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xlm-roberta-base-finetuned-DS_Q_QA-topAug.8
Z-Jafari/xlm-roberta-base-finetuned-DS_Q_QA-topAug.8
xlm-roberta-base-finetuned-DS_Q_QA-topAug.8 is a question answering model from Z-Jafari. Use it when the input is a question plus a passage. 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
- Question Answering
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Dec 16, 2025
- Updated
- Dec 16, 2025
- SHA
- f0fe17b7
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. -->
xlm-roberta-base-finetuned-DS_Q_QA-topAug.8
This model is a fine-tuned version of FacebookAI/xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9220
- {'exact_match': 81.3, 'f1': 83.81703349282292}
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: 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
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7162 | 1.0 | 2159 | 0.8343 |
| 0.5171 | 2.0 | 4318 | 0.8947 |
| 0.3839 | 3.0 | 6477 | 0.9220 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Question Answering
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Dec 16, 2025
- Updated
- Dec 16, 2025