bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization

Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization

bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization is a summarization model from Narrativa. Use it when you need a shorter version of a longer text. It is set up for transformers.

BSC-TeMU/roberta-base-bne (RoBERTa Checkpoint)

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At a glance

Task: Summarization
Library: transformers
Model type: encoder-decoder
Access: Public
Created: Mar 2, 2022
Updated: Aug 1, 2021
SHA: b7987d12

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Datasets

mlsum

Spaces

mrm8488/summarizer_mlsum

Model card

From the Hugging Face model README

Spanish RoBERTa2RoBERTa (roberta-base-bne) fine-tuned on MLSUM ES for summarization

Model

BSC-TeMU/roberta-base-bne (RoBERTa Checkpoint)

Dataset

MLSUM is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.

MLSUM es

Results

Set	Metric	Value
Test	Rouge2 - mid -precision	11.42
Test	Rouge2 - mid - recall	10.58
Test	Rouge2 - mid - fmeasure	10.69
Test	Rouge1 - fmeasure	28.83
Test	RougeL - fmeasure	23.15

Raw metrics using HF/metrics rouge:

rouge = datasets.load_metric("rouge")
rouge.compute(predictions=results["pred_summary"], references=results["summary"])

{'rouge1': AggregateScore(low=Score(precision=0.30393366820245, recall=0.27905239591639935, fmeasure=0.283148902808752), mid=Score(precision=0.3068521142101569, recall=0.2817252494122592, fmeasure=0.28560373425206464), high=Score(precision=0.30972608774202665, recall=0.28458152325781716, fmeasure=0.2883786700591887)),
 'rougeL': AggregateScore(low=Score(precision=0.24184668819794716, recall=0.22401171380621518, fmeasure=0.22624104698839514), mid=Score(precision=0.24470388406868163, recall=0.22665793214539162, fmeasure=0.2289118878817394), high=Score(precision=0.2476594458951327, recall=0.22932683203591905, fmeasure=0.23153001570662513))}
 
rouge.compute(predictions=results["pred_summary"], references=results["summary"], rouge_types=["rouge2"])["rouge2"].mid

Score(precision=0.11423200347113865, recall=0.10588038944902506, fmeasure=0.1069921217219595)

Usage

import torch
from transformers import RobertaTokenizerFast, EncoderDecoderModel
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization'
tokenizer = RobertaTokenizerFast.from_pretrained(ckpt)
model = EncoderDecoderModel.from_pretrained(ckpt).to(device)

def generate_summary(text):

   inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
   input_ids = inputs.input_ids.to(device)
   attention_mask = inputs.attention_mask.to(device)
   output = model.generate(input_ids, attention_mask=attention_mask)
   return tokenizer.decode(output[0], skip_special_tokens=True)
   
text = "Your text here..."
generate_summary(text)

Created by: Narrativa

About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI

Task: Summarization
Library: transformers
Type: encoder-decoder
Languages: es
Created: Mar 2, 2022
Updated: Aug 1, 2021