readme: ---
  language: el
  tags:
  - roberta
  - twitter
  - Greek
  widget:
   - text: "<mask>: μεγαλη υποχωρηση του ιικου φορτιου σε αττικη και θεσσαλονικη"
  ---
  
  # Greek RoBERTa Uncased (v1)
  
  Pretrained model on Greek language using a masked language modeling (MLM) objective using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).
  
  ### Training data
  
  This model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.
  
  ### Preprocessing
  
  The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.
  
  ```python
  import unicodedata
  from transformers import pipeline
  
  def normalize_tweet(tweet, do_lower = True, do_strip_accents = True, do_split_word_numbers = False, user_fill = '', url_fill = ''):
      # your tweet pre-processing logic goes here
      # example... 
  
      # remove extra spaces, escape HTML, replace non-standard punctuation
      # replace any @user with blank
      # replace any link with blank
      # explode hashtags to strings (ex. #EU2019 ==> EU 2019)
      # remove all emojis
      
      # if do_split_word_numbers:
      #     splited strings containing any numbers 
          
      # standardize punctuation
      # remove unicode symbols
      
      if do_lower:
          tweet = tweet.lower()
      if do_strip_accents:
          tweet = strip_accents(tweet)
      
      return tweet.strip()
  
  def strip_accents(s):
      return ''.join(c for c in unicodedata.normalize('NFD', s)
                    if unicodedata.category(c) != 'Mn')
  
  nlp = pipeline('fill-mask', model = 'cvcio/roberta-el-uncased-twitter-v1')
  
  print(
      nlp(
          normalize_tweet(
              '<mask>: Μεγάλη υποχώρηση του ιικού φορτίου σε Αττική και Θεσσαλονίκη'
          )
      )
  )
  ```
  
  ### Pretraining
  
  The model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.
  
  ### Authors
  
  Dimitris Papaevagelou - [@andefined](https://github.com/andefined)
  
  ### About Us
  
  [Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
  