added example code and readme

.gitignore

@@ -150,6 +150,9 @@ dmypy.json
 # pytype static type analyzer
 .pytype/
 
+# ml Model
+fabelous-mini-embedder
+
 # Cython debug symbols
 cython_debug/
 

README.md

@@ -1,3 +1,50 @@
-# fabelous-embeddings
+# Fabelous Embedder Mini
 
-This is the fabelous-embeddings model that is based on the jina-embeddings-v2-base-de Model from Jina AI.
+This repository contains a custom sentence transformer model named **fabelous-mini-embedder**, trained on 13 different programming languages and English.
+
+## Description
+
+The model was built using the Sentence Transformer library, which provides an easy-to-use interface for working with sentence embeddings, including pre-trained models. 
+
+In addition to **fabelous-mini-embedder**, we also have a proprietary model called **fabelous-embedder-base**. They are trained on a vast dataset covering various programming languages, including:
+
+- Python
+- Java
+- Go
+- C++
+- TypeScript
+
+
+## Example Usage
+
+Here’s how to use the model to generate sentence embeddings:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("fabelous-mini-embedder")
+
+instruction = "This is an example sentence"
+embeddings = model.encode(instruction)
+
+print(embeddings)  # Output: (array of numerical embeddings)
+```
+
+The generated embeddings can be used for tasks like semantic search or classification.
+
+## Future Improvements
+
+We are currently working on the second generation of our model, focusing on:
+
+- **Enhanced Multilingual Support:** Adding support for German in addition to English.
+- **Expanded Dataset:** Increasing the code dataset significantly to improve model performance and accuracy.
+
+We welcome contributions from the community to help us achieve these goals!
+
+## Installation
+
+To install the required libraries, run:
+
+```bash
+pip install sentence-transformers
+```

example.py

@@ -0,0 +1,9 @@
+from sentence_transformers import SentenceTransformer
+
+
+model = SentenceTransformer("fabelous-mini-embedder")
+
+Instruction = "This is a example Sentence"
+
+embeddings = model.encode(Instruction)
+print(embeddings)