---
base_model: AIRI-Institute/gena-lm-bert-base-t2t
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: gut_1024-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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# gut_1024-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: 0.4700
- F1: 0.8448
- Mcc Score: 0.5728
- Accuracy: 0.7943

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 0.6922        | 0.02  | 100  | 0.6644          | 0.7478 | 0.0       | 0.5971   |
| 0.6582        | 0.04  | 200  | 0.6766          | 0.4699 | 0.2578    | 0.5570   |
| 0.6578        | 0.05  | 300  | 0.5801          | 0.8210 | 0.4886    | 0.7508   |
| 0.5793        | 0.07  | 400  | 0.5814          | 0.8013 | 0.4141    | 0.7082   |
| 0.5933        | 0.09  | 500  | 0.5877          | 0.7408 | 0.4494    | 0.7183   |
| 0.5616        | 0.11  | 600  | 0.5000          | 0.8229 | 0.5282    | 0.7766   |
| 0.5168        | 0.12  | 700  | 0.5027          | 0.8347 | 0.5540    | 0.7884   |
| 0.4788        | 0.14  | 800  | 0.5284          | 0.7922 | 0.5012    | 0.7572   |
| 0.5255        | 0.16  | 900  | 0.4859          | 0.8445 | 0.5696    | 0.7901   |
| 0.5404        | 0.18  | 1000 | 0.4700          | 0.8448 | 0.5728    | 0.7943   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2