metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
base_model: facebook/esm-1b
model-index:
- name: ESM1b_AAV2_classification
results: []
ESM1b_AAV2_classification
To load tokenizer from ESM, you need to install transformers with this version as follow:
!git clone -b add_esm-proper --single-branch https://github.com/liujas000/transformers.git !pip -q install ./transformers
This model is a fine-tuned version of facebook/esm-1b on AAV2 dataset with ~230k sequences (Bryant et al 2020).
The WT sequence (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 Maximum length: 50
It achieves the following results on the evaluation set. Note:this is result of the last epoch, I think the pushed model is loaded with best checkpoint - best val_loss, I'm not so sure though :/
- Loss: 0.2250
- Accuracy: 0.9620
- F1: 0.9632
- Precision: 0.9642
- Recall: 0.9622
- Auroc: 0.9620
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: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 1024
- 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 | 232 | 0.1311 | 0.9495 | 0.9501 | 0.9711 | 0.9299 | 0.9502 |
| No log | 2.0 | 464 | 0.1032 | 0.9606 | 0.9620 | 0.9583 | 0.9657 | 0.9604 |
| 0.1924 | 3.0 | 696 | 0.0995 | 0.9627 | 0.9641 | 0.9584 | 0.9700 | 0.9625 |
| 0.1924 | 4.0 | 928 | 0.1218 | 0.9611 | 0.9624 | 0.9607 | 0.9641 | 0.9610 |
| 0.067 | 5.0 | 1160 | 0.1187 | 0.9622 | 0.9633 | 0.9678 | 0.9588 | 0.9623 |
| 0.067 | 6.0 | 1392 | 0.1514 | 0.9612 | 0.9621 | 0.9710 | 0.9534 | 0.9615 |
| 0.0271 | 7.0 | 1624 | 0.1890 | 0.9612 | 0.9626 | 0.9580 | 0.9673 | 0.9610 |
| 0.0271 | 8.0 | 1856 | 0.2250 | 0.9620 | 0.9632 | 0.9642 | 0.9622 | 0.9620 |
Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.10.3