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--- |
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tags: |
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- summarization |
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- news |
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language: es |
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datasets: |
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- mlsum |
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--- |
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# Spanish RoBERTa2RoBERTa (roberta-base-bne) fine-tuned on MLSUM ES for summarization |
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## Model |
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[BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) (RoBERTa Checkpoint) |
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## Dataset |
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**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. |
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[MLSUM es](https://huggingface.co/datasets/viewer/?dataset=mlsum) |
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## Results |
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|Set|Metric| Value| |
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|----|------|------| |
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| Test |Rouge2 - mid -precision | 11.42| |
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| Test | Rouge2 - mid - recall | 10.58 | |
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| Test | Rouge2 - mid - fmeasure | 10.69| |
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| Test | Rouge1 - fmeasure | 28.83 | |
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| Test | RougeL - fmeasure | 23.15 | |
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Raw metrics using HF/metrics `rouge`: |
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```python |
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rouge = datasets.load_metric("rouge") |
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rouge.compute(predictions=results["pred_summary"], references=results["summary"]) |
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{'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)), |
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'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))} |
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rouge.compute(predictions=results["pred_summary"], references=results["summary"], rouge_types=["rouge2"])["rouge2"].mid |
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Score(precision=0.11423200347113865, recall=0.10588038944902506, fmeasure=0.1069921217219595) |
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``` |
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## Usage |
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```python |
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import torch |
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from transformers import RobertaTokenizerFast, EncoderDecoderModel |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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ckpt = 'Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization' |
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tokenizer = RobertaTokenizerFast.from_pretrained(ckpt) |
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model = EncoderDecoderModel.from_pretrained(ckpt).to(device) |
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def generate_summary(text): |
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inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt") |
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input_ids = inputs.input_ids.to(device) |
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attention_mask = inputs.attention_mask.to(device) |
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output = model.generate(input_ids, attention_mask=attention_mask) |
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return tokenizer.decode(output[0], skip_special_tokens=True) |
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text = "Your text here..." |
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generate_summary(text) |
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``` |
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Created by: [Narrativa](https://www.narrativa.com/) |
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About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI |
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