Downloads · 30 days
5
17% of all-time downloads
mt5-stage2-singlish-si
RASHINI01/mt5-stage2-singlish-si
mt5-stage2-singlish-si is a machine learning model from RASHINI01. 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.
should probably proofread and complete it, then remove this comment. --
All-time downloads
29
Public
Parameters
582M
34.9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt4.7 GB · 50%
At a glance
- Library
- transformers
- Model type
- mt5
- Access
- Public
- Created
- Jan 2, 2026
- Updated
- Jan 2, 2026
- SHA
- 977cdc03
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. -->
mt5-stage2-singlish-si
This model is a fine-tuned version of RASHINI01/mt5-stage1-en-si on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8203
- Cer: 0.1506
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: 8
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 1.974 | 1.0 | 930 | 1.2075 | 0.2493 |
| 1.4873 | 2.0 | 1860 | 0.9611 | 0.1875 |
| 1.3199 | 3.0 | 2790 | 0.8785 | 0.1674 |
| 1.2609 | 4.0 | 3720 | 0.8305 | 0.1554 |
| 1.1909 | 5.0 | 4650 | 0.8203 | 0.1506 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
- Library
- transformers
- Type
- mt5
- Created
- Jan 2, 2026
- Updated
- Jan 2, 2026