readme: ---
  tags:
  language:
  - grc
  model-index:
  - name: aristoBERTo
    results: []
  widget:
  - text: "Πλάτων ὁ Περικτιόνης [MASK] γένος ἀνέφερεν εἰς Σόλωνα."
  - text: "ὁ Κριτίας ἀπέβλεψε [MASK] τὴν θύραν."
  - text: "πρῶτοι δὲ καὶ οὐνόματα ἱρὰ ἔγνωσαν καὶ [MASK] ἱροὺς ἔλεξαν."
  
  ---
  
  
  # aristoBERTo
  
  aristoBERTo is a transformer model for ancient Greek, a low resource  language. We initialized the pre-training with weights from [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1), a Greek version of BERT which was trained on a large corpus of modern Greek (~ 30 GB of texts). We continued the pre-training with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. 
  
  Applied to the processing of ancient Greek, aristoBERTo outperforms xlm-roberta-base and mdeberta in most downstream tasks like the labeling of POS, MORPH, DEP and LEMMA. 
  
  aristoBERTo is provided by the [Diogenet project](https://diogenet.ucsd.edu) of the University of California, San Diego. 
   
  
  ## Intended uses
  
  This model was created for fine-tuning with spaCy and the ancient Greek Universal Dependency datasets as well as a NER corpus produced by the [Diogenet project](https://diogenet.ucsd.edu). As a fill-mask model, AristoBERTo can also be used in the restoration of damaged Greek papyri, inscriptions, and manuscripts. 
  
  
  It achieves the following results on the evaluation set:
  - Loss: 1.6323
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step    | Validation Loss |
  |:-------------:|:-----:|:-------:|:---------------:|
  | 1.377         | 20.0  | 3414220 | 1.6314          |
  
  
  ### Framework versions
  
  - Transformers 4.14.0.dev0
  - Pytorch 1.10.0+cu102
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  