---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_conll_learning_rate1e4
  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: 0.9345188632208742
    - name: Recall
      type: recall
      value: 0.9463143722652305
    - name: F1
      type: f1
      value: 0.94037963040388
    - name: Accuracy
      type: accuracy
      value: 0.9862998987450523
---

<!-- 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_learning_rate1e4

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: 0.0665
- Precision: 0.9345
- Recall: 0.9463
- F1: 0.9404
- Accuracy: 0.9863

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0909        | 1.0   | 1756 | 0.0778          | 0.8810    | 0.9130 | 0.8967 | 0.9786   |
| 0.0413        | 2.0   | 3512 | 0.0720          | 0.9242    | 0.9337 | 0.9289 | 0.9838   |
| 0.0194        | 3.0   | 5268 | 0.0665          | 0.9345    | 0.9463 | 0.9404 | 0.9863   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1