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STL-xlm-roberta-large-stance-finetuned
tadiecool29/STL-xlm-roberta-large-stance-finetuned
STL-xlm-roberta-large-stance-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 Aug 31, 2026
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At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- e6e2fd4a
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-large-stance-finetuned
This model is a fine-tuned version of FacebookAI/xlm-roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4042
- Stance Precision: 0.7715
- Stance Recall: 0.7710
- F1: 0.7709
- Stance Acc: 0.7631
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 | Stance Precision | Stance Recall | F1 | Stance Acc |
|---|---|---|---|---|---|---|---|
| 0.9146 | 1.0 | 377 | 0.8488 | 0.6661 | 0.6646 | 0.6521 | 0.6471 |
| 0.6538 | 2.0 | 754 | 0.7305 | 0.7364 | 0.7217 | 0.7258 | 0.7170 |
| 0.5753 | 3.0 | 1131 | 0.6869 | 0.7680 | 0.7542 | 0.7601 | 0.7494 |
| 0.5299 | 4.0 | 1508 | 0.8155 | 0.7336 | 0.7263 | 0.7205 | 0.7157 |
| 0.3747 | 5.0 | 1885 | 0.8693 | 0.7513 | 0.7535 | 0.7503 | 0.7431 |
| 0.2472 | 6.0 | 2262 | 0.9068 | 0.7628 | 0.7675 | 0.7647 | 0.7569 |
| 0.1758 | 7.0 | 2639 | 1.1083 | 0.7674 | 0.7522 | 0.7563 | 0.7481 |
| 0.1843 | 8.0 | 3016 | 1.2880 | 0.7606 | 0.7646 | 0.7624 | 0.7544 |
| 0.1372 | 9.0 | 3393 | 1.3702 | 0.7701 | 0.7686 | 0.7691 | 0.7606 |
| 0.0639 | 10.0 | 3770 | 1.4042 | 0.7715 | 0.7710 | 0.7709 | 0.7631 |
Framework versions
- Transformers 5.15.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- License
- mit
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026