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README.md

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+
+# MQDD - Multimodal Question Duplicity Detection
+
+This repository publishes trained models and other supporting materials for the paper 
+[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
+The Stack Overflow Datasets (SOD) and Stack Overflow Duplicity Dataset (SODD) presented in the paper can be obtained from our [Stack Overflow Dataset repository](https://github.com/kiv-air/StackOverflowDataset).
+
+To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https://huggingface.co/UWB-AIR/MQDD-pretrained).
+
+## Fine-tuned MQDD
+
+We release a fine-tuned version of our MQDD model for duplicate detection task. The model's architecture follows the architecture of a two-tower model as depicted in the figure below:
+
+<img src="img/architecture.png" width="700">
+
+A self-standing encoder without a duplicate detection head can be loaded using the following source code snippet. Such a model can be used for building search systems based, for example, on [Faiss](https://github.com/facebookresearch/faiss) library.
+
+```Python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates")
+model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates")
+```
+
+A checkpoint of a full two-tower model can than be obtained from our [GoogleDrive folder](https://drive.google.com/drive/folders/1CYiqF2GJ2fSQzx_oM4-X_IhpObi4af5Q?usp=sharing). To load the model, one needs to use the model's implementation from `models/MQDD_model.py` in our [GitHub repository](https://github.com/kiv-air/MQDD). To construct the model and load it's checkpoint, use the following source code:
+
+```Python
+from MQDD_model import ClsHeadModelMQDD
+
+model = ClsHeadModelMQDD("UWB-AIR/MQDD-duplicates")
+ckpt = torch.load("model.pt",  map_location="cpu")
+model.load_state_dict(ckpt["model_state"])
+```
+
+## Licence
+This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
+
+## How should I cite the MQDD? 
+For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
+```
+@misc{https://doi.org/10.48550/arxiv.2203.14093,
+  doi = {10.48550/ARXIV.2203.14093},
+  url = {https://arxiv.org/abs/2203.14093},
+  author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej},
+  title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
+}
+```