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Model Details

This model is for Obtaining Internship Opportunities Aligned with Student Skills

Model Description

SkillMatch is a machine learning model designed to match students with internship opportunities based on their skills, experiences, and qualifications. The model takes as input the resumes of students and the job descriptions of internship listings and outputs a measure of similarity or relevance between the two.

Sure, here's a description of a model used for Obtaining Internship Opportunities Aligned with Student Skills:

Model Name: SkillMatch

Description: SkillMatch is a machine learning model designed to match students with internship opportunities based on their skills, experiences, and qualifications. The model takes as input the resumes of students and the job descriptions of internship listings and outputs a measure of similarity or relevance between the two.

Model Architecture: SkillMatch utilizes a hybrid approach combining natural language processing (NLP) techniques with machine learning algorithms. The model consists of the following components:

Text Embedding Layer: Both student resumes and internship job descriptions are processed through a text embedding layer to convert the textual data into numerical representations. This layer may use techniques like word embeddings (e.g., Word2Vec, GloVe) or contextual embeddings (e.g., BERT, GPT) to capture the semantic meaning of words and phrases.
Feature Extraction: The numerical representations obtained from the text embedding layer are then fed into a feature extraction module. This module extracts relevant features such as skills, experiences, education, and qualifications from the textual data.
Matching Algorithm: A matching algorithm compares the extracted features from student resumes with those from internship job descriptions to determine the similarity or relevance between the two. Various similarity metrics can be used for this purpose, including cosine similarity, Jaccard similarity, or neural network-based similarity measures.
Ranking and Filtering: The model ranks the internship opportunities based on their similarity scores with each student's profile. Additionally, it may apply filters based on criteria such as location, industry, duration, and required qualifications to narrow down the list of recommended opportunities.

Training Data: SkillMatch is trained on a large dataset consisting of labeled pairs of student resumes and internship job descriptions. The dataset is manually curated and annotated to indicate which internship opportunities are suitable for each student based on their skills and qualifications.

Evaluation Metrics: The performance of SkillMatch is evaluated using metrics such as precision, recall, and F1-score, which measure the model's ability to accurately recommend relevant internship opportunities to students. Additionally, user feedback and satisfaction surveys may be collected to assess the model's effectiveness in practice.

Deployment: Once trained and evaluated, SkillMatch can be deployed as part of a web application or API where students can upload their resumes and receive personalized recommendations for internship opportunities aligned with their skills and interests. The model can be updated periodically with new data to improve its performance over time.

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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