---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_token_classification_AraiEval24_Eng_multi_n_dupl
  results: []
---

<!-- 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_token_classification_AraiEval24_Eng_multi_n_dupl

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6921
- Precision: 0.1617
- Recall: 0.0919
- F1: 0.1172
- Accuracy: 0.6855

## 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.3253        | 1.0   | 617  | 1.3214          | 0.1630    | 0.0115 | 0.0216 | 0.7059   |
| 1.1069        | 2.0   | 1234 | 1.2762          | 0.1354    | 0.0299 | 0.0490 | 0.7012   |
| 0.9809        | 3.0   | 1851 | 1.3347          | 0.1268    | 0.0614 | 0.0827 | 0.6621   |
| 0.8247        | 4.0   | 2468 | 1.4661          | 0.1354    | 0.0572 | 0.0804 | 0.6672   |
| 0.5789        | 5.0   | 3085 | 1.4868          | 0.1434    | 0.0593 | 0.0839 | 0.6698   |
| 0.4944        | 6.0   | 3702 | 1.5318          | 0.1525    | 0.0829 | 0.1074 | 0.6845   |
| 0.445         | 7.0   | 4319 | 1.6190          | 0.1608    | 0.0808 | 0.1076 | 0.6882   |
| 0.4139        | 8.0   | 4936 | 1.6784          | 0.1736    | 0.0945 | 0.1224 | 0.6906   |
| 0.3402        | 9.0   | 5553 | 1.6696          | 0.1599    | 0.0934 | 0.1180 | 0.6813   |
| 0.3125        | 10.0  | 6170 | 1.6921          | 0.1617    | 0.0919 | 0.1172 | 0.6855   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3