---
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_9
  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: 0.9447027565592826
    - name: Recall
      type: recall
      value: 0.9574217435207001
    - name: F1
      type: f1
      value: 0.9510197258441992
    - name: Accuracy
      type: accuracy
      value: 0.9884323893099322
---

<!-- 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_9

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: 0.0841
- Precision: 0.9447
- Recall: 0.9574
- F1: 0.9510
- Accuracy: 0.9884

## 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: 9

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0779        | 1.0   | 1756  | 0.0640          | 0.9142    | 0.9359 | 0.9249 | 0.9836   |
| 0.0448        | 2.0   | 3512  | 0.0867          | 0.9220    | 0.9364 | 0.9291 | 0.9836   |
| 0.03          | 3.0   | 5268  | 0.0580          | 0.9263    | 0.9482 | 0.9371 | 0.9865   |
| 0.018         | 4.0   | 7024  | 0.0760          | 0.9330    | 0.9490 | 0.9409 | 0.9864   |
| 0.0108        | 5.0   | 8780  | 0.0733          | 0.9363    | 0.9544 | 0.9452 | 0.9873   |
| 0.0096        | 6.0   | 10536 | 0.0773          | 0.9413    | 0.9534 | 0.9473 | 0.9879   |
| 0.0039        | 7.0   | 12292 | 0.0755          | 0.9442    | 0.9561 | 0.9501 | 0.9885   |
| 0.0024        | 8.0   | 14048 | 0.0834          | 0.9425    | 0.9567 | 0.9496 | 0.9884   |
| 0.0006        | 9.0   | 15804 | 0.0841          | 0.9447    | 0.9574 | 0.9510 | 0.9884   |


### Framework versions

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