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--- |
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language: |
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- el |
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widget: |
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- text: "μεσα <mask> δικτυωσης" |
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--- |
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# PaloBERT |
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A greek pre-trained language model based on [RoBERTa](https://arxiv.org/abs/1907.11692). This model is an updated version of [palobert-base-greek-uncased-v1](https://huggingface.co/gealexandri/palobert-base-greek-uncased-v1). |
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## Pre-training data |
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The model is pre-trained on a corpus of 458,293 documents collected from greek social media (Twitter, Instagram, Facebook and YouTube). A RoBERTa tokenizer trained from scratch on the same corpus is also included. |
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The corpus has been provided by [Palo LTD](http://www.paloservices.com/). |
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## Requirements |
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``` |
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pip install transformers |
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pip install torch |
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``` |
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## Pre-processing details |
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In order to use this model, the text needs to be pre-processed as follows: |
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* remove all greek diacritics |
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* convert to lowercase |
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* remove all punctuation |
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```python |
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import re |
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import unicodedata |
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def preprocess(text, default_replace=""): |
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text = text.lower() |
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text = unicodedata.normalize('NFD',text).translate({ord('\N{COMBINING ACUTE ACCENT}'):None}) |
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text = re.sub(r'[^\w\s]', default_replace, text) |
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return text |
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``` |
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## Load Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("pchatz/palobert-base-greek-social-media-v2") |
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model = AutoModelForMaskedLM.from_pretrained("pchatz/palobert-base-greek-social-media-v2") |
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``` |
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You can use this model directly with a pipeline for masked language modeling: |
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```python |
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from transformers import pipeline |
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fill = pipeline('fill-mask', model=model, tokenizer=tokenizer) |
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fill(f'μεσα {fill.tokenizer.mask_token} δικτυωσης') |
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[{'score': 0.8760559558868408, |
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'token': 12853, |
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'token_str': ' κοινωνικης', |
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'sequence': 'μεσα κοινωνικης δικτυωσης'}, |
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{'score': 0.020922638475894928, |
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'token': 1104, |
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'token_str': ' μεσα', |
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'sequence': 'μεσα μεσα δικτυωσης'}, |
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{'score': 0.017568595707416534, |
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'token': 337, |
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'token_str': ' της', |
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'sequence': 'μεσα της δικτυωσης'}, |
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{'score': 0.006678201723843813, |
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'token': 1258, |
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'token_str': 'τικης', |
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'sequence': 'μεσατικης δικτυωσης'}, |
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{'score': 0.004737381357699633, |
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'token': 16245, |
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'token_str': 'τερης', |
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'sequence': 'μεσατερης δικτυωσης'}] |
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``` |
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## Evaluation on MLM and Sentiment Analysis tasks |
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For detailed results refer to Thesis: ['Ανάλυση συναισθήματος κειμένου στα Ελληνικά με χρήση Δικτύων Μετασχηματιστών']( http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18623) (version - p2) |
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## Author |
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[Pavlina Chatziantoniou](https://huggingface.co/pchatz), [Georgios Alexandridis](https://huggingface.co/gealexandri) and Athanasios Voulodimos |
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## BibTeX entry and Citation info |
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http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18623 |
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```bibtex |
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@Article{info12080331, |
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AUTHOR = {Alexandridis, Georgios and Varlamis, Iraklis and Korovesis, Konstantinos and Caridakis, George and Tsantilas, Panagiotis}, |
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TITLE = {A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media}, |
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JOURNAL = {Information}, |
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VOLUME = {12}, |
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YEAR = {2021}, |
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NUMBER = {8}, |
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ARTICLE-NUMBER = {331}, |
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URL = {https://www.mdpi.com/2078-2489/12/8/331}, |
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ISSN = {2078-2489}, |
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DOI = {10.3390/info12080331} |
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} |
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``` |
