Instructions to use SNALYF/lab1_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SNALYF/lab1_finetuning with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SNALYF/lab1_finetuning") model = AutoModelForSeq2SeqLM.from_pretrained("SNALYF/lab1_finetuning") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: Helsinki-NLP/opus-mt-en-fr | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - kde4 | |
| metrics: | |
| - bleu | |
| model-index: | |
| - name: lab1_finetuning | |
| results: | |
| - task: | |
| name: Sequence-to-sequence Language Modeling | |
| type: text2text-generation | |
| dataset: | |
| name: kde4 | |
| type: kde4 | |
| config: en-fr | |
| split: train | |
| args: en-fr | |
| metrics: | |
| - name: Bleu | |
| type: bleu | |
| value: 48.897213805206235 | |
| <!-- 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. --> | |
| # lab1_finetuning | |
| This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0256 | |
| - Model Preparation Time: 0.0133 | |
| - Bleu: 48.8972 | |
| ## 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: 32 | |
| - 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 | |
| - training_steps: 5000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.57.6 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.22.2 | |