---
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: 0.9254922831293241
    - name: Recall
      type: recall
      value: 0.9361205813744842
    - name: F1
      type: f1
      value: 0.9307760927743086
    - name: Accuracy
      type: accuracy
      value: 0.9831488785541885
---

<!-- 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: 0.0966
- Precision: 0.9255
- Recall: 0.9361
- F1: 0.9308
- Accuracy: 0.9831

## 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: 5e-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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1045        | 1.0   | 1756 | 0.0891          | 0.8908    | 0.9032 | 0.8970 | 0.9747   |
| 0.044         | 2.0   | 3512 | 0.0809          | 0.9209    | 0.9175 | 0.9192 | 0.9793   |
| 0.0253        | 3.0   | 5268 | 0.0806          | 0.9268    | 0.9280 | 0.9274 | 0.9821   |
| 0.0129        | 4.0   | 7024 | 0.0909          | 0.9301    | 0.9341 | 0.9321 | 0.9829   |
| 0.0042        | 5.0   | 8780 | 0.0966          | 0.9255    | 0.9361 | 0.9308 | 0.9831   |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3