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--- |
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tags: |
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language: |
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- grc |
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model-index: |
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- name: aristoBERTo |
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results: [] |
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widget: |
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- text: "Πλάτων ὁ Περικτιόνης [MASK] γένος ἀνέφερεν εἰς Σόλωνα." |
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- text: "ὁ Κριτίας ἀπέβλεψε [MASK] τὴν θύραν." |
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- text: "πρῶτοι δὲ καὶ οὐνόματα ἱρὰ ἔγνωσαν καὶ [MASK] ἱροὺς ἔλεξαν." |
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--- |
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# aristoBERTo |
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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. |
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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. |
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aristoBERTo is provided by the [Diogenet project](https://diogenet.ucsd.edu) of the University of California, San Diego. |
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## Intended uses |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6323 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-------:|:---------------:| |
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| 1.377 | 20.0 | 3414220 | 1.6314 | |
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### Framework versions |
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- Transformers 4.14.0.dev0 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.16.1 |
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- Tokenizers 0.10.3 |
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