Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use Samavia/Summary_model_trained_on_reduced_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samavia/Summary_model_trained_on_reduced_data with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Samavia/Summary_model_trained_on_reduced_data") model = AutoModelForSeq2SeqLM.from_pretrained("Samavia/Summary_model_trained_on_reduced_data") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: Summary_model_trained_on_reduced_data | |
| 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. --> | |
| # Summary_model_trained_on_reduced_data | |
| 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: 1.6040 | |
| - Rouge1: 0.2182 | |
| - Rouge2: 0.0945 | |
| - Rougel: 0.1844 | |
| - Rougelsum: 0.1842 | |
| - Generated Length: 19.0 | |
| ## 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: 3 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:| | |
| | No log | 1.0 | 431 | 1.6239 | 0.2169 | 0.0932 | 0.1827 | 0.1827 | 19.0 | | |
| | 1.9203 | 2.0 | 862 | 1.6075 | 0.2167 | 0.0937 | 0.1829 | 0.1828 | 19.0 | | |
| | 1.822 | 3.0 | 1293 | 1.6040 | 0.2182 | 0.0945 | 0.1844 | 0.1842 | 19.0 | | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.19.1 | |