metadata

language:
  - it
tags:
  - summarization
datasets:
  - ARTeLab/ilpost
metrics:
  - rouge
base_model: gsarti/it5-base
model-index:
  - name: summarization_ilpost
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
        metrics:
          - type: rouge
            value: 11.9376
            name: ROUGE-1
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 3.5381
            name: ROUGE-2
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 9.9611
            name: ROUGE-L
            verified: true
            verifyToken: >-
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          - type: rouge
            value: 11.2146
            name: ROUGE-LSUM
            verified: true
            verifyToken: >-
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          - type: loss
            value: 8.635225296020508
            name: loss
            verified: true
            verifyToken: >-
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          - type: gen_len
            value: 18.9985
            name: gen_len
            verified: true
            verifyToken: >-
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summarization_ilpost

This model is a fine-tuned version of 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

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