---
base_model: AIRI-Institute/gena-lm-bigbird-base-t2t
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: test_run
  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. -->

# test_run

This model is a fine-tuned version of [AIRI-Institute/gena-lm-bigbird-base-t2t](https://huggingface.co/AIRI-Institute/gena-lm-bigbird-base-t2t) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0475
- F1: 0.9938
- Mcc Score: 0.9866

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Mcc Score |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|
| 0.5793        | 1.0   | 62   | 0.2541          | 0.9150 | 0.8319    |
| 0.2401        | 2.0   | 124  | 0.1078          | 0.9645 | 0.9226    |
| 0.1257        | 3.0   | 186  | 0.1660          | 0.9631 | 0.9258    |
| 0.0658        | 4.0   | 248  | 0.0823          | 0.9797 | 0.9564    |
| 0.0225        | 5.0   | 310  | 0.0759          | 0.9861 | 0.9697    |
| 0.0296        | 6.0   | 372  | 0.0754          | 0.9876 | 0.9731    |
| 0.0247        | 7.0   | 434  | 0.1813          | 0.9697 | 0.9383    |
| 0.0124        | 8.0   | 496  | 0.1256          | 0.9811 | 0.9605    |
| 0.0253        | 9.0   | 558  | 0.0912          | 0.9843 | 0.9668    |
| 0.0099        | 10.0  | 620  | 0.0475          | 0.9938 | 0.9866    |


### Framework versions

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