update model card README.md

README.md

@@ -1,7 +1,76 @@
-Embedding taken from protBERT-BFD pretrained model
+---
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: protBERTbfd_AAV2_classification
+  results: []
+---
 
-Classification task was trained on AAV2 dataset ~230k sequences (Bryant et al 2020)
+<!-- 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. -->
 
-AAV2 WT sequence (pdb 6IH9: aa561-588): D E E E I R T T N P V A T E Q Y G S V S T N L Q R G N R
+# protBERTbfd_AAV2_classification
 
-Maximum length: 50 amino acids
+This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1341
+- Accuracy: 0.9615
+- F1: 0.9627
+- Precision: 0.9637
+- Recall: 0.9618
+- Auroc: 0.9615
+
+## 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: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 64
+- total_train_batch_size: 2048
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 200
+- num_epochs: 8
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
+| No log        | 1.0   | 116  | 0.2582          | 0.9064   | 0.9157 | 0.8564    | 0.9839 | 0.9038 |
+| No log        | 2.0   | 232  | 0.1447          | 0.9424   | 0.9432 | 0.9618    | 0.9252 | 0.9430 |
+| No log        | 3.0   | 348  | 0.1182          | 0.9542   | 0.9556 | 0.9573    | 0.9539 | 0.9542 |
+| No log        | 4.0   | 464  | 0.1129          | 0.9585   | 0.9602 | 0.9520    | 0.9685 | 0.9581 |
+| 0.2162        | 5.0   | 580  | 0.1278          | 0.9553   | 0.9558 | 0.9776    | 0.9351 | 0.9561 |
+| 0.2162        | 6.0   | 696  | 0.1139          | 0.9587   | 0.9607 | 0.9465    | 0.9752 | 0.9581 |
+| 0.2162        | 7.0   | 812  | 0.1127          | 0.9620   | 0.9633 | 0.9614    | 0.9652 | 0.9619 |
+| 0.2162        | 8.0   | 928  | 0.1341          | 0.9615   | 0.9627 | 0.9637    | 0.9618 | 0.9615 |
+
+
+### Framework versions
+
+- Transformers 4.18.0
+- Pytorch 1.11.0+cu113
+- Datasets 2.1.0
+- Tokenizers 0.12.1