---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_conll_epoch_5
  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: 0.937014382542569
    - name: Recall
      type: recall
      value: 0.9538875799394143
    - name: F1
      type: f1
      value: 0.945375698440497
    - name: Accuracy
      type: accuracy
      value: 0.9872971065631616
---

<!-- 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. -->

# RoBERTa_conll_epoch_5

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0716
- Precision: 0.9370
- Recall: 0.9539
- F1: 0.9454
- Accuracy: 0.9873

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0787        | 1.0   | 1756 | 0.0734          | 0.9024    | 0.9317 | 0.9168 | 0.9819   |
| 0.0389        | 2.0   | 3512 | 0.0706          | 0.9359    | 0.9440 | 0.9399 | 0.9854   |
| 0.023         | 3.0   | 5268 | 0.0632          | 0.9340    | 0.9483 | 0.9411 | 0.9864   |
| 0.0137        | 4.0   | 7024 | 0.0762          | 0.9368    | 0.9534 | 0.9450 | 0.9875   |
| 0.0054        | 5.0   | 8780 | 0.0716          | 0.9370    | 0.9539 | 0.9454 | 0.9873   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1