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Type Name Latest commit message Commit time colabs Initial commit Mar 8, 2022 images Initial commit Mar 8, 2022 ithaca Initial commit Mar 8, 2022 train Initial commit Mar 8, 2022 .gitignore Initial commit Mar 8, 2022 AUTHORS Initial commit Mar 8, 2022 CONTRIBUTING.md Initial commit Mar 8, 2022 LICENSE Initial commit Mar 8, 2022 README.md Fix README typo Mar 9, 2022 example_input.txt Initial commit Mar 8, 2022 inference_example.py Initial commit Mar 8, 2022 requirements.txt Initial commit Mar 8, 2022 setup.py Initial commit Mar 8, 2022 View code Restoring and attributing ancient texts using deep neural networks References Ithaca inference online Ithaca inference offline Dataset generation Training Ithaca License README.md Ithaca logo Restoring and attributing ancient texts using deep neural networks Yannis Assael^1,*, Thea Sommerschield^2,3,*, Brendan Shillingford^1, Mahyar Bordbar^1, John Pavlopoulos^4, Marita Chatzipanagiotou^4, Ion Androutsopoulos^4, Jonathan Prag^3, Nando de Freitas^1 ^1 DeepMind, United Kingdom ^2 Ca' Foscari University of Venice, Italy ^3 University of Oxford, United Kingdom ^4 Athens University of Economics and Business, Greece ^* Authors contributed equally to this work --------------------------------------------------------------------- Open In Colab Ancient History relies on disciplines such as Epigraphy, the study of inscribed texts known as "inscriptions", for evidence of the thought, language, society and history of past civilizations. However, over the centuries many inscriptions have been damaged to the point of illegibility, transported far from their original location, and their date of writing is steeped in uncertainty. We present Ithaca, the first Deep Neural Network for the textual restoration, geographical and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian's workflow: its architecture focuses on collaboration, decision support, and interpretability. Restoration of damaged inscription Restoration of damaged inscription: this inscription (IG I^3 4B) records a decree concerning the Acropolis of Athens and dates 485/4 BCE. (CC BY-SA 3.0, WikiMedia) While Ithaca alone achieves 62% accuracy when restoring damaged texts, as soon as historians use Ithaca their performance leaps from 25% to 72%, confirming this synergistic research aid's impact. Ithaca can attribute inscriptions to their original location with 71% accuracy and can date them with a distance of less than 30 years from ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in Ancient History. This work shows how models like Ithaca can unlock the cooperative potential between AI and historians, transformationally impacting the way we study and write about one of the most significant periods in human history. Ithaca architecture Ithaca's architecture processing the phrase "demo to athenaion" ("the people of Athens"). The first 3 characters of the phrase were hidden and their restoration is proposed. In tandem, Ithaca also predicts the inscription's region and date. References * Nature article * DeepMind blog When using any of this project's source code, please cite: @article{asssome2022restoring, title = {Restoring and attributing ancient texts using deep neural networks}, author = {Assael*, Yannis and Sommerschield*, Thea and Shillingford, Brendan and Bordbar, Mahyar and Pavlopoulos, John and Chatzipanagiotou, Marita and Androutsopoulos, Ion and Prag, Jonathan and de Freitas, Nando}, doi = {10.1038/s41586-022-04448-z}, journal = {Nature}, year = {2022} } Ithaca inference online To aid further research in the field we created an online interactive python notebook, where researchers can query one of our trained models to get text restorations, visualise attention weights, and more. * Ithaca Interactive Interface * Google Colab for using Ithaca for your research Ithaca inference offline Advanced users who want to perform inference using the trained model may want to do so manually using the ithaca library directly. First, to install the ithaca library and its dependencies, run: pip install . Then, download the model via curl --output checkpoint.pkl https://storage.googleapis.com/ithaca-resources/models/checkpoint_v1.pkl An example of using the library can be run via python inference_example.py --input_file=example_input.txt which will run restoration and attribution on the text in example_input.txt. To run it with different input text, run python inference_example.py --input="..." # or using text in a UTF-8 encoded text file: python inference_example.py --input_file=some_other_input_file.txt The restoration or attribution JSON can be saved to a file: python inference_example.py \ --input_file=example_input.txt \ --attribute_json=attribute.json \ --restore_json=restore.json For full help, run: python inference_example.py --help Dataset generation Ithaca was trained on The Packard Humanities Institute's "Searchable Greek Inscriptions" public dataset. The processing workflow for generating the machine-actionable text and metadata, as well as further details on the train, validation and test splits are available at I.PHI dataset. Training Ithaca See train/README.md for instructions. License Apache License, Version 2.0 About Restoring and attributing ancient texts using deep neural networks Resources Readme License Apache-2.0 License Stars 146 stars Watchers 7 watching Forks 14 forks Languages * Jupyter Notebook 82.1% * Python 17.8% * Shell 0.1% * (c) 2022 GitHub, Inc. * Terms * Privacy * Security * Status * Docs * Contact GitHub * Pricing * API * Training * Blog * About You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.