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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_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
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