metadata
base_model: ai-forever/ruT5-base
tags:
- generated_from_trainer
model-index:
- name: deabuse
results: []
deabuse
This model is a fine-tuned version of ai-forever/ruT5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0760
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.7858 | 0.05 | 400 | 0.7427 |
| 4.3608 | 0.1 | 800 | 0.4047 |
| 3.2919 | 0.15 | 1200 | 0.2366 |
| 3.2244 | 0.2 | 1600 | 0.2164 |
| 3.2491 | 0.25 | 2000 | 0.1757 |
| 1.6803 | 0.3 | 2400 | 0.1494 |
| 3.2828 | 0.35 | 2800 | 0.1500 |
| 3.39 | 0.4 | 3200 | 0.1510 |
| 0.0933 | 0.45 | 3600 | 0.1524 |
| 3.4757 | 0.5 | 4000 | 0.1423 |
| 3.1424 | 0.55 | 4400 | 0.1460 |
| 0.9616 | 0.6 | 4800 | 0.1178 |
| 2.6271 | 0.65 | 5200 | 0.1178 |
| 1.1441 | 0.7 | 5600 | 0.1190 |
| 3.018 | 0.75 | 6000 | 0.1136 |
| 1.3421 | 0.8 | 6400 | 0.0936 |
| 2.3062 | 0.85 | 6800 | 0.0994 |
| 2.5594 | 0.9 | 7200 | 0.0945 |
| 2.1381 | 0.95 | 7600 | 0.1061 |
| 1.0893 | 1.0 | 8000 | 0.1029 |
| 0.7525 | 1.05 | 8400 | 0.0978 |
| 2.1886 | 1.1 | 8800 | 0.0840 |
| 1.9948 | 1.15 | 9200 | 0.0952 |
| 0.7933 | 1.2 | 9600 | 0.0871 |
| 2.0757 | 1.25 | 10000 | 0.0853 |
| 0.6129 | 1.31 | 10400 | 0.0857 |
| 0.1338 | 1.36 | 10800 | 0.0936 |
| 2.6454 | 1.41 | 11200 | 0.0834 |
| 0.4243 | 1.46 | 11600 | 0.0891 |
| 0.6615 | 1.51 | 12000 | 0.0885 |
| 0.6634 | 1.56 | 12400 | 0.0942 |
| 0.5665 | 1.61 | 12800 | 0.0808 |
| 0.6661 | 1.66 | 13200 | 0.1021 |
| 1.1028 | 1.71 | 13600 | 0.0820 |
| 1.5217 | 1.76 | 14000 | 0.0769 |
| 0.7644 | 1.81 | 14400 | 0.0771 |
| 1.3725 | 1.86 | 14800 | 0.0800 |
| 0.846 | 1.91 | 15200 | 0.0788 |
| 1.7207 | 1.96 | 15600 | 0.0806 |
| 0.9188 | 2.01 | 16000 | 0.0806 |
| 1.4303 | 2.06 | 16400 | 0.0814 |
| 0.1599 | 2.11 | 16800 | 0.1072 |
| 0.1976 | 2.16 | 17200 | 0.0823 |
| 0.7077 | 2.21 | 17600 | 0.0830 |
| 1.8896 | 2.26 | 18000 | 0.0768 |
| 0.6957 | 2.31 | 18400 | 0.0826 |
| 0.7827 | 2.36 | 18800 | 0.0802 |
| 1.3298 | 2.41 | 19200 | 0.0791 |
| 0.2254 | 2.46 | 19600 | 0.0871 |
| 1.041 | 2.51 | 20000 | 0.0809 |
| 1.5451 | 2.56 | 20400 | 0.0838 |
| 1.6318 | 2.61 | 20800 | 0.0801 |
| 1.8972 | 2.66 | 21200 | 0.0774 |
| 1.8895 | 2.71 | 21600 | 0.0762 |
| 0.7721 | 2.76 | 22000 | 0.0740 |
| 0.3528 | 2.81 | 22400 | 0.0781 |
| 1.325 | 2.86 | 22800 | 0.0770 |
| 0.0282 | 2.91 | 23200 | 0.0785 |
| 1.6303 | 2.96 | 23600 | 0.0760 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2