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STL-xlm-roberta-base-stance-finetuned
tadiecool29/STL-xlm-roberta-base-stance-finetuned
STL-xlm-roberta-base-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
- bf9eaef4
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-stance-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: 1.0433
- Stance Precision: 0.7164
- Stance Recall: 0.7222
- F1: 0.7190
- Stance Acc: 0.7120
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 |
|---|---|---|---|---|---|---|---|
| 0.9863 | 1.0 | 377 | 0.8426 | 0.6822 | 0.6668 | 0.6688 | 0.6621 |
| 0.7451 | 2.0 | 754 | 0.7934 | 0.6941 | 0.7109 | 0.6962 | 0.6933 |
| 0.6529 | 3.0 | 1131 | 0.7942 | 0.7008 | 0.7106 | 0.7036 | 0.6983 |
| 0.5651 | 4.0 | 1508 | 0.8433 | 0.6992 | 0.6993 | 0.6936 | 0.6883 |
| 0.4656 | 5.0 | 1885 | 0.8419 | 0.7075 | 0.7200 | 0.7113 | 0.7057 |
| 0.3997 | 6.0 | 2262 | 0.9248 | 0.7170 | 0.7262 | 0.7196 | 0.7132 |
| 0.3214 | 7.0 | 2639 | 0.9897 | 0.7221 | 0.7138 | 0.7164 | 0.7082 |
| 0.3028 | 8.0 | 3016 | 1.0324 | 0.7097 | 0.7123 | 0.7103 | 0.7020 |
| 0.2653 | 9.0 | 3393 | 1.0366 | 0.7275 | 0.7277 | 0.7273 | 0.7207 |
| 0.2659 | 10.0 | 3770 | 1.0433 | 0.7164 | 0.7222 | 0.7190 | 0.7120 |
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