Instructions to use UWB-AIR/MQDD-duplicates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UWB-AIR/MQDD-duplicates with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UWB-AIR/MQDD-duplicates")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UWB-AIR/MQDD-duplicates") model = AutoModel.from_pretrained("UWB-AIR/MQDD-duplicates") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -14,7 +14,7 @@ To acquire the pre-trained model only, see the [UWB-AIR/MQDD-pretrained](https:/
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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="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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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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