---
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_10
  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: 0.9443059019118869
    - name: Recall
      type: recall
      value: 0.9559071019858634
    - name: F1
      type: f1
      value: 0.9500710880655683
    - name: Accuracy
      type: accuracy
      value: 0.9882329477463103
---

<!-- 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_10

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: 0.0906
- Precision: 0.9443
- Recall: 0.9559
- F1: 0.9501
- Accuracy: 0.9882

## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0839        | 1.0   | 1756  | 0.0705          | 0.9055    | 0.9303 | 0.9177 | 0.9827   |
| 0.0454        | 2.0   | 3512  | 0.0690          | 0.9257    | 0.9431 | 0.9343 | 0.9853   |
| 0.0272        | 3.0   | 5268  | 0.0590          | 0.9310    | 0.9495 | 0.9402 | 0.9865   |
| 0.0183        | 4.0   | 7024  | 0.0803          | 0.9324    | 0.9515 | 0.9419 | 0.9862   |
| 0.0129        | 5.0   | 8780  | 0.0747          | 0.9433    | 0.9517 | 0.9475 | 0.9872   |
| 0.0079        | 6.0   | 10536 | 0.0792          | 0.9359    | 0.9534 | 0.9446 | 0.9874   |
| 0.0055        | 7.0   | 12292 | 0.0785          | 0.9457    | 0.9549 | 0.9503 | 0.9879   |
| 0.003         | 8.0   | 14048 | 0.0881          | 0.9438    | 0.9561 | 0.9499 | 0.9879   |
| 0.001         | 9.0   | 15804 | 0.0875          | 0.9448    | 0.9562 | 0.9505 | 0.9879   |
| 0.0008        | 10.0  | 17560 | 0.0906          | 0.9443    | 0.9559 | 0.9501 | 0.9882   |


### Framework versions

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