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