Instructions to use nocti9/t5-pl-en-transliteration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nocti9/t5-pl-en-transliteration with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("nocti9/t5-pl-en-transliteration") model = AutoModelForSeq2SeqLM.from_pretrained("nocti9/t5-pl-en-transliteration", device_map="auto") - Notebooks
- Google Colab
- Kaggle
t5-pl-en-transliteration
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 10.7533
- Rouge1: 0.0238
- Rouge2: 0.0
- Rougel: 0.0235
- Rougelsum: 0.0233
- Gen Len: 18.04
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: 3e-06
- 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 74 | 11.7583 | 0.0188 | 0.0 | 0.0176 | 0.0185 | 18.58 |
| No log | 2.0 | 148 | 11.0046 | 0.0216 | 0.0 | 0.0217 | 0.0222 | 18.04 |
| No log | 3.0 | 222 | 10.7533 | 0.0238 | 0.0 | 0.0235 | 0.0233 | 18.04 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Base model
google-t5/t5-base