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A Greek edition of Google's BERT pre-trained language model.

Pre-training corpora

The pre-training corpora of bert-base-greek-uncased-v1 include:

Future release will also include:

  • The entire corpus of Greek legislation, as published by the National Publication Office,
  • The entire corpus of EU legislation (Greek translation), as published in Eur-Lex.

Pre-training details

  • We trained BERT using the official code provided in Google BERT's github repository (
  • We released a model similar to the English bert-base-uncased model (12-layer, 768-hidden, 12-heads, 110M parameters).
  • We chose to follow the same training set-up: 1 million training steps with batches of 256 sequences of length 512 with an initial learning rate 1e-4.
  • We were able to use a single Google Cloud TPU v3-8 provided for free from TensorFlow Research Cloud (TFRC), while also utilizing GCP research credits. Huge thanks to both Google programs for supporting us!


We published bert-base-greek-uncased-v1 as part of Hugging Face's Transformers repository. So, you need to install the transfomers library through pip along with PyTorch or Tensorflow 2.

pip install unicodedata
pip install transfomers
pip install (torch|tensorflow)

Pre-process text (Deaccent - Lower)

In order to use bert-base-greek-uncased-v1, you have to pre-process texts to lowercase letters and remove all Greek diacritics.

import unicodedata

def strip_accents_and_lowercase(s):
   return ''.join(c for c in unicodedata.normalize('NFD', s)
                  if unicodedata.category(c) != 'Mn').lower()

accented_string = "Αυτή είναι η Ελληνική έκδοση του BERT."
unaccented_string = strip_accents_and_lowercase(accented_string)

print(unaccented_string) # αυτη ειναι η ελληνικη εκδοση του bert.

Load Pretrained Model

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")
model = AutoModel.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")

Use Pretrained Model as a Language Model

import torch
from transformers import *

# Load model and tokenizer
tokenizer_greek = AutoTokenizer.from_pretrained('nlpaueb/bert-base-greek-uncased-v1')
lm_model_greek = AutoModelWithLMHead.from_pretrained('nlpaueb/bert-base-greek-uncased-v1')

# ================ EXAMPLE 1 ================
text_1 = 'O ποιητής έγραψε ένα [MASK] .'
# EN: 'The poet wrote a [MASK].'
input_ids = tokenizer_greek.encode(text_1)
# ['[CLS]', 'o', 'ποιητης', 'εγραψε', 'ενα', '[MASK]', '.', '[SEP]']
outputs = lm_model_greek(torch.tensor([input_ids]))[0]
print(tokenizer_greek.convert_ids_to_tokens(outputs[0, 5].max(0)[1].item()))
# the most plausible prediction for [MASK] is "song"

# ================ EXAMPLE 2 ================
text_2 = 'Είναι ένας [MASK] άνθρωπος.'
# EN: 'He is a [MASK] person.'
input_ids = tokenizer_greek.encode(text_1)
# ['[CLS]', 'ειναι', 'ενας', '[MASK]', 'ανθρωπος', '.', '[SEP]']
outputs = lm_model_greek(torch.tensor([input_ids]))[0]
print(tokenizer_greek.convert_ids_to_tokens(outputs[0, 3].max(0)[1].item()))
# the most plausible prediction for [MASK] is "good"

# ================ EXAMPLE 3 ================
text_3 = 'Είναι ένας [MASK] άνθρωπος και κάνει συχνά [MASK].'
# EN: 'He is a [MASK] person he does frequently [MASK].'
input_ids = tokenizer_greek.encode(text_3)
# ['[CLS]', 'ειναι', 'ενας', '[MASK]', 'ανθρωπος', 'και', 'κανει', 'συχνα', '[MASK]', '.', '[SEP]']
outputs = lm_model_greek(torch.tensor([input_ids]))[0]
print(tokenizer_greek.convert_ids_to_tokens(outputs[0, 8].max(0)[1].item()))
# the most plausible prediction for the second [MASK] is "trips"

Evaluation on downstream tasks



Ilias Chalkidis on behalf of AUEB's Natural Language Processing Group

| Github: @ilias.chalkidis | Twitter: @KiddoThe2B |

About Us

AUEB's Natural Language Processing Group develops algorithms, models, and systems that allow computers to process and generate natural language texts.

The group's current research interests include:

  • question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering,
  • natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content,
  • information extraction and opinion mining, including legal text analytics and sentiment analysis,
  • natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning.

The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business.


A Greek edition of BERT pre-trained language model








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