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--- |
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language: |
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- it |
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tags: |
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- Biomedical Language Modeling |
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widget: |
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- text: >- |
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L'asma allergica è una patologia dell'[MASK] respiratorio causata dalla |
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presenza di allergeni responsabili dell'infiammazione dell'albero |
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bronchiale. |
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example_title: Example 1 |
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- text: >- |
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Il pancreas produce diversi [MASK] molto importanti tra i quali l'insulina e |
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il glucagone. |
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example_title: Example 2 |
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- text: >- |
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Il GABA è un amminoacido ed è il principale neurotrasmettitore inibitorio |
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del [MASK]. |
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example_title: Example 3 |
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datasets: |
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- IVN-RIN/BioBERT_Italian |
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--- |
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🤗 + 📚🩺🇮🇹 = **BioBIT** |
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From this repository you can download the **BioBIT** (Biomedical Bert for ITalian) checkpoint. |
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**BioBIT** stems from [Italian XXL BERT](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased), obtained from a recent Wikipedia dump and various texts in Italian from the OPUS and OSCAR corpora collection, which sums up to the final corpus size of 81 GB and 13B tokens. |
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To pretrain **BioBIT**, we followed the general approach outlined in [BioBERT paper](https://arxiv.org/abs/1901.08746), built on the foundation of the BERT architecture. The pretraining objective is a combination of **MLM** (Masked Language Modelling) and **NSP** (Next Sentence Prediction). The MLM objective is based on randomly masking 15% of the input sequence, trying then to predict the missing tokens; for the NSP objective, instead, the model is given a couple of sentences and has to guess if the second comes after the first in the original document. |
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Due to the unavailability of an Italian equivalent for the millions of abstracts and full-text scientific papers used by English, BERT-based biomedical models, in this work we leveraged machine translation to obtain an Italian biomedical corpus based on PubMed abstracts and train **BioBIT**. More details in the paper. |
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**BioBIT** has been evaluated on 3 downstream tasks: **NER** (Named Entity Recognition), extractive **QA** (Question Answering), **RE** (Relation Extraction). |
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Here are the results, summarized: |
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- NER: |
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- [BC2GM](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb32) = 82.14% |
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- [BC4CHEMD](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb35) = 80.70% |
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- [BC5CDR(CDR)](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb31) = 82.15% |
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- [BC5CDR(DNER)](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb31) = 76.27% |
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- [NCBI_DISEASE](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb33) = 65.06% |
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- [SPECIES-800](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb34) = 61.86% |
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- QA: |
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- [BioASQ 4b](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb30) = 68.49% |
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- [BioASQ 5b](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb30) = 78.33% |
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- [BioASQ 6b](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb30) = 75.73% |
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- RE: |
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- [CHEMPROT](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb36) = 38.16% |
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- [BioRED](http://refhub.elsevier.com/S1532-0464(23)00152-1/sb37) = 67.15% |
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[Check the full paper](https://www.sciencedirect.com/science/article/pii/S1532046423001521) for further details, and feel free to contact us if you have some inquiry! |
