LABEL_0 LABEL_1 LABEL_10 LABEL_11 LABEL_12 LABEL_13 LABEL_14 LABEL_15 LABEL_16 LABEL_17 LABEL_18 LABEL_19 LABEL_2 LABEL_3 LABEL_4 LABEL_5 LABEL_6 LABEL_7 LABEL_8 LABEL_9
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: TAPE-finetuned-viralProteins
  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. -->

# TAPE-finetuned-viralProteins

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9033
- Accuracy: 0.87
- F1: 0.8555
- Precision: 0.8475
- Recall: 0.87

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8845        | 1.0   | 5000  | 0.8302          | 0.85     | 0.8060 | 0.7779    | 0.85   |
| 0.8189        | 2.0   | 10000 | 0.6062          | 0.86     | 0.8255 | 0.8115    | 0.86   |
| 0.806         | 3.0   | 15000 | 0.8546          | 0.85     | 0.8095 | 0.7840    | 0.85   |
| 0.6971        | 4.0   | 20000 | 0.7660          | 0.86     | 0.8228 | 0.8027    | 0.86   |
| 0.6269        | 5.0   | 25000 | 0.7787          | 0.85     | 0.8343 | 0.8226    | 0.85   |
| 0.5771        | 6.0   | 30000 | 0.7965          | 0.855    | 0.8402 | 0.8290    | 0.855  |
| 0.5433        | 7.0   | 35000 | 0.7864          | 0.875    | 0.8573 | 0.8473    | 0.875  |
| 0.5183        | 8.0   | 40000 | 0.8292          | 0.87     | 0.8521 | 0.8425    | 0.87   |
| 0.4396        | 9.0   | 45000 | 0.8838          | 0.875    | 0.8566 | 0.8483    | 0.875  |
| 0.4019        | 10.0  | 50000 | 0.9033          | 0.87     | 0.8555 | 0.8475    | 0.87   |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
