Instructions to use Tsedeniya/finetuned_byT5base_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tsedeniya/finetuned_byT5base_multi with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Tsedeniya/finetuned_byT5base_multi") model = AutoModelForSeq2SeqLM.from_pretrained("Tsedeniya/finetuned_byT5base_multi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finetuned_byT5base_multi
This model is a fine-tuned version of google/byt5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7726
- Chrf++: 61.5912
- Gen Len: 112.0672
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: 1
- seed: 42
- gradient_accumulation_steps: 8
- 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
- num_epochs: 13
Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf++ | Gen Len |
|---|---|---|---|---|---|
| 3.1885 | 1.9967 | 1052 | 0.6582 | 51.9871 | 111.2477 |
| 1.2729 | 3.9929 | 2104 | 0.6439 | 62.7503 | 105.3778 |
| 0.8070 | 5.9891 | 3156 | 0.7583 | 64.0368 | 103.529 |
| 0.6037 | 7.9853 | 4208 | 0.8029 | 62.3811 | 107.0042 |
| 0.4911 | 9.9815 | 5260 | 0.8241 | 63.2761 | 102.2351 |
| 0.4495 | 11.9777 | 6312 | 0.7726 | 61.5912 | 112.0672 |
Framework versions
- Transformers 5.5.0
- Pytorch 2.11.0+cu130
- Datasets 4.3.0
- Tokenizers 0.22.2
- Downloads last month
- 41
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Tsedeniya/finetuned_byT5base_multi
Base model
google/byt5-base