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---
base_model: d0rj/rut5-base-summ
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summary_resume_keywords
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. -->
# summary_resume_keywords
This model is a fine-tuned version of [d0rj/rut5-base-summ](https://huggingface.co/d0rj/rut5-base-summ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9737
- Rouge1: 0.2285
- Rouge2: 0.1524
- Rougel: 0.2285
- Rougelsum: 0.2285
- Gen Len: 51.3333
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 90 | 2.7766 | 0.2485 | 0.1111 | 0.2485 | 0.2485 | 52.0 |
| No log | 2.0 | 180 | 2.7734 | 0.2556 | 0.1404 | 0.2389 | 0.2389 | 53.6667 |
| No log | 3.0 | 270 | 2.7763 | 0.2882 | 0.1368 | 0.2557 | 0.2557 | 51.6667 |
| No log | 4.0 | 360 | 2.7921 | 0.2722 | 0.1404 | 0.2389 | 0.2389 | 58.3333 |
| No log | 5.0 | 450 | 2.8146 | 0.2778 | 0.1622 | 0.2607 | 0.2607 | 57.3333 |
| 2.1351 | 6.0 | 540 | 2.8387 | 0.2778 | 0.1622 | 0.2607 | 0.2607 | 57.3333 |
| 2.1351 | 7.0 | 630 | 2.8569 | 0.2778 | 0.1622 | 0.2607 | 0.2607 | 57.3333 |
| 2.1351 | 8.0 | 720 | 2.8736 | 0.2538 | 0.1524 | 0.2538 | 0.2538 | 55.3333 |
| 2.1351 | 9.0 | 810 | 2.8883 | 0.2538 | 0.1524 | 0.2538 | 0.2538 | 55.3333 |
| 2.1351 | 10.0 | 900 | 2.9025 | 0.2315 | 0.1524 | 0.2315 | 0.2315 | 51.0 |
| 2.1351 | 11.0 | 990 | 2.9161 | 0.2315 | 0.1524 | 0.2315 | 0.2315 | 51.0 |
| 1.7131 | 12.0 | 1080 | 2.9269 | 0.2315 | 0.1524 | 0.2315 | 0.2315 | 51.0 |
| 1.7131 | 13.0 | 1170 | 2.9354 | 0.226 | 0.1524 | 0.226 | 0.226 | 54.0 |
| 1.7131 | 14.0 | 1260 | 2.9427 | 0.226 | 0.1524 | 0.226 | 0.226 | 54.0 |
| 1.7131 | 15.0 | 1350 | 2.9471 | 0.2272 | 0.1524 | 0.2272 | 0.2272 | 53.6667 |
| 1.7131 | 16.0 | 1440 | 2.9509 | 0.226 | 0.1524 | 0.226 | 0.226 | 54.0 |
| 1.5914 | 17.0 | 1530 | 2.9558 | 0.2272 | 0.1524 | 0.2272 | 0.2272 | 53.6667 |
| 1.5914 | 18.0 | 1620 | 2.9589 | 0.226 | 0.1524 | 0.226 | 0.226 | 54.0 |
| 1.5914 | 19.0 | 1710 | 2.9636 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 51.0 |
| 1.5914 | 20.0 | 1800 | 2.9660 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 51.0 |
| 1.5914 | 21.0 | 1890 | 2.9687 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 50.3333 |
| 1.5914 | 22.0 | 1980 | 2.9709 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 50.3333 |
| 1.5508 | 23.0 | 2070 | 2.9736 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 50.3333 |
| 1.5508 | 24.0 | 2160 | 2.9742 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 50.3333 |
| 1.5508 | 25.0 | 2250 | 2.9737 | 0.2285 | 0.1524 | 0.2285 | 0.2285 | 51.3333 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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