---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_Test_Training
  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: 0.9508227550540668
    - name: Recall
      type: recall
      value: 0.955043445409898
    - name: F1
      type: f1
      value: 0.9529284267068746
    - name: Accuracy
      type: accuracy
      value: 0.9880181645239483
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# RoBERTa_Test_Training

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: 0.0590
- Precision: 0.9508
- Recall: 0.9550
- F1: 0.9529
- Accuracy: 0.9880

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0803        | 1.0   | 1756 | 0.0725          | 0.9236    | 0.9313 | 0.9274 | 0.9820   |
| 0.0373        | 2.0   | 3512 | 0.0627          | 0.9453    | 0.9487 | 0.9470 | 0.9868   |
| 0.0213        | 3.0   | 5268 | 0.0590          | 0.9508    | 0.9550 | 0.9529 | 0.9880   |


### Framework versions

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