---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-cased-finetuned-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: 0.9276294098252555
    - name: Recall
      type: recall
      value: 0.9469875462807136
    - name: F1
      type: f1
      value: 0.9372085276482345
    - name: Accuracy
      type: accuracy
      value: 0.9848119149938188
---

<!-- 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-finetuned-CONLL2003

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: 0.0983
- Precision: 0.9276
- Recall: 0.9470
- F1: 0.9372
- 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0302        | 1.0   | 1756 | 0.0832          | 0.9055    | 0.9318 | 0.9185 | 0.9812   |
| 0.024         | 2.0   | 3512 | 0.0867          | 0.9237    | 0.9387 | 0.9311 | 0.9833   |
| 0.0123        | 3.0   | 5268 | 0.0909          | 0.9224    | 0.9438 | 0.9330 | 0.9845   |
| 0.0059        | 4.0   | 7024 | 0.0962          | 0.9218    | 0.9448 | 0.9332 | 0.9844   |
| 0.0026        | 5.0   | 8780 | 0.0983          | 0.9276    | 0.9470 | 0.9372 | 0.9848   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3