Downloads · 30 days
10
19% of all-time downloads
xlmr_swedish_immigration3
AnonymousCS/xlmr_swedish_immigration3
xlmr_swedish_immigration3 is a text classification model from AnonymousCS. 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
- Aug 19, 2025
- Updated
- Aug 19, 2025
- SHA
- 5d12bbfa
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. -->
xlmr_swedish_immigration3
This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3970
- Accuracy: 0.8615
- 1-f1: 0.7857
- 1-recall: 0.7674
- 1-precision: 0.8049
- Balanced Acc: 0.8377
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: 128
- eval_batch_size: 128
- 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
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|---|---|---|---|---|---|---|---|---|
| 0.4995 | 1.0 | 5 | 0.4609 | 0.8538 | 0.7397 | 0.6279 | 0.9 | 0.7967 |
| 0.361 | 2.0 | 10 | 0.4179 | 0.8231 | 0.7473 | 0.7907 | 0.7083 | 0.8149 |
| 0.3011 | 3.0 | 15 | 0.3806 | 0.8692 | 0.7792 | 0.6977 | 0.8824 | 0.8258 |
| 0.3618 | 4.0 | 20 | 0.4251 | 0.8154 | 0.7391 | 0.7907 | 0.6939 | 0.8091 |
| 0.2286 | 5.0 | 25 | 0.3762 | 0.8692 | 0.7901 | 0.7442 | 0.8421 | 0.8376 |
| 0.5345 | 6.0 | 30 | 0.3777 | 0.8692 | 0.7848 | 0.7209 | 0.8611 | 0.8317 |
| 0.1878 | 7.0 | 35 | 0.3679 | 0.8769 | 0.8 | 0.7442 | 0.8649 | 0.8434 |
| 0.1607 | 8.0 | 40 | 0.3851 | 0.8692 | 0.7901 | 0.7442 | 0.8421 | 0.8376 |
| 0.1597 | 9.0 | 45 | 0.3970 | 0.8615 | 0.7857 | 0.7674 | 0.8049 | 0.8377 |
Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Aug 19, 2025
- Updated
- Aug 19, 2025