Update README.md

README.md

@@ -15,18 +15,6 @@ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-
 ## Purpose of the Project
 This project aims to help job seekers by matching their CVs to job descriptions from major companies using semantic similarity. By analyzing the text content of the CV, the system identifies and presents the top three job descriptions that best match the provided CV. Additionally, it provides translations for CVs that are not in English, as well as the corresponding job descriptions.
 
-## Main Files and Their Functions
-- **project.ipynb**: This is the primary script that runs the project. It includes the following functionalities:
-  - Contains predefined job descriptions from major companies like Google, Amazon, Microsoft, etc.
-  - Detects the language of the input CV.
-  - Translates the CV to English if necessary using Hugging Face’s translation pipeline.
-  - Compares the CV with the job descriptions using semantic similarity through SentenceTransformer.
-  - Outputs the top 3 matching job descriptions and their similarity scores in a bar chart.
-  - Translates job descriptions to the CV’s original language if the input CV is not in English.
-
-- **Pipeline.ipynb**: This Python notebook demonstrates how the Hugging Face text-to-text pipeline works independently, without the Gradio interface. It walks through using Hugging Face’s translation and semantic similarity tools with hardcoded inputs.
-
-- **Gradio.ipynb**: This notebook shows how Gradio components work using hardcoded data. It demonstrates the basic setup of a Gradio interface with similar functionalities, but without relying on external inputs or complex workflows.
 ## Models
 ### Semantic Similarity Model
 The project uses the **SentenceTransformer** model, specifically the `sentence-transformers/all-MiniLM-L6-v2`, to compute semantic similarity between the CV and job descriptions. The model works by converting both the CV and job descriptions into embeddings (vector representations of the text). It then calculates the cosine similarity between these embeddings to determine how closely the texts match in meaning. The higher the cosine similarity score, the more similar the texts are. This allows the system to rank the job descriptions and find the top 3 that best match the content of the provided CV.