---
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_c
  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: 0.6431327160493827
    - name: F1
      type: f1
      value: 0.6424433208447596
---

<!-- 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_c

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: 2.5108
- Accuracy: 0.6431
- F1: 0.6424

## 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: 134
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.9471        | 1.09  | 500   | 0.8205          | 0.6412   | 0.6387 |
| 0.7916        | 2.17  | 1000  | 0.8077          | 0.6474   | 0.6462 |
| 0.6978        | 3.26  | 1500  | 0.8621          | 0.6528   | 0.6534 |
| 0.6176        | 4.35  | 2000  | 0.9091          | 0.6412   | 0.6363 |
| 0.5422        | 5.43  | 2500  | 0.9120          | 0.6454   | 0.6440 |
| 0.4822        | 6.52  | 3000  | 0.9097          | 0.6512   | 0.6469 |
| 0.4117        | 7.61  | 3500  | 1.0223          | 0.6420   | 0.6406 |
| 0.3669        | 8.7   | 4000  | 1.1259          | 0.6404   | 0.6427 |
| 0.3229        | 9.78  | 4500  | 1.2050          | 0.6516   | 0.6489 |
| 0.2797        | 10.87 | 5000  | 1.2616          | 0.6408   | 0.6415 |
| 0.2657        | 11.96 | 5500  | 1.3181          | 0.6435   | 0.6412 |
| 0.226         | 13.04 | 6000  | 1.4459          | 0.6400   | 0.6424 |
| 0.2123        | 14.13 | 6500  | 1.5978          | 0.6389   | 0.6379 |
| 0.1853        | 15.22 | 7000  | 1.6409          | 0.6412   | 0.6438 |
| 0.1759        | 16.3  | 7500  | 1.6756          | 0.6485   | 0.6495 |
| 0.1579        | 17.39 | 8000  | 1.6652          | 0.6412   | 0.6418 |
| 0.1409        | 18.48 | 8500  | 1.9476          | 0.6389   | 0.6384 |
| 0.1282        | 19.57 | 9000  | 2.0246          | 0.6285   | 0.6280 |
| 0.1254        | 20.65 | 9500  | 1.9803          | 0.6412   | 0.6437 |
| 0.1077        | 21.74 | 10000 | 2.0991          | 0.6447   | 0.6429 |
| 0.097         | 22.83 | 10500 | 2.1971          | 0.6424   | 0.6413 |
| 0.0965        | 23.91 | 11000 | 2.2161          | 0.6420   | 0.6387 |
| 0.0859        | 25.0  | 11500 | 2.3387          | 0.6346   | 0.6329 |
| 0.0744        | 26.09 | 12000 | 2.3921          | 0.6466   | 0.6458 |
| 0.0693        | 27.17 | 12500 | 2.4696          | 0.6424   | 0.6428 |
| 0.072         | 28.26 | 13000 | 2.5027          | 0.6435   | 0.6431 |
| 0.0701        | 29.35 | 13500 | 2.5108          | 0.6431   | 0.6424 |


### Framework versions

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