---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- tweet_sentiment_multilingual
metrics:
- accuracy
- f1
model-index:
- name: scenario-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_gamma
  results: []
---

<!-- 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-KD-PR-CDF-ALL-D2_data-cardiffnlp_tweet_sentiment_multilingual_all_gamma

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the tweet_sentiment_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4838
- Accuracy: 0.5505
- F1: 0.5508

## 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: 88458
- 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     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 4.8911        | 1.09  | 500   | 4.3088          | 0.4047   | 0.3809 |
| 4.106         | 2.17  | 1000  | 3.7773          | 0.5058   | 0.4920 |
| 3.4954        | 3.26  | 1500  | 3.3608          | 0.5347   | 0.5357 |
| 3.1472        | 4.35  | 2000  | 3.4225          | 0.5343   | 0.5327 |
| 2.8094        | 5.43  | 2500  | 3.4088          | 0.5436   | 0.5399 |
| 2.5414        | 6.52  | 3000  | 3.3362          | 0.5552   | 0.5562 |
| 2.331         | 7.61  | 3500  | 3.3218          | 0.5459   | 0.5389 |
| 2.1295        | 8.7   | 4000  | 3.6107          | 0.5525   | 0.5532 |
| 1.9843        | 9.78  | 4500  | 3.4533          | 0.5575   | 0.5578 |
| 1.8472        | 10.87 | 5000  | 3.2933          | 0.5482   | 0.5469 |
| 1.7227        | 11.96 | 5500  | 3.3387          | 0.5513   | 0.5521 |
| 1.6067        | 13.04 | 6000  | 3.2725          | 0.5444   | 0.5454 |
| 1.5328        | 14.13 | 6500  | 3.3817          | 0.5513   | 0.5528 |
| 1.4166        | 15.22 | 7000  | 3.5382          | 0.5421   | 0.5437 |
| 1.346         | 16.3  | 7500  | 3.4353          | 0.5567   | 0.5574 |
| 1.3038        | 17.39 | 8000  | 3.5873          | 0.5478   | 0.5462 |
| 1.2285        | 18.48 | 8500  | 3.7322          | 0.5525   | 0.5516 |
| 1.1916        | 19.57 | 9000  | 3.5055          | 0.5486   | 0.5488 |
| 1.1143        | 20.65 | 9500  | 3.4413          | 0.5575   | 0.5589 |
| 1.0749        | 21.74 | 10000 | 3.7211          | 0.5559   | 0.5572 |
| 1.0668        | 22.83 | 10500 | 3.5802          | 0.5575   | 0.5576 |
| 1.0111        | 23.91 | 11000 | 3.5038          | 0.5606   | 0.5598 |
| 0.9837        | 25.0  | 11500 | 3.6704          | 0.5521   | 0.5517 |
| 0.9643        | 26.09 | 12000 | 3.5238          | 0.5598   | 0.5609 |
| 0.9311        | 27.17 | 12500 | 3.5195          | 0.5559   | 0.5558 |
| 0.902         | 28.26 | 13000 | 3.3760          | 0.5679   | 0.5653 |
| 0.8935        | 29.35 | 13500 | 3.6155          | 0.5536   | 0.5539 |
| 0.8745        | 30.43 | 14000 | 3.5108          | 0.5667   | 0.5662 |
| 0.8444        | 31.52 | 14500 | 3.6231          | 0.5606   | 0.5597 |
| 0.8327        | 32.61 | 15000 | 3.5783          | 0.5552   | 0.5508 |
| 0.8237        | 33.7  | 15500 | 3.5527          | 0.5556   | 0.5548 |
| 0.8035        | 34.78 | 16000 | 3.4553          | 0.5660   | 0.5657 |
| 0.7948        | 35.87 | 16500 | 3.4230          | 0.5490   | 0.5503 |
| 0.7886        | 36.96 | 17000 | 3.5010          | 0.5482   | 0.5494 |
| 0.7711        | 38.04 | 17500 | 3.4771          | 0.5644   | 0.5648 |
| 0.76          | 39.13 | 18000 | 3.5514          | 0.5563   | 0.5570 |
| 0.7509        | 40.22 | 18500 | 3.4726          | 0.5586   | 0.5585 |
| 0.7522        | 41.3  | 19000 | 3.5237          | 0.5606   | 0.5586 |
| 0.7368        | 42.39 | 19500 | 3.4514          | 0.5532   | 0.5516 |
| 0.7377        | 43.48 | 20000 | 3.5320          | 0.5633   | 0.5636 |
| 0.7142        | 44.57 | 20500 | 3.4685          | 0.5613   | 0.5608 |
| 0.7255        | 45.65 | 21000 | 3.4919          | 0.5652   | 0.5635 |
| 0.7139        | 46.74 | 21500 | 3.4869          | 0.5556   | 0.5551 |
| 0.7124        | 47.83 | 22000 | 3.4748          | 0.5644   | 0.5642 |
| 0.7065        | 48.91 | 22500 | 3.4405          | 0.5602   | 0.5601 |
| 0.7038        | 50.0  | 23000 | 3.4838          | 0.5505   | 0.5508 |


### Framework versions

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