---
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: 0.932077342588002
    - name: Recall
      type: recall
      value: 0.9491753618310333
    - name: F1
      type: f1
      value: 0.940548653381139
    - name: Accuracy
      type: accuracy
      value: 0.984782480720551
---

<!-- 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: 0.1088
- Precision: 0.9321
- Recall: 0.9492
- F1: 0.9405
- Accuracy: 0.9848

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1015        | 1.0   | 1756  | 0.1001          | 0.8858    | 0.9167 | 0.9010 | 0.9740   |
| 0.049         | 2.0   | 3512  | 0.0803          | 0.8993    | 0.9273 | 0.9131 | 0.9798   |
| 0.0327        | 3.0   | 5268  | 0.0794          | 0.9199    | 0.9350 | 0.9274 | 0.9821   |
| 0.0237        | 4.0   | 7024  | 0.0880          | 0.9050    | 0.9344 | 0.9194 | 0.9813   |
| 0.0131        | 5.0   | 8780  | 0.0849          | 0.9178    | 0.9446 | 0.9310 | 0.9837   |
| 0.0073        | 6.0   | 10536 | 0.0975          | 0.9166    | 0.9446 | 0.9304 | 0.9838   |
| 0.0044        | 7.0   | 12292 | 0.0965          | 0.9267    | 0.9475 | 0.9370 | 0.9842   |
| 0.0015        | 8.0   | 14048 | 0.1075          | 0.9273    | 0.9463 | 0.9367 | 0.9843   |
| 0.0011        | 9.0   | 15804 | 0.1089          | 0.9317    | 0.9480 | 0.9398 | 0.9847   |
| 0.0006        | 10.0  | 17560 | 0.1088          | 0.9321    | 0.9492 | 0.9405 | 0.9848   |


### Framework versions

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