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results_soft_label
ilyaschaki-numrah/results_soft_label
results_soft_label is a text classification model from ilyaschaki-numrah. Use it when you need a label for a piece of text. 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
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Oct 30, 2025
- Updated
- Nov 3, 2025
- SHA
- b91b205e
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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. -->
results_soft_label
This model is a fine-tuned version of xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2341
- Mae: 0.0495
- Rmse: 0.1321
- Pearson Correlation: 0.9561
- Auc Roc: 0.9866
- Average Precision: 0.9803
- F1 At 0.5: 0.8754
- Precision At 0.5: 0.7871
- Recall At 0.5: 0.9862
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mae | Rmse | Pearson Correlation | Auc Roc | Average Precision | F1 At 0.5 | Precision At 0.5 | Recall At 0.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.2484 | 1.0 | 11243 | 0.2585 | 0.0659 | 0.1608 | 0.9350 | 0.9802 | 0.9735 | 0.8617 | 0.7706 | 0.9772 |
| 0.2275 | 2.0 | 22486 | 0.2410 | 0.0541 | 0.1404 | 0.9506 | 0.9852 | 0.9800 | 0.8710 | 0.7811 | 0.9842 |
| 0.228 | 3.0 | 33729 | 0.2341 | 0.0495 | 0.1321 | 0.9561 | 0.9866 | 0.9803 | 0.8754 | 0.7871 | 0.9862 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Oct 30, 2025
- Updated
- Nov 3, 2025