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xlm-roberta-base-eng-only-sentiment-single-finetuned-memes
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5629
- Accuracy: 0.8652
- Precision: 0.8794
- Recall: 0.8786
- F1: 0.8789
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 378 | 0.3506 | 0.8459 | 0.8647 | 0.8584 | 0.8605 |
| 0.4424 | 2.0 | 756 | 0.3264 | 0.8563 | 0.8818 | 0.8696 | 0.8689 |
| 0.2888 | 3.0 | 1134 | 0.3563 | 0.8578 | 0.8759 | 0.8701 | 0.8714 |
| 0.1889 | 4.0 | 1512 | 0.3939 | 0.8585 | 0.8733 | 0.8729 | 0.8730 |
| 0.1889 | 5.0 | 1890 | 0.4698 | 0.8622 | 0.8765 | 0.8761 | 0.8763 |
| 0.1136 | 6.0 | 2268 | 0.5629 | 0.8652 | 0.8794 | 0.8786 | 0.8789 |
Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
