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@@ -4,12 +4,22 @@ tags:
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  - sentence-transformers
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  - feature-extraction
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  - sentence-similarity
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-
 
 
 
 
 
 
 
 
 
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  ---
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  # FractalGPT/SberDistil
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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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  <!--- Describe your model here -->
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  print(embeddings)
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  ```
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-
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- ## Evaluation Results
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-
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- <!--- Describe how your model was evaluated -->
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-
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=FractalGPT/SberDistil)
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-
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-
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  ## Full Model Architecture
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  ```
@@ -49,8 +59,4 @@ SentenceTransformer(
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  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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  (2): Dense({'in_features': 312, 'out_features': 384, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
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  )
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- ```
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-
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- ## Citing & Authors
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-
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- <!--- Describe where people can find more information -->
 
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  - sentence-transformers
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  - feature-extraction
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  - sentence-similarity
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+ license: apache-2.0
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+ datasets:
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+ - wikimedia/wikipedia
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+ - SiberiaSoft/SiberianPersonaChat-2
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+ language:
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+ - ru
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+ - en
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+ metrics:
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+ - mse
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+ library_name: transformers
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  ---
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  # FractalGPT/SberDistil
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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/ModelEmbedderDistilation)
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  <!--- Describe your model here -->
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  print(embeddings)
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  ```
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+ ## Training
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+ * The original weights was taken from [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
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+ *
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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/ModelEmbedderDistilation/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, a dialog dataset, and NLI for one epoch.
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+ <img src="https://github.com/FractalGPT/ModelEmbedderDistilation/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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  (1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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  (2): Dense({'in_features': 312, 'out_features': 384, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
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  )
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+ ```