readme: ---
  tags:
  - summarization
  language:
  - it
  metrics:
  - rouge
  model-index:
  - name: summarization_ilpost
    results: []
  datasets:
  - ARTeLab/ilpost
  ---
  
  # 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