---
base_model: UrukHan/t5-russian-summarization
metrics:
- f1
tags:
- generated_from_trainer
model-index:
- name: last_run_UrukHan_t5-russian-summarization
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/diffraction-zebra/RoseltorgItemsTunning/runs/3b4e0ldl)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/diffraction-zebra/RoseltorgItemsTunning/runs/pvl2qfvv)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/diffraction-zebra/RoseltorgItemsTunning/runs/o04syxyd)
# last_run_UrukHan_t5-russian-summarization

This model is a fine-tuned version of [UrukHan/t5-russian-summarization](https://huggingface.co/UrukHan/t5-russian-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6429
- F1: 0.4241

## 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: 0.00012
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | F1     |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.8758        | 0.6667 | 200  | 0.9457          | 0.3173 |
| 0.7212        | 1.3333 | 400  | 0.8009          | 0.3486 |
| 0.5636        | 2.0    | 600  | 0.7738          | 0.3699 |
| 0.4931        | 2.6667 | 800  | 0.7189          | 0.3951 |
| 0.4748        | 3.3333 | 1000 | 0.6766          | 0.4020 |
| 0.4125        | 4.0    | 1200 | 0.6658          | 0.4143 |
| 0.3326        | 4.6667 | 1400 | 0.6481          | 0.4243 |
| 0.2676        | 5.3333 | 1600 | 0.6494          | 0.4063 |
| 0.3643        | 6.0    | 1800 | 0.6383          | 0.4175 |
| 0.2064        | 6.6667 | 2000 | 0.6429          | 0.4241 |


### Framework versions

- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1