Model save

README.md

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+---
+license: mit
+base_model: facebook/esm2_t12_35M_UR50D
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37
+  results: []
+---
+
+<!-- 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. -->
+
+# esm2_t12_35M_qlora_glycosylation_sites_2024-02-14_21-47-37
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 0.0108
+- Accuracy: 0.9990
+- Precision: 0.3291
+- Recall: 0.9951
+- F1: 0.4946
+- Auc: 0.9970
+- Mcc: 0.5720
+
+## 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: 0.0003701568055793089
+- train_batch_size: 36
+- eval_batch_size: 36
+- seed: 8893
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- num_epochs: 1
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     | Auc    | Mcc    |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|:------:|
+| 0.0125        | 1.0   | 16521 | 0.0108          | 0.9990   | 0.3291    | 0.9951 | 0.4946 | 0.9970 | 0.5720 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.1