Downloads · 30 days
8
15% of all-time downloads
sentiment-xlm-batch16
Carlosdca/sentiment-xlm-batch16
sentiment-xlm-batch16 is a text classification model from Carlosdca. 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. --
All-time downloads
54
Public
Parameters
560M
2.3 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors2.2 GB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 18, 2025
- Updated
- Nov 18, 2025
- SHA
- 94a1f7e5
Try a prompt
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. -->
sentiment-xlm-batch16
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: 0.6867
- Accuracy: 0.8842
- F1 Macro: 0.8818
- F1 Weighted: 0.8844
- Precision Macro: 0.8809
- Recall Macro: 0.8827
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
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro |
|---|---|---|---|---|---|---|---|---|
| 0.7178 | 1.0 | 164 | 0.4392 | 0.8460 | 0.8456 | 0.8468 | 0.8499 | 0.8572 |
| 0.3917 | 2.0 | 328 | 0.3979 | 0.8659 | 0.8644 | 0.8665 | 0.8627 | 0.8698 |
| 0.2971 | 3.0 | 492 | 0.4042 | 0.8643 | 0.8626 | 0.8649 | 0.8608 | 0.8671 |
| 0.2313 | 4.0 | 656 | 0.6291 | 0.8735 | 0.8716 | 0.8739 | 0.8697 | 0.8750 |
| 0.1943 | 5.0 | 820 | 0.6439 | 0.8918 | 0.8878 | 0.8910 | 0.8950 | 0.8835 |
| 0.1332 | 6.0 | 984 | 0.6504 | 0.8841 | 0.8825 | 0.8846 | 0.8805 | 0.8862 |
| 0.0749 | 7.0 | 1148 | 0.6669 | 0.8902 | 0.8879 | 0.8903 | 0.8873 | 0.8887 |
| 0.0657 | 8.0 | 1312 | 0.8075 | 0.8780 | 0.8754 | 0.8781 | 0.8750 | 0.8757 |
| 0.0343 | 9.0 | 1476 | 0.8822 | 0.8765 | 0.8740 | 0.8767 | 0.8732 | 0.8749 |
| 0.0488 | 10.0 | 1640 | 0.8728 | 0.8780 | 0.8756 | 0.8782 | 0.8747 | 0.8767 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Nov 18, 2025
- Updated
- Nov 18, 2025