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multilingual_xlmr_v2
Yashaswini21/multilingual_xlmr_v2
multilingual_xlmr_v2 is a text classification model from Yashaswini21. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Aug 27, 2025
- Updated
- Sep 2, 2025
- SHA
- 53c4a453
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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. -->
multilingual_xlmr_v2
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3044
- Accuracy: 0.8662
- Precision: 0.8835
- Recall: 0.8437
- F1: 0.8631
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: 64
- eval_batch_size: 64
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3315 | 1.0 | 3515 | 0.3428 | 0.8427 | 0.8920 | 0.7800 | 0.8322 |
| 0.3064 | 2.0 | 7030 | 0.3127 | 0.8593 | 0.9013 | 0.8070 | 0.8516 |
| 0.249 | 3.0 | 10545 | 0.3044 | 0.8662 | 0.8835 | 0.8437 | 0.8631 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu129
- Datasets 4.0.0
- Tokenizers 0.21.4
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 27, 2025
- Updated
- Sep 2, 2025