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XLM-RoBERTa-PRETRAINED431-CERED2
stulcrad/XLM-RoBERTa-PRETRAINED431-CERED2
XLM-RoBERTa-PRETRAINED431-CERED2 is a machine learning model from stulcrad. 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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At a glance
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Apr 13, 2025
- Updated
- Jul 18, 2025
- SHA
- b9f82673
Base models
Datasets
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. -->
XLM-RoBERTa-PRETRAINED431-CERED2
This model is a fine-tuned version of stulcrad/XLM-RoBERTa-PRETRAINED43-CERED1 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.1302
- Accuracy: 0.9090
- Micro Precision: 0.9090
- Micro Recall: 0.9090
- Micro F1: 0.9090
- Macro Precision: 0.8920
- Macro Recall: 0.8684
- Macro F1: 0.8759
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: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use adamw_torch 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: 1500
- num_epochs: 8
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.8436 | 1.0000 | 11305 | 1.0630 | 0.9097 | 0.9097 | 0.9097 | 0.9097 | 0.8806 | 0.8813 | 0.8777 |
| 0.8306 | 2.0 | 22611 | 1.0938 | 0.9076 | 0.9076 | 0.9076 | 0.9076 | 0.8807 | 0.8764 | 0.8732 |
| 0.7918 | 3.0000 | 33916 | 1.1135 | 0.9115 | 0.9115 | 0.9115 | 0.9115 | 0.8936 | 0.8780 | 0.8823 |
| 0.7752 | 4.0 | 45222 | 1.1283 | 0.9112 | 0.9112 | 0.9112 | 0.9112 | 0.8815 | 0.8833 | 0.8788 |
| 0.7629 | 5.0000 | 56527 | 1.1361 | 0.9104 | 0.9104 | 0.9104 | 0.9104 | 0.8920 | 0.8759 | 0.8790 |
| 0.7558 | 6.0 | 67833 | 1.1426 | 0.9110 | 0.9110 | 0.9110 | 0.9110 | 0.8886 | 0.8802 | 0.8805 |
| 0.7467 | 7.0000 | 79138 | 1.1471 | 0.9107 | 0.9107 | 0.9107 | 0.9107 | 0.8896 | 0.8784 | 0.8797 |
| 0.7472 | 7.9996 | 90440 | 1.1466 | 0.9101 | 0.9101 | 0.9101 | 0.9101 | 0.8903 | 0.8792 | 0.8805 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Apr 13, 2025
- Updated
- Jul 18, 2025