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xlmr_english_immigration
AnonymousCS/xlmr_english_immigration
xlmr_english_immigration 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. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Aug 18, 2025
- Updated
- Aug 18, 2025
- SHA
- 39871de7
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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. -->
xlmr_english_immigration
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: 0.2598
- Accuracy: 0.9308
- 1-f1: 0.8966
- 1-recall: 0.9070
- 1-precision: 0.8864
- Balanced Acc: 0.9248
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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|---|---|---|---|---|---|---|---|---|
| 0.5549 | 1.0 | 5 | 0.4841 | 0.8615 | 0.7353 | 0.5814 | 1.0 | 0.7907 |
| 0.5873 | 2.0 | 10 | 0.4579 | 0.8692 | 0.8132 | 0.8605 | 0.7708 | 0.8670 |
| 0.4552 | 3.0 | 15 | 0.4112 | 0.9 | 0.8434 | 0.8140 | 0.875 | 0.8782 |
| 0.3566 | 4.0 | 20 | 0.3635 | 0.8923 | 0.8205 | 0.7442 | 0.9143 | 0.8549 |
| 0.3533 | 5.0 | 25 | 0.3118 | 0.9231 | 0.8810 | 0.8605 | 0.9024 | 0.9072 |
| 0.3372 | 6.0 | 30 | 0.3078 | 0.9077 | 0.8696 | 0.9302 | 0.8163 | 0.9134 |
| 0.1963 | 7.0 | 35 | 0.2485 | 0.9154 | 0.8706 | 0.8605 | 0.8810 | 0.9015 |
| 0.1598 | 8.0 | 40 | 0.2199 | 0.9154 | 0.8736 | 0.8837 | 0.8636 | 0.9074 |
| 0.1177 | 9.0 | 45 | 0.2188 | 0.9154 | 0.8764 | 0.9070 | 0.8478 | 0.9133 |
| 0.0412 | 10.0 | 50 | 0.2444 | 0.9154 | 0.8764 | 0.9070 | 0.8478 | 0.9133 |
| 0.1093 | 11.0 | 55 | 0.2598 | 0.9308 | 0.8966 | 0.9070 | 0.8864 | 0.9248 |
Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Aug 18, 2025
- Updated
- Aug 18, 2025