Update README.md

README.md

@@ -9,10 +9,14 @@ tags:
 
 ---
 
-# TatonkaHF/bge-m3-retromae_en_ru
+# bge-m3 model for english and russian
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+This is a tokenizer shrinked version of [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae).
 
+The current model has only English and Russian tokens left in the vocabulary.
+Thus, the vocabulary is 21% of the original, and number of parameters in the whole model is 63.3% of the original, without any loss in the quality of English and Russian embeddings.
+
+Notebook with code is available [here](https://github.com/BlessedTatonka/pet_projects/tree/main/huggingface/bge-m3-shrinking).
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
@@ -72,6 +76,15 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+## Specs 
+
+Other bge-m3 models are also shrinked.
+
+| Model name                |
+|---------------------------|
+| [bge-m3-retromae_en_ru](https://huggingface.co/TatonkaHF/bge-m3-retromae_en_ru)     |
+| [bge-m3-unsupervised_en_ru](https://huggingface.co/TatonkaHF/bge-m3-unsupervised_en_ru) |
+| [bge-m3_en_ru](https://huggingface.co/TatonkaHF/bge-m3_en_ru)     
 
 
 ## Evaluation Results
@@ -80,16 +93,20 @@ print(sentence_embeddings)
 
 For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=TatonkaHF/bge-m3-retromae_en_ru)
 
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
-  (0): Transformer({'max_seq_length': 8194, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
   (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```
 
-## Citing & Authors
+## Reference:
+
+Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, Zheng Liu. [BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://arxiv.org/abs/2402.03216).
+
+Inspired by [LaBSE-en-ru](https://huggingface.co/cointegrated/LaBSE-en-ru) and [https://discuss.huggingface.co/t/tokenizer-shrinking-recipes/8564/1](https://discuss.huggingface.co/t/tokenizer-shrinking-recipes/8564/1).
+
+License: [mit](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md)
 
 <!--- Describe where people can find more information -->