---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_d
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_sentiment_multilingual
      type: tweet_sentiment_multilingual
      config: all
      split: validation
      args: all
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6396604938271605
    - name: F1
      type: f1
      value: 0.6384456793550767
---

<!-- 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. -->

# scenario-TCR_data-cardiffnlp_tweet_sentiment_multilingual_all_d

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8506
- Accuracy: 0.6397
- F1: 0.6384

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 53
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.9598        | 1.09  | 500   | 0.8321          | 0.6335   | 0.6229 |
| 0.7983        | 2.17  | 1000  | 0.7922          | 0.6381   | 0.6278 |
| 0.7031        | 3.26  | 1500  | 0.8300          | 0.6520   | 0.6468 |
| 0.6192        | 4.35  | 2000  | 0.8659          | 0.6497   | 0.6443 |
| 0.5472        | 5.43  | 2500  | 0.9646          | 0.6331   | 0.6343 |
| 0.4664        | 6.52  | 3000  | 0.9555          | 0.6485   | 0.6455 |
| 0.4025        | 7.61  | 3500  | 1.0121          | 0.6427   | 0.6405 |
| 0.3568        | 8.7   | 4000  | 1.1016          | 0.6327   | 0.6324 |
| 0.3069        | 9.78  | 4500  | 1.2521          | 0.6408   | 0.6400 |
| 0.2701        | 10.87 | 5000  | 1.3727          | 0.6397   | 0.6372 |
| 0.2398        | 11.96 | 5500  | 1.4539          | 0.6319   | 0.6334 |
| 0.2004        | 13.04 | 6000  | 1.6097          | 0.6420   | 0.6376 |
| 0.1864        | 14.13 | 6500  | 1.6302          | 0.6343   | 0.6349 |
| 0.157         | 15.22 | 7000  | 1.7491          | 0.6381   | 0.6339 |
| 0.1411        | 16.3  | 7500  | 1.8634          | 0.6400   | 0.6392 |
| 0.1318        | 17.39 | 8000  | 2.0229          | 0.6277   | 0.6275 |
| 0.1159        | 18.48 | 8500  | 2.0196          | 0.6385   | 0.6359 |
| 0.1135        | 19.57 | 9000  | 2.1959          | 0.6377   | 0.6368 |
| 0.1018        | 20.65 | 9500  | 2.3238          | 0.6323   | 0.6320 |
| 0.0888        | 21.74 | 10000 | 2.3449          | 0.6339   | 0.6341 |
| 0.0797        | 22.83 | 10500 | 2.4967          | 0.6354   | 0.6338 |
| 0.0828        | 23.91 | 11000 | 2.5070          | 0.6358   | 0.6362 |
| 0.0675        | 25.0  | 11500 | 2.5895          | 0.6381   | 0.6393 |
| 0.067         | 26.09 | 12000 | 2.6730          | 0.6370   | 0.6372 |
| 0.0566        | 27.17 | 12500 | 2.7454          | 0.6377   | 0.6386 |
| 0.0571        | 28.26 | 13000 | 2.7673          | 0.6420   | 0.6413 |
| 0.048         | 29.35 | 13500 | 2.8506          | 0.6397   | 0.6384 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3