base-cased-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: 0.1183
- Precision: 0.9307
- Recall: 0.9382
- F1: 0.9344
- Accuracy: 0.9833

## 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: 8
- eval_batch_size: 8
- seed: 2147483647
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1081        | 1.0   | 1756  | 0.0963          | 0.8947    | 0.8982 | 0.8964 | 0.9742   |
| 0.0518        | 2.0   | 3512  | 0.0780          | 0.9219    | 0.9182 | 0.9200 | 0.9803   |
| 0.0348        | 3.0   | 5268  | 0.0833          | 0.9258    | 0.9271 | 0.9264 | 0.9819   |
| 0.0268        | 4.0   | 7024  | 0.0900          | 0.9152    | 0.9241 | 0.9196 | 0.9805   |
| 0.0167        | 5.0   | 8780  | 0.0929          | 0.9225    | 0.9320 | 0.9272 | 0.9822   |
| 0.0071        | 6.0   | 10536 | 0.1119          | 0.9229    | 0.9270 | 0.9249 | 0.9816   |
| 0.0056        | 7.0   | 12292 | 0.1073          | 0.9286    | 0.9366 | 0.9326 | 0.9832   |
| 0.0021        | 8.0   | 14048 | 0.1194          | 0.9285    | 0.9350 | 0.9318 | 0.9829   |
| 0.0019        | 9.0   | 15804 | 0.1156          | 0.9318    | 0.9376 | 0.9347 | 0.9833   |
| 0.0011        | 10.0  | 17560 | 0.1183          | 0.9307    | 0.9382 | 0.9344 | 0.9833   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3