Update README.md

README.md

@@ -1,49 +1,27 @@
----
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-
----
-
-# {MODEL_NAME}
-
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
-
-<!--- Describe your model here -->
-
-## Usage (Sentence-Transformers)
-
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
-
-```
-pip install -U sentence-transformers
-```
-
-Then you can use the model like this:
-
-```python
-from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
-
-model = SentenceTransformer('{MODEL_NAME}')
-embeddings = model.encode(sentences)
-print(embeddings)
-```
-
-
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+license: mit
+language:
+- en
+- ru
+---
+
+# Multilingual E5 WB
+
+Fine-tuned version of default [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) for WB DS School and RAG project.
 
 ## Evaluation Results
 
 <!--- Describe how your model was evaluated -->
+As model is used as retriever, goal was to boost its performance at cosine similarity between question and answer. 
+With given dataset of QA pairs model performance on EmbeddingSimilarityEvaluator improved from 0.62 to 0.78
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
-
-## Training
-The model was trained with the parameters:
+## Fine Tuning
 
 **DataLoader**:
 
@@ -65,7 +43,6 @@ Parameters of the fit()-Method:
     "epochs": 10,
     "evaluation_steps": 100,
     "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
-    "max_grad_norm": 1,
     "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
     "optimizer_params": {
         "lr": 2e-05
@@ -75,18 +52,4 @@ Parameters of the fit()-Method:
     "warmup_steps": 790,
     "weight_decay": 0.01
 }
-```
-
-
-## Full Model Architecture
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 768, '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})
-  (2): Normalize()
-)
-```
-
-## Citing & Authors
-
-<!--- Describe where people can find more information -->
+```