In 2007 the Raphael Research Resource project began to examine how complex conservation, scientific and art historical research could be combined in a flexible digital form. Exploring the presentation of interrelated high resolution images and text, along with how the d...

MONAPipe: Modes of Narration and Attribution Pipeline This repository is associated with the project group Modes of Narration and Attribution (MONA). We provide natural-language-processing tools for German, implemented in Python/spaCy. DFG Schwerpunktprogramm SPP 2207 &...

BERT-Kcr is a tool for prediction of lysine crotonylation sites by a transfer learning method with pre-trained BERT models.

BioVAE is a pre-trained latent variable language model for biomedical text mining. Large scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the a...

The archive NERD provides a collation of 11,081 radiocarbon dates from 1027 archaeological sites in the Near East from the Late Pleistocene until the Late Holocene (15 - 1.5 cal. kyr BP). These dates have been collected from existing online digital archives, and electro...

Supplementary material (scripts and dataset) for the Master's thesis in Archaeological Science at Leiden University. The two scripts are Jupyter notebooks created to run one supervised learning session (Section 4.1.3) and the clustering analysis with k-Means (Sectio...

En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitst...

En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/). The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitst...

topical_wordvec_models You first need to create a save folder for training. Download the [saved model](https://topicvecmodels.s3.eu-west-2.amazonaws.com/save/47/model) and place it in ./save/47/ to run the trained model. To construct the training set, refer to https://...

Transformer encoder-decoder for emotion detection in dialogues