Model: Bo8dady/finetuned2-College-embeddings
Author: Bo8dady
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, roberta, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:4030, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:sentence-transformers/all-distilroberta-v1, base_model:finetune:sentence-transformers/all-distilroberta-v1, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: sentence-transformers/all-distilroberta-v1
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:4030, loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-distilroberta-v1
widget: source_sentence: What is the contact email for Dr. Amr Ashraf Mohamed Amin?
sentences: Topic: Second Level Courses (Mainstream)
Summary: Outlines the course list for the third and fourth semesters, including course codes, titles, credit hours, and prerequisites.
Chunk: "Second Level Courses (Mainstream) 
Third Semester
 • HUM113: Report Writing (2 Credit Hours) 
• CIS250: Object-Oriented Programming (3 Credit Hours) – Prerequisite: CIS150 
(Structured Programming) 
• BSC221: Discrete Mathematics (3 Credit Hours) 
• CIS260: Logic Design (3 Credit Hours) – Prerequisite: BSC121 (Physics I) 
• CIS280: Database Management Systems (3 Credit Hours) – Prerequisite: CIS150 
(Structured Programming) 
• CIS240: Statistical Analysis (3 Credit Hours) – Prerequisite: BSC123 (Probability & 
Statistics) 
• Total Credit Hours: 17 
Fourth Semester 
• CIS220: Computer Organization & Architecture (3 Credit Hours) – Prerequisite: CIS260 
(Logic Design) 
• CIS270: Data Structure (3 Credit Hours) – Prerequisite: CIS250 (Object-Oriented 
Programming) 
• BSC225: Linear Algebra (3 Credit Hours) 
• CIS230: Operations Research (3 Credit Hours) 
• CIS243: Artificial Intelligence (3 Credit Hours) – Prerequisite: CIS150 (Structured 
Programming) 
• Total Credit Hours: 15", The final exam for the Structured programming course, offered by the general department, from 2022, is available at the following link: [https://drive.google.com/file/d/1Bpqoa78DcFNC8335i7vucV0nBN-J01v9/view?usp=sharing, Dr. Amr Ashraf Mohamed Amin is part of the Unknown department and can be reached at amr.amin.stdsw@cis.asu.edu.eg., source_sentence: What systems have been developed for quickly locating missing children?
sentences: The final exam for Digital Signal Processing course, offered by the computer science department, from 2024, is available at the following link: [https://drive.google.com/file/d/1RO0aPoom-TA-qgsopwR9krszD_pQIzfJ/view?usp=sharing, **Lost People Finder**

### **Abstract**

**Missing Persons Statistics**
Recently, there has been a clear increase in the population. As stated in a 2005 report, published by the US Department of Justice, over 340,500 of children's population go missing, from their parents, for at least an hour. Not only was this issue minor in between children, but also it has been evident that the elderly and people with special needs seem missing whenever their guardians get distracted.

**Lost People Finder Application**
Through the Lost People Finder application, we can search for missing people quickly and efficiently by entering the missing person's picture in the application, and the application searches for him immediately., The final exam for the English 1course, offered by the general department, from 2022, is available at the following link: [https://drive.google.com/file/d/1IbqLbHuyZoDyhsL1BERpI2P0iLFZmgt8/view]., source_sentence: What are the conditions for the College Council granting a final chance?
sentences: Dr. Zeina Rayan is part of the Unknown department and can be reached at zeinarayan@cis.asu.edu.eg., Topic: Academic Warning and Dismissal
Summary: Students receive academic warnings for low GPAs and may be dismissed if the GPA remains low for six semesters or if graduation requirements aren't met within double the study years. Students can re-study courses to improve their average, with certain conditions and grade limits.
Chunk: "Academic warning - dismissal from study - mechanisms of raising the cumulative average
1. The student is given an academic warning if he obtains a cumulative average less than "2" for any semester that he must raise his cumulative average to at least 2.00.
2. A student who is academically probated is dismissed from the study if the GPA drops below 2.00 is repeated during six main semesters.
3. If the student does not meet the graduation requirements within the maximum period of study, which is double the years of study according to the law, he will be dismissed.
4. The College Council may consider the possibility of granting the student exposed to dismissal as a result of his inability to raise his cumulative average to At least one and final chance of two semesters to raise his/her GPA to 2.00 and meet graduation requirements if he/she has successfully completed at least 80% of the credit hours required for graduation.
5. The student may re-study the courses in which he has previously passed in order to improve the cumulative average, and the repetition is a study and an exam, and the grade he obtained the last time he studied the course is calculated for him. A maximum of (5) courses unless the improvement is for the purpose of raising the academic warning or achieving the graduation requirements, and in all cases, both grades are mentioned in his academic record.
6. For the student to re-study a course in which he has previously obtained a grade of (F), the grade he obtained in the repetition is calculated with a maximum of (B), and for calculating the cumulative average, the last grade is calculated for him only, provided that both grades are mentioned in the student's academic record.", **Abstract**

**Introduction to Renewable Energy**
Renewable energy is gaining great importance nowadays. Solar energy is one of the most popular renewable energy sources as it is carbon dioxide free, has low operating costs, and its exploitation helps improve public health.

**Project Overview**
This project deals with the introduction of an embedded automatic solar energy tracking system that can be monitored remotely. The main objective of the system is to exploit the maximum amount of sunlight and convert it into electricity so that it can be used easily and efficiently. This can be done by rendering and aligning a model that drives the solar panels to be perpendicular to and track the sun's rays so that more energy is generated.

**Advantages of the Tracker System**
The main advantage of this tracker is that the various readings received from the sensors can be tracked remotely with a decentralized technological system that allows analysis of results, detection of faults and making tracking decisions. The advantage of this system is to provide access to a permanent and contamination-free power supply source. When connected to large battery banks, they can independently fill the needs of local areas., source_sentence: How can I contact Dr. Doaa Mahmoud?
sentences: Dr. Hanan Hindy is part of the CS department and can be reached at hanan.hindy@cis.asu.edu.eg., The final exam for Database Management System course, offered by the general department, from 2019, is available at the following link: [https://drive.google.com/file/d/1OOIPr48WI8Cm3TVzPdel2Dh3SZUQTVxA/view, Dr. Doaa Mahmoud is part of the Unknown department and can be reached at Doaa.Mahmoud@cis.asu.edu.eg., source_sentence: Where can I find Abdel Badi Salem's email address?
sentences: # **Abstract**

## **Introduction**
One of the main issues we are aiming to help in society are those of the disabled. Disabilities do not have a single type or manner in which it attacks the body but comes in a very wide range. At the present time, the amount of disabled people is **increasing annually**, so we aim to make a standard wheelchair to aid the mobility of disabled people who cannot walk; by designing two mechanisms, one uses eye-movement guidance and the other uses EEG Signals, which goes through pre-processing stage to extract more information from the data. This' done by segmentation using a window of size 200 (Sampling frequency), then features extraction. That takes us to classification, the highest accuracy we got is on subject [E] for motor imaginary dataset on Classical paradigm, Multi Level Perceptron classifier (with accuracy of 60.5%), The result of this classification's used as a command to move the wheelchair after that., # **Abstract**

## **Sports Analytics Overview**
Sports analytics has been successfully applied in sports like football and basketball. However, its application in soccer has been limited. Research in soccer analytics with Machine Learning techniques is limited and is mostly employed only for predictions. There is a need to find out if the application of Machine Learning can bring better and more insightful results in soccer analytics. In this thesis, we perform descriptive as well as predictive analysis of soccer matches and player performances.

## **Football Rating Analysis**
In football, it is popular to rely on ratings by experts to assess a player's performance. However, the experts do not unravel the criteria they use for their rating. We attempt to identify the most important attributes of player's performance which determine the expert ratings. In this way we find the latent knowledge which the experts use to assign ratings to players. We performed a series of classifications with three different pruning strategies and an array of Machine Learning algorithms. The best results for predicting ratings using performance metrics had mean absolute error of 0.17. We obtained a list of most important performance metrics for each of the playing positions which approximates the attributes considered by the experts for assigning ratings. Then we find the most influential performance metrics of the players for determining the match outcome and we examine the extent to which the outcome is characterized by the performance attributes of the players. We found 34 performance attributes, Dr. Abdel Badi Salem is part of the CS department and can be reached at absalem@cis.asu.edu.eg.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100
model-index: name: SentenceTransformer based on sentence-transformers/all-distilroberta-v1
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: ai college validation
type: ai-college-validation
metrics: type: cosine_accuracy@1
value: 0.18810557968593383
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4186435015035082
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5676578683595055
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8463080521216171
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.18810557968593383
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.13954783383450275
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.1135315736719011
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08463080521216171
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.18810557968593383
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4186435015035082
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5676578683595055
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8463080521216171
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.47259073953229414
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3588172667440963
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3678298256041653
name: Cosine Map@100
verified: no, type: cosine_accuracy@1
value: 0.18843969261610424
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.4173070497828266
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5669896424991647
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8456398262612763
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.18843969261610424
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.13910234992760886
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.11339792849983296
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08456398262612765
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.18843969261610424
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.4173070497828266
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5669896424991647
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8456398262612763
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.47223133269915585
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.3585802056650706
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.3676667485080777
name: Cosine Map@100
verified: no, type: cosine_accuracy@1
value: 0.10194511983327545
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.3183397012851685
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.5359499826328586
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.8726988537686696
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.10194511983327545
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.10611323376172282
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.10718999652657174
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.08726988537686697
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.10194511983327545
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.3183397012851685
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.5359499826328586
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.8726988537686696
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.4252051320311702
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.28928936689878015
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.29650939746113625
name: Cosine Map@100
verified: no
Likes: 0 | All-time downloads: 73
Created: 2025-04-13 16:18:24