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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_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
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