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---
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license: cc-by-nc-sa-4.0
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---
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# MQDD - Multimodal Question Duplicity Detection
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This repository publishes trained models and other supporting materials for the paper
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[MQDD – Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://arxiv.org/abs/2203.14093). For more information, see the paper.
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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).
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To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https://huggingface.co/UWB-AIR/MQDD-pretrained).
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## Fine-tuned MQDD
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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:
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<img src="https://raw.githubusercontent.com/kiv-air/MQDD/master/img/architecture.png" width="700">
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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.
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```Python
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from transformers import AutoTokenizer, AutoModel
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tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates")
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model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates")
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```
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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:
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```Python
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from MQDD_model import ClsHeadModelMQDD
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model = ClsHeadModelMQDD("UWB-AIR/MQDD-duplicates")
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ckpt = torch.load("model.pt", map_location="cpu")
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model.load_state_dict(ckpt["model_state"])
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```
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## Licence
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
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## How should I cite the MQDD?
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For now, please cite [the Arxiv paper](https://arxiv.org/abs/2203.14093):
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```
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@misc{https://doi.org/10.48550/arxiv.2203.14093,
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doi = {10.48550/ARXIV.2203.14093},
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url = {https://arxiv.org/abs/2203.14093},
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author = {Pašek, Jan and Sido, Jakub and Konopík, Miloslav and Pražák, Ondřej},
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title = {MQDD -- Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain},
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publisher = {arXiv},
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year = {2022},
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copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
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}
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```
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