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rag_deberta_checkpoints
23f3003974/rag_deberta_checkpoints
rag_deberta_checkpoints is a machine learning model from 23f3003974. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. 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
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Jul 25, 2026
- Updated
- Jul 25, 2026
- SHA
- 0c408b00
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. -->
rag_deberta_checkpoints
This model is a fine-tuned version of google/bigbird-roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6086
- Map@3: 0.4102
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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 25
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Map@3 |
|---|---|---|---|---|
| 25.9079 | 1.0 | 62 | 1.6103 | 0.3054 |
| 25.7527 | 2.0 | 124 | 1.6093 | 0.3959 |
| 25.6303 | 3.0 | 186 | 1.6084 | 0.3878 |
| 25.8033 | 4.0 | 248 | 1.6082 | 0.4088 |
| 25.6139 | 5.0 | 310 | 1.6086 | 0.4102 |
Framework versions
- PEFT 0.19.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
- Library
- peft
- License
- apache-2.0
- Created
- Jul 25, 2026
- Updated
- Jul 25, 2026