fdastak/FoodEntity_Hybrid_Lora_Freezing_v1
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README.md
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---
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library_name: peft
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base_model: dmis-lab/biobert-v1.1
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tags:
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- base_model:adapter:dmis-lab/biobert-v1.1
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- lora
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- transformers
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: FoodEntity_Hybrid_Lora_Freezing_v1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# FoodEntity_Hybrid_Lora_Freezing_v1
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This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0060
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- Precision: 0.7778
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- Recall: 0.8509
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- F1: 0.8127
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- Accuracy: 0.9804
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 2
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.0194 | 1.0 | 117 | 0.0076 | 0.7206 | 0.8321 | 0.7723 | 0.9736 |
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| 0.0059 | 2.0 | 234 | 0.0060 | 0.7778 | 0.8509 | 0.8127 | 0.9804 |
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### Framework versions
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- PEFT 0.18.0
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- Transformers 4.55.4
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- Pytorch 2.8.0+cpu
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- Datasets 4.0.0
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- Tokenizers 0.21.4
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