---
base_model: AIRI-Institute/gena-lm-bert-large-t2t
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: gut_1024-finetuned-lora-bert-large-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-large-t2t

This model is a fine-tuned version of [AIRI-Institute/gena-lm-bert-large-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bert-large-t2t) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4378
- F1: 0.8676
- Mcc Score: 0.6476
- Accuracy: 0.8315

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:--------:|
| 0.6357        | 0.02  | 100  | 0.5047          | 0.8379 | 0.5616    | 0.7918   |
| 0.5873        | 0.04  | 200  | 1.0646          | 0.6898 | 0.4070    | 0.6829   |
| 0.5661        | 0.05  | 300  | 0.4921          | 0.8386 | 0.5593    | 0.7901   |
| 0.5018        | 0.07  | 400  | 0.4753          | 0.8476 | 0.5791    | 0.7927   |
| 0.5461        | 0.09  | 500  | 0.4841          | 0.8465 | 0.5947    | 0.8074   |
| 0.4555        | 0.11  | 600  | 0.4521          | 0.8580 | 0.6239    | 0.8209   |
| 0.4155        | 0.12  | 700  | 0.4519          | 0.8655 | 0.6386    | 0.8264   |
| 0.438         | 0.14  | 800  | 0.4634          | 0.8539 | 0.6130    | 0.8159   |
| 0.4306        | 0.16  | 900  | 0.4298          | 0.8615 | 0.6232    | 0.8150   |
| 0.4791        | 0.18  | 1000 | 0.4378          | 0.8676 | 0.6476    | 0.8315   |


### Framework versions

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