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STL-xlm-roberta-base-sentiment-finetuned
tadiecool29/STL-xlm-roberta-base-sentiment-finetuned
STL-xlm-roberta-base-sentiment-finetuned is a machine learning model from tadiecool29. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Sep 1, 2026
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At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026
- SHA
- 49b2bf9b
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
STL-xlm-roberta-base-sentiment-finetuned
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9744
- Sentiment Precision: 0.7065
- Sentiment Recall: 0.7045
- F1: 0.7053
- Sentiment Acc: 0.7107
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Sentiment Precision | Sentiment Recall | F1 | Sentiment Acc |
|---|---|---|---|---|---|---|---|
| 0.9122 | 1.0 | 377 | 0.7960 | 0.6541 | 0.6405 | 0.6407 | 0.6471 |
| 0.7705 | 2.0 | 754 | 0.7061 | 0.7028 | 0.6978 | 0.6959 | 0.6983 |
| 0.6692 | 3.0 | 1131 | 0.7028 | 0.6995 | 0.7025 | 0.6974 | 0.7082 |
| 0.5384 | 4.0 | 1508 | 0.7335 | 0.7111 | 0.7041 | 0.7041 | 0.7045 |
| 0.5218 | 5.0 | 1885 | 0.7551 | 0.7031 | 0.7032 | 0.6985 | 0.7057 |
| 0.4010 | 6.0 | 2262 | 0.8239 | 0.7229 | 0.7143 | 0.7157 | 0.7170 |
| 0.3910 | 7.0 | 2639 | 0.8586 | 0.7075 | 0.7068 | 0.7065 | 0.7107 |
| 0.3148 | 8.0 | 3016 | 0.8971 | 0.7150 | 0.7129 | 0.7132 | 0.7170 |
| 0.3203 | 9.0 | 3393 | 0.9744 | 0.7065 | 0.7045 | 0.7053 | 0.7107 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1
- Library
- transformers
- License
- mit
- Created
- Sep 1, 2026
- Updated
- Sep 1, 2026