
# 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}
}
```
{"language": ["el"], "widget": [{"text": "\u03bc\u03b5\u03c3\u03b1 <mask> \u03b4\u03b9\u03ba\u03c4\u03c5\u03c9\u03c3\u03b7\u03c2"}]}
transformers pytorch roberta fill-mask el arxiv:1907.11692 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #el #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us 


# PaloBERT

A greek pre-trained language model based on RoBERTa. This model is an updated version of 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.


## Requirements



## 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



## Load Model


You can use this model directly with a pipeline for masked language modeling:



## Evaluation on MLM and Sentiment Analysis tasks

For detailed results refer to Thesis: 'Ανάλυση συναισθήματος κειμένου στα Ελληνικά με χρήση Δικτύων Μετασχηματιστών' (version - p2)

## Author

Pavlina Chatziantoniou, Georgios Alexandridis and Athanasios Voulodimos

## BibTeX entry and Citation info

URL


# PaloBERT

A greek pre-trained language model based on RoBERTa. This model is an updated version of 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. ## Requirements ## 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 ## Load Model


You can use this model directly with a pipeline for masked language modeling: ## Evaluation on MLM and Sentiment Analysis tasks

For detailed results refer to Thesis: 'Ανάλυση συναισθήματος κειμένου στα Ελληνικά με χρήση Δικτύων Μετασχηματιστών' (version - p2) ## Author

Pavlina Chatziantoniou, Georgios Alexandridis and Athanasios Voulodimos ## BibTeX entry and Citation info

URL
TAGS
#transformers #pytorch #roberta #fill-mask #el #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us 
 # PaloBERT

A greek pre-trained language model based on RoBERTa. This model is an updated version of 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. ## Requirements ## 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 ## Load Model


You can use this model directly with a pipeline for masked language modeling: ## Evaluation on MLM and Sentiment Analysis tasks

For detailed results refer to Thesis: 'Ανάλυση συναισθήματος κειμένου στα Ελληνικά με χρήση Δικτύων Μετασχηματιστών' (version - p2) ## Author

Pavlina Chatziantoniou, Georgios Alexandridis and Athanasios Voulodimos ## BibTeX entry and Citation info

URL
TAGS
#transformers #pytorch #roberta #fill-mask #el #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us 
# PaloBERT

A greek pre-trained language model based on RoBERTa. This model is an updated version of 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.## Requirements## 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## Load Model


You can use this model directly with a pipeline for masked language modeling:## Evaluation on MLM and Sentiment Analysis tasks

For detailed results refer to Thesis: 'Ανάλυση συναισθήματος κειμένου στα Ελληνικά με χρήση Δικτύων Μετασχηματιστών' (version - p2)## Author

Pavlina Chatziantoniou, Georgios Alexandridis and Athanasios Voulodimos## BibTeX entry and Citation info

URL