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README.md
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---
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license: mit
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base_model: facebook/esm2_t12_35M_UR50D
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: esm2_t12_35M_qlora_glycosylation_sites_2024-02-11_22-11-09
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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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# esm2_t12_35M_qlora_glycosylation_sites_2024-02-11_22-11-09
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This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1117
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- Accuracy: 0.9968
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- Precision: 0.4831
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- Recall: 0.9671
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- F1: 0.6443
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- Auc: 0.9820
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- Mcc: 0.6823
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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: 0.0003701568055793089
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- train_batch_size: 36
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- eval_batch_size: 36
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- seed: 8893
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- num_epochs: 3
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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 | Accuracy | Precision | Recall | F1 | Auc | Mcc |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
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| 0.1789 | 1.0 | 295 | 0.1102 | 0.9962 | 0.4391 | 0.9638 | 0.6034 | 0.9801 | 0.6492 |
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| 0.0145 | 2.0 | 590 | 0.1105 | 0.9967 | 0.4776 | 0.9663 | 0.6393 | 0.9816 | 0.6782 |
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| 0.0115 | 3.0 | 885 | 0.1117 | 0.9968 | 0.4831 | 0.9671 | 0.6443 | 0.9820 | 0.6823 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.1.0+cu121
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- Datasets 2.17.0
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- Tokenizers 0.15.1
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