Instructions to use s3h/mt5-small-finetuned-src-to-trg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s3h/mt5-small-finetuned-src-to-trg with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("s3h/mt5-small-finetuned-src-to-trg") model = AutoModelForSeq2SeqLM.from_pretrained("s3h/mt5-small-finetuned-src-to-trg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
mt5-small-finetuned-src-to-trg
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|---|---|---|---|---|---|
| No log | 1.0 | 40 | nan | 0.1737 | 3.1818 |
Framework versions
- Transformers 4.14.1
- Pytorch 1.6.0
- Datasets 1.16.1
- Tokenizers 0.10.3
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