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README.md
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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This is a fast and small model for solving the problem of determining the proximity between sentences, in the future we will reduce and speed it up. [Project](https://github.com/FractalGPT/
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<!--- Describe your model here -->
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* The original weights was taken from [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
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* Training was conducted in two stages:
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1. In the first stage, the model was trained on Wikipedia texts (4 million texts) for three epochs.
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<img src="https://github.com/FractalGPT/
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3. In the second stage, training was conducted on Wikipedia and dialog dataset for one epoch.
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<img src="https://github.com/FractalGPT/
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## Full Model Architecture
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```
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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This is a fast and small model for solving the problem of determining the proximity between sentences, in the future we will reduce and speed it up. [Project](https://github.com/FractalGPT/ModelEmbedderDistillation)
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<!--- Describe your model here -->
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* The original weights was taken from [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
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* Training was conducted in two stages:
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1. In the first stage, the model was trained on Wikipedia texts (4 million texts) for three epochs.
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<img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/1_st_en.JPG?raw=true" width=700 />
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3. In the second stage, training was conducted on Wikipedia and dialog dataset for one epoch.
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<img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/2_st_en.JPG?raw=true" width=700 />
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## Full Model Architecture
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```
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