---
license: cc-by-nc-sa-4.0
base_model: ElnaggarLab/ankh-base
tags:
- generated_from_trainer
model-index:
- name: TooT-PLM-P2S
  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. -->

# TooT-PLM-P2S

This model is a fine-tuned version of [ElnaggarLab/ankh-base](https://huggingface.co/ElnaggarLab/ankh-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1575
- Q3 Accuracy: 0.5314

## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 7
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Q3 Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 0.6057        | 1.0   | 318  | 0.2338          | 0.4211      |
| 0.214         | 2.0   | 636  | 0.1712          | 0.6755      |
| 0.2068        | 3.0   | 954  | 0.1697          | 0.6628      |
| 0.2178        | 4.0   | 1272 | 0.1755          | 0.3646      |
| 0.1815        | 5.0   | 1590 | 0.1678          | 0.6628      |
| 0.1768        | 6.0   | 1908 | 0.1667          | 0.6628      |
| 0.1682        | 7.0   | 2226 | 0.1650          | 0.6628      |
| 0.1626        | 8.0   | 2544 | 0.1693          | 0.6527      |
| 0.1609        | 9.0   | 2862 | 0.1594          | 0.6566      |
| 0.1577        | 10.0  | 3180 | 0.1575          | 0.5314      |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1