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---
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
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