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FoodEntity_Hybrid_Lora_Freezing_v1
fdastak/FoodEntity_Hybrid_Lora_Freezing_v1
FoodEntity_Hybrid_Lora_Freezing_v1 is a machine learning model from fdastak. 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.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- Access
- Public
- Created
- Nov 28, 2025
- Updated
- Nov 29, 2025
- SHA
- 65e95db5
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. -->
FoodEntity_Hybrid_Lora_Freezing_v1
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0060
- Precision: 0.7778
- Recall: 0.8509
- F1: 0.8127
- Accuracy: 0.9804
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0194 | 1.0 | 117 | 0.0076 | 0.7206 | 0.8321 | 0.7723 | 0.9736 |
| 0.0059 | 2.0 | 234 | 0.0060 | 0.7778 | 0.8509 | 0.8127 | 0.9804 |
Framework versions
- PEFT 0.18.0
- Transformers 4.55.4
- Pytorch 2.8.0+cpu
- Datasets 4.0.0
- Tokenizers 0.21.4
- Library
- peft
- Created
- Nov 28, 2025
- Updated
- Nov 29, 2025