---
base_model: AIRI-Institute/gena-lm-bert-base-t2t
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: mus_promoter-finetuned-lora-bert-base-t2t
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mus_promoter-finetuned-lora-bert-base-t2t

This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-base-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1792
- F1: 0.9577
- Mcc Score: 0.9094
- Accuracy: 0.9531

## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Mcc Score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 0.6514        | 0.43  | 100  | 0.4785          | 0.9351 | 0.8414    | 0.9219   |
| 0.3646        | 0.85  | 200  | 0.9139          | 0.8276 | 0.5429    | 0.7656   |
| 0.5499        | 1.28  | 300  | 0.2149          | 0.9600 | 0.9039    | 0.9531   |
| 0.3001        | 1.71  | 400  | 0.3707          | 0.9351 | 0.8414    | 0.9219   |
| 0.227         | 2.14  | 500  | 0.1903          | 0.9474 | 0.8724    | 0.9375   |
| 0.2107        | 2.56  | 600  | 0.1515          | 0.9730 | 0.9359    | 0.9688   |
| 0.1793        | 2.99  | 700  | 0.2371          | 0.9444 | 0.8749    | 0.9375   |
| 0.1212        | 3.42  | 800  | 0.1112          | 0.9600 | 0.9039    | 0.9531   |
| 0.1338        | 3.85  | 900  | 0.1401          | 0.9730 | 0.9359    | 0.9688   |
| 0.0912        | 4.27  | 1000 | 0.1792          | 0.9577 | 0.9094    | 0.9531   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2