Hugging Face's logo

ARTeLab
/
it5-summarization-mlsum

Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Inference Endpoints
Model card Files Community
5
it5-summarization-mlsum / README.md
autoevaluator's picture
autoevaluator HF staff
Add evaluation results on the samsum config and test split of samsum
319c277
preview code
|
raw
history blame
3.24 kB
metadata
tags:
  - summarization
language:
  - it
metrics:
  - rouge
model-index:
  - name: summarization_mlsum
    results:
      - task:
          type: summarization
          name: Summarization
        dataset:
          name: samsum
          type: samsum
          config: samsum
          split: test
        metrics:
          - name: ROUGE-1
            type: rouge
            value: 14.0255
            verified: true
          - name: ROUGE-2
            type: rouge
            value: 3.2328
            verified: true
          - name: ROUGE-L
            type: rouge
            value: 12.2979
            verified: true
          - name: ROUGE-LSUM
            type: rouge
            value: 13.1072
            verified: true
          - name: loss
            type: loss
            value: 2.0128023624420166
            verified: true
          - name: gen_len
            type: gen_len
            value: 18.9939
            verified: true
datasets:
  - ARTeLab/mlsum-it

summarization_mlsum

This model is a fine-tuned version of gsarti/it5-base on MLSum-it for Abstractive Summarization.

It achieves the following results:

  • Loss: 2.0190
  • Rouge1: 19.3739
  • Rouge2: 5.9753
  • Rougel: 16.691
  • Rougelsum: 16.7862
  • Gen Len: 32.5268

Usage 

from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ARTeLab/it5-summarization-mlsum")
model = T5ForConditionalGeneration.from_pretrained("ARTeLab/it5-summarization-mlsum")

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

Citation

More details and results in published work

@Article{info13050228,
    AUTHOR = {Landro, Nicola and Gallo, Ignazio and La Grassa, Riccardo and Federici, Edoardo},
    TITLE = {Two New Datasets for Italian-Language Abstractive Text Summarization},
    JOURNAL = {Information},
    VOLUME = {13},
    YEAR = {2022},
    NUMBER = {5},
    ARTICLE-NUMBER = {228},
    URL = {https://www.mdpi.com/2078-2489/13/5/228},
    ISSN = {2078-2489},
    ABSTRACT = {Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.},
    DOI = {10.3390/info13050228}
}