---
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_a
  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: 0.6439043209876543
    - name: F1
      type: f1
      value: 0.6443757148090576
---

<!-- 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_a

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: 2.6822
- Accuracy: 0.6439
- F1: 0.6444

## 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: 1234
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.9412        | 1.09  | 500   | 0.8062          | 0.6389   | 0.6335 |
| 0.7943        | 2.17  | 1000  | 0.8448          | 0.6451   | 0.6394 |
| 0.7026        | 3.26  | 1500  | 0.8509          | 0.6497   | 0.6438 |
| 0.6019        | 4.35  | 2000  | 0.8999          | 0.6478   | 0.6468 |
| 0.5379        | 5.43  | 2500  | 0.9424          | 0.6312   | 0.6222 |
| 0.4635        | 6.52  | 3000  | 1.0401          | 0.6431   | 0.6439 |
| 0.3985        | 7.61  | 3500  | 1.0584          | 0.6397   | 0.6390 |
| 0.3506        | 8.7   | 4000  | 1.1607          | 0.6443   | 0.6432 |
| 0.3105        | 9.78  | 4500  | 1.1806          | 0.6408   | 0.6423 |
| 0.2712        | 10.87 | 5000  | 1.3112          | 0.6316   | 0.6304 |
| 0.2361        | 11.96 | 5500  | 1.3772          | 0.6466   | 0.6454 |
| 0.2111        | 13.04 | 6000  | 1.4492          | 0.6385   | 0.6396 |
| 0.1885        | 14.13 | 6500  | 1.6604          | 0.6335   | 0.6347 |
| 0.1658        | 15.22 | 7000  | 1.7153          | 0.6358   | 0.6353 |
| 0.1501        | 16.3  | 7500  | 1.7849          | 0.6412   | 0.6427 |
| 0.135         | 17.39 | 8000  | 1.9749          | 0.6416   | 0.6394 |
| 0.1217        | 18.48 | 8500  | 2.0530          | 0.6439   | 0.6431 |
| 0.1112        | 19.57 | 9000  | 2.1378          | 0.6439   | 0.6448 |
| 0.1018        | 20.65 | 9500  | 2.2656          | 0.6393   | 0.6390 |
| 0.0885        | 21.74 | 10000 | 2.3568          | 0.6431   | 0.6438 |
| 0.0897        | 22.83 | 10500 | 2.3852          | 0.6435   | 0.6446 |
| 0.0854        | 23.91 | 11000 | 2.4019          | 0.6327   | 0.6329 |
| 0.0734        | 25.0  | 11500 | 2.5260          | 0.6331   | 0.6333 |
| 0.067         | 26.09 | 12000 | 2.5368          | 0.6470   | 0.6465 |
| 0.0546        | 27.17 | 12500 | 2.6255          | 0.6431   | 0.6441 |
| 0.0581        | 28.26 | 13000 | 2.6467          | 0.6458   | 0.6456 |
| 0.0564        | 29.35 | 13500 | 2.6822          | 0.6439   | 0.6444 |


### Framework versions

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