Instructions to use krishnareddy/translation_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krishnareddy/translation_example with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("krishnareddy/translation_example") model = AutoModelForSeq2SeqLM.from_pretrained("krishnareddy/translation_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: translation_example | |
| results: [] | |
| <!-- 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. --> | |
| # translation_example | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.6028 | |
| - Bleu: 5.6496 | |
| - Gen Len: 17.6072 | |
| ## 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: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:| | |
| | 1.8533 | 1.0 | 6355 | 1.6277 | 5.4887 | 17.6146 | | |
| | 1.8293 | 2.0 | 12710 | 1.6028 | 5.6496 | 17.6072 | | |
| ### Framework versions | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 | |