---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: SEMEVAL23_TASK3_SUBTASK1_MULTI
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# SEMEVAL23_TASK3_SUBTASK1_MULTI

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co./xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6313
- F1: 0.6299

## 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.9669        | 1.0   | 160  | 0.9574          | 0.4075 |
| 0.4214        | 2.0   | 320  | 0.6809          | 0.5769 |
| 0.0096        | 3.0   | 480  | 1.3114          | 0.4152 |
| 0.2681        | 4.0   | 640  | 0.7792          | 0.6122 |
| 0.0007        | 5.0   | 800  | 1.3213          | 0.5765 |
| 0.0005        | 6.0   | 960  | 1.7983          | 0.5749 |
| 0.0011        | 7.0   | 1120 | 2.2000          | 0.5298 |
| 0.0008        | 8.0   | 1280 | 1.3757          | 0.5812 |
| 0.0007        | 9.0   | 1440 | 1.5493          | 0.5990 |
| 0.001         | 10.0  | 1600 | 1.4796          | 0.6233 |
| 0.0008        | 11.0  | 1760 | 1.4954          | 0.6251 |
| 0.0002        | 12.0  | 1920 | 1.6313          | 0.6299 |
| 0.0004        | 13.0  | 2080 | 1.5037          | 0.6296 |
| 0.0008        | 14.0  | 2240 | 1.5526          | 0.6277 |
| 0.0001        | 15.0  | 2400 | 1.5745          | 0.6254 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3