| language: | |
| - it | |
| tags: | |
| - summarization | |
| datasets: | |
| - ARTeLab/ilpost | |
| metrics: | |
| - rouge | |
| base_model: gsarti/it5-base | |
| model-index: | |
| - name: summarization_ilpost | |
| results: [] | |
| # summarization_ilpost | |
| This model is a fine-tuned version of [gsarti/it5-base](https://huggingface.co/gsarti/it5-base) on IlPost dataset for Abstractive Summarization. | |
| It achieves the following results: | |
| - Loss: 1.6020 | |
| - Rouge1: 33.7802 | |
| - Rouge2: 16.2953 | |
| - Rougel: 27.4797 | |
| - Rougelsum: 30.2273 | |
| - Gen Len: 45.3175 | |
| ## Usage | |
| ```python | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-ilpost") | |
| model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-ilpost") | |
| ``` | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 6 | |
| - eval_batch_size: 6 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 4.0 | |
| ### Framework versions | |
| - Transformers 4.12.0.dev0 | |
| - Pytorch 1.9.1+cu102 | |
| - Datasets 1.12.1 | |
| - Tokenizers 0.10.3 |