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STL-xlm-roberta-large-sentiment
tadiecool29/STL-xlm-roberta-large-sentiment
STL-xlm-roberta-large-sentiment 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
- 7710c036
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-sentiment
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: 1.2339
- Sentiment Precision: 0.7323
- Sentiment Recall: 0.7313
- F1: 0.7317
- Sentiment Acc: 0.7369
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 |
|---|---|---|---|---|---|---|---|
| 0.8659 | 1.0 | 377 | 0.7622 | 0.6997 | 0.6928 | 0.6943 | 0.6983 |
| 0.7560 | 2.0 | 754 | 0.6376 | 0.7381 | 0.7352 | 0.7284 | 0.7369 |
| 0.5973 | 3.0 | 1131 | 0.6430 | 0.7265 | 0.7269 | 0.7203 | 0.7319 |
| 0.4718 | 4.0 | 1508 | 0.6938 | 0.7301 | 0.7304 | 0.7281 | 0.7394 |
| 0.3951 | 5.0 | 1885 | 0.6745 | 0.7335 | 0.7357 | 0.7323 | 0.7394 |
| 0.2933 | 6.0 | 2262 | 0.8124 | 0.7382 | 0.7358 | 0.7366 | 0.7406 |
| 0.2386 | 7.0 | 2639 | 0.9305 | 0.7232 | 0.7229 | 0.7228 | 0.7294 |
| 0.1537 | 8.0 | 3016 | 1.1450 | 0.7301 | 0.7288 | 0.7290 | 0.7357 |
| 0.1615 | 9.0 | 3393 | 1.2339 | 0.7323 | 0.7313 | 0.7317 | 0.7369 |
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