transformers safetensors mt5 text2text-generation generated_from_trainer base_model:cointegrated/rut5-small license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: cointegrated/rut5-small
tags:
- generated_from_trainer
model-index:
- name: finetune_t5_small_gusev_full
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetune_t5_small_gusev_full

This model is a fine-tuned version of [cointegrated/rut5-small](https://huggingface.co/cointegrated/rut5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7795

## 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: 0.0004
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 288
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7388        | 0.61  | 150  | 2.2027          |
| 2.5562        | 1.22  | 300  | 2.0326          |
| 2.4982        | 1.83  | 450  | 1.9607          |
| 2.4324        | 2.44  | 600  | 1.9077          |
| 2.4015        | 3.05  | 750  | 1.8711          |
| 2.3623        | 3.65  | 900  | 1.8451          |
| 2.3282        | 4.26  | 1050 | 1.8304          |
| 2.3072        | 4.87  | 1200 | 1.8120          |
| 2.2878        | 5.48  | 1350 | 1.8007          |
| 2.2689        | 6.09  | 1500 | 1.7919          |
| 2.2814        | 6.7   | 1650 | 1.7863          |
| 2.2443        | 7.31  | 1800 | 1.7835          |
| 2.2665        | 7.92  | 1950 | 1.7795          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
