Update README.md

README.md

@@ -20,7 +20,7 @@ library_name: transformers
 
 
 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.
-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)
+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)
 
 <!--- Describe your model here -->
 
@@ -66,9 +66,9 @@ cos(a, b)
 * The original weights was taken from [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2).
 * Training was conducted in two stages:
 1. In the first stage, the model was trained on Wikipedia texts (4 million texts) for three epochs.
-   <img src="https://github.com/FractalGPT/ModelEmbedderDistilation/blob/main/DistilSBERT/Train/1_st_en.JPG?raw=true" width=700 />
+   <img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/1_st_en.JPG?raw=true" width=700 />
 3. In the second stage, training was conducted on Wikipedia and dialog dataset for one epoch.
-   <img src="https://github.com/FractalGPT/ModelEmbedderDistilation/blob/main/DistilSBERT/Train/2_st_en.JPG?raw=true" width=700 />
+   <img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/DistilSBERT/Train/2_st_en.JPG?raw=true" width=700 />
 
 ## Full Model Architecture
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