---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_
  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: 0.5767746913580247
    - name: F1
      type: f1
      value: 0.5751836259585372
---

<!-- 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-NON-KD-PR-COPY-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_

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: 4.1676
- Accuracy: 0.5768
- F1: 0.5752

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 1123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 1.0487        | 1.09  | 500   | 0.9666          | 0.5305   | 0.5194 |
| 0.9092        | 2.17  | 1000  | 0.9220          | 0.5760   | 0.5733 |
| 0.76          | 3.26  | 1500  | 1.0464          | 0.5791   | 0.5681 |
| 0.6233        | 4.35  | 2000  | 1.1732          | 0.5864   | 0.5809 |
| 0.4852        | 5.43  | 2500  | 1.1695          | 0.5590   | 0.5578 |
| 0.374         | 6.52  | 3000  | 1.3903          | 0.5691   | 0.5669 |
| 0.2851        | 7.61  | 3500  | 1.5832          | 0.5760   | 0.5711 |
| 0.2203        | 8.7   | 4000  | 1.6098          | 0.5737   | 0.5739 |
| 0.1863        | 9.78  | 4500  | 1.9189          | 0.5656   | 0.5566 |
| 0.1437        | 10.87 | 5000  | 2.1445          | 0.5826   | 0.5783 |
| 0.1302        | 11.96 | 5500  | 1.9960          | 0.5791   | 0.5723 |
| 0.1075        | 13.04 | 6000  | 2.5978          | 0.5710   | 0.5663 |
| 0.0957        | 14.13 | 6500  | 2.9129          | 0.5675   | 0.5682 |
| 0.0898        | 15.22 | 7000  | 2.8487          | 0.5799   | 0.5780 |
| 0.082         | 16.3  | 7500  | 2.8461          | 0.5714   | 0.5621 |
| 0.0673        | 17.39 | 8000  | 2.8416          | 0.5849   | 0.5767 |
| 0.0647        | 18.48 | 8500  | 3.1083          | 0.5849   | 0.5810 |
| 0.0597        | 19.57 | 9000  | 2.9063          | 0.5772   | 0.5700 |
| 0.0508        | 20.65 | 9500  | 3.1069          | 0.5706   | 0.5663 |
| 0.0492        | 21.74 | 10000 | 3.1434          | 0.5841   | 0.5853 |
| 0.0485        | 22.83 | 10500 | 2.9341          | 0.5887   | 0.5816 |
| 0.0373        | 23.91 | 11000 | 3.2828          | 0.5810   | 0.5807 |
| 0.0352        | 25.0  | 11500 | 3.1742          | 0.5864   | 0.5802 |
| 0.0326        | 26.09 | 12000 | 3.2767          | 0.5733   | 0.5734 |
| 0.0269        | 27.17 | 12500 | 3.5101          | 0.5826   | 0.5797 |
| 0.0338        | 28.26 | 13000 | 3.2453          | 0.5725   | 0.5693 |
| 0.0289        | 29.35 | 13500 | 3.3957          | 0.5694   | 0.5703 |
| 0.0232        | 30.43 | 14000 | 3.4668          | 0.5710   | 0.5714 |
| 0.0215        | 31.52 | 14500 | 3.5250          | 0.5721   | 0.5660 |
| 0.0197        | 32.61 | 15000 | 3.5990          | 0.5787   | 0.5755 |
| 0.0138        | 33.7  | 15500 | 3.7731          | 0.5745   | 0.5682 |
| 0.0177        | 34.78 | 16000 | 3.6367          | 0.5698   | 0.5671 |
| 0.0145        | 35.87 | 16500 | 3.8987          | 0.5725   | 0.5705 |
| 0.013         | 36.96 | 17000 | 3.8459          | 0.5745   | 0.5737 |
| 0.0133        | 38.04 | 17500 | 3.7106          | 0.5733   | 0.5711 |
| 0.0095        | 39.13 | 18000 | 3.8834          | 0.5683   | 0.5688 |
| 0.0091        | 40.22 | 18500 | 3.9118          | 0.5733   | 0.5731 |
| 0.0107        | 41.3  | 19000 | 3.9038          | 0.5768   | 0.5733 |
| 0.0089        | 42.39 | 19500 | 3.8957          | 0.5826   | 0.5784 |
| 0.0042        | 43.48 | 20000 | 4.1050          | 0.5775   | 0.5761 |
| 0.0067        | 44.57 | 20500 | 4.0982          | 0.5756   | 0.5739 |
| 0.0042        | 45.65 | 21000 | 4.2051          | 0.5737   | 0.5733 |
| 0.0057        | 46.74 | 21500 | 4.1266          | 0.5764   | 0.5764 |
| 0.0056        | 47.83 | 22000 | 4.1318          | 0.5787   | 0.5765 |
| 0.0034        | 48.91 | 22500 | 4.1443          | 0.5791   | 0.5772 |
| 0.003         | 50.0  | 23000 | 4.1676          | 0.5768   | 0.5752 |


### Framework versions

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