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xlmr-papluca-model
zues0102/xlmr-papluca-model
xlmr-papluca-model is a text classification model from zues0102. 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
- May 29, 2025
- Updated
- May 29, 2025
- SHA
- b505d5cf
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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-papluca-model
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0176
- Accuracy: 0.9972
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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0587 | 1.0 | 500 | 0.0253 | 0.9955 |
| 0.0177 | 2.0 | 1000 | 0.0194 | 0.9968 |
| 0.012 | 3.0 | 1500 | 0.0174 | 0.9968 |
| 0.0026 | 4.0 | 2000 | 0.0149 | 0.9978 |
| 0.003 | 5.0 | 2500 | 0.0138 | 0.9978 |
| 0.0031 | 6.0 | 3000 | 0.0179 | 0.9978 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- May 29, 2025
- Updated
- May 29, 2025