Downloads · 30 days
11
22% of all-time downloads
ukrt5-formality-transfer-ukrainian-v2
ViHr/ukrt5-formality-transfer-ukrainian-v2
ukrt5-formality-transfer-ukrainian-v2 is a machine learning model from ViHr. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
All-time downloads
49
Public
Parameters
306M
1.2 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.2 GB · 100%
At a glance
- Library
- transformers
- License
- mit
- Model type
- t5
- Access
- Public
- Created
- May 18, 2026
- Updated
- May 19, 2026
- SHA
- 71b51705
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
ukrt5-formality-transfer-ukrainian-v2
This model is a fine-tuned version of uaritm/ukrt5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4157
- Bleu: 27.4996
- Mean Pred Words: 8.7825
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.05
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Mean Pred Words |
|---|---|---|---|---|---|
| 5.7873 | 0.3220 | 2000 | 2.3162 | 15.1974 | 10.4785 |
| 4.4623 | 0.6440 | 4000 | 1.8967 | 23.6807 | 8.908 |
| 4.0487 | 0.9660 | 6000 | 1.7493 | 24.9980 | 8.7445 |
| 3.7900 | 1.2880 | 8000 | 1.6613 | 25.6807 | 8.826 |
| 3.6372 | 1.6100 | 10000 | 1.6000 | 26.0377 | 8.8365 |
| 3.5140 | 1.9321 | 12000 | 1.5544 | 26.4619 | 8.763 |
| 3.3908 | 2.2541 | 14000 | 1.5228 | 26.8278 | 8.859 |
| 3.3445 | 2.5761 | 16000 | 1.4974 | 26.5964 | 8.7925 |
| 3.3143 | 2.8981 | 18000 | 1.4715 | 27.1185 | 8.7945 |
| 3.2164 | 3.2201 | 20000 | 1.4577 | 26.9899 | 8.7645 |
| 3.2085 | 3.5421 | 22000 | 1.4430 | 27.3377 | 8.779 |
| 3.1858 | 3.8641 | 24000 | 1.4332 | 27.3143 | 8.788 |
| 3.1476 | 4.1861 | 26000 | 1.4237 | 27.5502 | 8.7675 |
| 3.1263 | 4.5081 | 28000 | 1.4195 | 27.5021 | 8.7745 |
| 3.1078 | 4.8301 | 30000 | 1.4157 | 27.4996 | 8.7825 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Library
- transformers
- Type
- t5
- License
- mit
- Created
- May 18, 2026
- Updated
- May 19, 2026