---
library_name: peft
license: bsd-3-clause
base_model: hugohrban/progen2-base
tags:
- generated_from_trainer
model-index:
- name: Progen2_Kinase_PhosphositeGen_dkz_traindata
  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. -->

# Progen2_Kinase_PhosphositeGen_dkz_traindata

This model is a fine-tuned version of [hugohrban/progen2-base](https://huggingface.co/hugohrban/progen2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0955
- Perplexity: 8.1296

## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 5000

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Perplexity |
|:-------------:|:------:|:----:|:---------------:|:----------:|
| 4.7229        | 0.1455 | 100  | 2.1862          | 8.9015     |
| 4.2858        | 0.2909 | 200  | 2.1091          | 8.2405     |
| 4.2112        | 0.4364 | 300  | 2.0519          | 7.7824     |
| 4.1146        | 0.5818 | 400  | 2.0049          | 7.4252     |
| 4.0772        | 0.7273 | 500  | 1.9859          | 7.2855     |
| 3.9871        | 0.8727 | 600  | 1.9478          | 7.0130     |
| 3.891         | 1.0175 | 700  | 1.9204          | 6.8236     |
| 3.4841        | 1.1629 | 800  | 1.8889          | 6.6122     |
| 3.4596        | 1.3084 | 900  | 1.8696          | 6.4854     |
| 3.4659        | 1.4538 | 1000 | 1.8430          | 6.3152     |
| 3.433         | 1.5993 | 1100 | 1.8105          | 6.1137     |
| 3.3728        | 1.7447 | 1200 | 1.7991          | 6.0441     |
| 3.3853        | 1.8902 | 1300 | 1.7924          | 6.0040     |
| 3.1832        | 2.0349 | 1400 | 1.7975          | 6.0348     |
| 2.8198        | 2.1804 | 1500 | 1.7924          | 6.0041     |
| 2.7867        | 2.3258 | 1600 | 1.7604          | 5.8149     |
| 2.8669        | 2.4713 | 1700 | 1.7437          | 5.7183     |
| 2.795         | 2.6167 | 1800 | 1.7307          | 5.6445     |
| 2.8152        | 2.7622 | 1900 | 1.7188          | 5.5779     |
| 2.7734        | 2.9076 | 2000 | 1.6911          | 5.4256     |
| 2.5299        | 3.0524 | 2100 | 1.7682          | 5.8605     |
| 2.2126        | 3.1978 | 2200 | 1.7346          | 5.6669     |
| 2.2435        | 3.3433 | 2300 | 1.7104          | 5.5310     |
| 2.2663        | 3.4887 | 2400 | 1.7144          | 5.5536     |
| 2.2463        | 3.6342 | 2500 | 1.7338          | 5.6620     |
| 2.3117        | 3.7796 | 2600 | 1.6879          | 5.4079     |
| 2.2655        | 3.9251 | 2700 | 1.6946          | 5.4447     |
| 1.9936        | 4.0698 | 2800 | 1.8444          | 6.3244     |
| 1.7929        | 4.2153 | 2900 | 1.8653          | 6.4577     |
| 1.8214        | 4.3607 | 3000 | 1.7600          | 5.8123     |
| 1.8505        | 4.5062 | 3100 | 1.7855          | 5.9628     |
| 1.8382        | 4.6516 | 3200 | 1.7955          | 6.0225     |
| 1.7945        | 4.7971 | 3300 | 1.7754          | 5.9028     |
| 1.8238        | 4.9425 | 3400 | 1.7820          | 5.9418     |
| 1.573         | 5.0873 | 3500 | 1.8691          | 6.4823     |
| 1.4562        | 5.2327 | 3600 | 1.8905          | 6.6225     |
| 1.47          | 5.3782 | 3700 | 2.0037          | 7.4163     |
| 1.4649        | 5.5236 | 3800 | 1.8911          | 6.6268     |
| 1.4778        | 5.6691 | 3900 | 1.9307          | 6.8940     |
| 1.4985        | 5.8145 | 4000 | 1.9265          | 6.8655     |
| 1.4587        | 5.96   | 4100 | 1.9128          | 6.7720     |
| 1.258         | 6.1047 | 4200 | 2.0383          | 7.6773     |
| 1.2239        | 6.2502 | 4300 | 2.0444          | 7.7244     |
| 1.2186        | 6.3956 | 4400 | 2.0497          | 7.7658     |
| 1.2174        | 6.5411 | 4500 | 2.0454          | 7.7323     |
| 1.2051        | 6.6865 | 4600 | 2.0195          | 7.5345     |
| 1.2189        | 6.832  | 4700 | 2.0461          | 7.7376     |
| 1.2061        | 6.9775 | 4800 | 2.0435          | 7.7176     |
| 1.0575        | 7.1222 | 4900 | 2.0885          | 8.0727     |
| 1.048         | 7.2676 | 5000 | 2.0955          | 8.1296     |


### Framework versions

- PEFT 0.13.2
- Transformers 4.47.1
- Pytorch 2.1.0.post301
- Datasets 3.0.2
- Tokenizers 0.21.0