Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,771 @@
+---
+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)\nSummary: Outlines the course list for\
+    \ the third and fourth semesters, including course codes, titles, credit hours,\
+    \ and prerequisites.\nChunk: \"Second Level Courses (Mainstream) \nThird Semester\n\
+    \ • HUM113: Report Writing (2 Credit Hours) \n• CIS250: Object-Oriented Programming\
+    \ (3 Credit Hours) – Prerequisite: CIS150 \n(Structured Programming) \n• BSC221:\
+    \ Discrete Mathematics (3 Credit Hours) \n• CIS260: Logic Design (3 Credit Hours)\
+    \ – Prerequisite: BSC121 (Physics I) \n• CIS280: Database Management Systems (3\
+    \ Credit Hours) – Prerequisite: CIS150 \n(Structured Programming) \n• CIS240:\
+    \ Statistical Analysis (3 Credit Hours) – Prerequisite: BSC123 (Probability &\
+    \ \nStatistics) \n• Total Credit Hours: 17 \nFourth Semester \n• CIS220: Computer\
+    \ Organization & Architecture (3 Credit Hours) – Prerequisite: CIS260 \n(Logic\
+    \ Design) \n• CIS270: Data Structure (3 Credit Hours) – Prerequisite: CIS250 (Object-Oriented\
+    \ \nProgramming) \n• BSC225: Linear Algebra (3 Credit Hours) \n• CIS230: Operations\
+    \ Research (3 Credit Hours) \n• CIS243: Artificial Intelligence (3 Credit Hours)\
+    \ – Prerequisite: CIS150 (Structured \nProgramming) \n• 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 [email protected].
+- 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 [email protected].
+  - '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 [email protected].
+  - '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 [email protected].
+- 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 [email protected].
+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
+    - type: cosine_accuracy@3
+      value: 0.4186435015035082
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5676578683595055
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8463080521216171
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18810557968593383
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13954783383450275
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1135315736719011
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08463080521216171
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18810557968593383
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4186435015035082
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5676578683595055
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8463080521216171
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.47259073953229414
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3588172667440963
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.3678298256041653
+      name: Cosine Map@100
+    - type: cosine_accuracy@1
+      value: 0.18843969261610424
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.4173070497828266
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5669896424991647
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8456398262612763
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.18843969261610424
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.13910234992760886
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.11339792849983296
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08456398262612765
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.18843969261610424
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.4173070497828266
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5669896424991647
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8456398262612763
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.47223133269915585
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.3585802056650706
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.3676667485080777
+      name: Cosine Map@100
+    - type: cosine_accuracy@1
+      value: 0.10194511983327545
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.3183397012851685
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.5359499826328586
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8726988537686696
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.10194511983327545
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.10611323376172282
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.10718999652657174
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08726988537686697
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.10194511983327545
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.3183397012851685
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.5359499826328586
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8726988537686696
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.4252051320311702
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.28928936689878015
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.29650939746113625
+      name: Cosine Map@100
+---
+
+# SentenceTransformer based on sentence-transformers/all-distilroberta-v1
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1) <!-- at revision 842eaed40bee4d61673a81c92d5689a8fed7a09f -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("Bo8dady/finetuned2-College-embeddings")
+# Run inference
+sentences = [
+    "Where can I find Abdel Badi Salem's email address?",
+    'Dr. Abdel Badi Salem is part of the CS department and can be reached at [email protected].',
+    "# **Abstract**\n\n## **Sports Analytics Overview**\nSports 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.\n\n## **Football Rating Analysis**\nIn 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",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Dataset: `ai-college-validation`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1881     |
+| cosine_accuracy@3   | 0.4186     |
+| cosine_accuracy@5   | 0.5677     |
+| cosine_accuracy@10  | 0.8463     |
+| cosine_precision@1  | 0.1881     |
+| cosine_precision@3  | 0.1395     |
+| cosine_precision@5  | 0.1135     |
+| cosine_precision@10 | 0.0846     |
+| cosine_recall@1     | 0.1881     |
+| cosine_recall@3     | 0.4186     |
+| cosine_recall@5     | 0.5677     |
+| cosine_recall@10    | 0.8463     |
+| **cosine_ndcg@10**  | **0.4726** |
+| cosine_mrr@10       | 0.3588     |
+| cosine_map@100      | 0.3678     |
+
+#### Information Retrieval
+
+* Dataset: `ai-college-validation`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1884     |
+| cosine_accuracy@3   | 0.4173     |
+| cosine_accuracy@5   | 0.567      |
+| cosine_accuracy@10  | 0.8456     |
+| cosine_precision@1  | 0.1884     |
+| cosine_precision@3  | 0.1391     |
+| cosine_precision@5  | 0.1134     |
+| cosine_precision@10 | 0.0846     |
+| cosine_recall@1     | 0.1884     |
+| cosine_recall@3     | 0.4173     |
+| cosine_recall@5     | 0.567      |
+| cosine_recall@10    | 0.8456     |
+| **cosine_ndcg@10**  | **0.4722** |
+| cosine_mrr@10       | 0.3586     |
+| cosine_map@100      | 0.3677     |
+
+#### Information Retrieval
+
+* Dataset: `ai-college-validation`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.1019     |
+| cosine_accuracy@3   | 0.3183     |
+| cosine_accuracy@5   | 0.5359     |
+| cosine_accuracy@10  | 0.8727     |
+| cosine_precision@1  | 0.1019     |
+| cosine_precision@3  | 0.1061     |
+| cosine_precision@5  | 0.1072     |
+| cosine_precision@10 | 0.0873     |
+| cosine_recall@1     | 0.1019     |
+| cosine_recall@3     | 0.3183     |
+| cosine_recall@5     | 0.5359     |
+| cosine_recall@10    | 0.8727     |
+| **cosine_ndcg@10**  | **0.4252** |
+| cosine_mrr@10       | 0.2893     |
+| cosine_map@100      | 0.2965     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+* Size: 4,030 training samples
+* Columns: <code>Question</code> and <code>chunk</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | Question                                                                          | chunk                                                                                |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 8 tokens</li><li>mean: 15.99 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 133.41 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | Question                                                                            | chunk                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+  |:------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Could you share the link to the 2018 Distributed Computing final exam?</code> | <code>The final exam for Distributed Computing course, offered by the computer science department, from 2018, is available at the following link: [https://drive.google.com/file/d/1YSzMeYStlFEztP0TloIcBqnfPr60o4ez/view?usp=sharing</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+  | <code>What databases exist for footstep recognition research?</code>                | <code>**Abstract**<br><br>**Documentation Overview**<br>This documentation reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the time domain of signals collected from an array of piezoelectric sensors.<br><br>**Database Information**<br>Results are related to the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons, which is well beyond previous related databases.<br><br>**Feature Extraction**<br>Three feature approaches have been extracted, the popular ground reaction force (GRF), the spatial average and the upper and lower contours of the pressure signals.<br><br>**Experimental Results**<br>Experimental work is based on a verification mode with a holistic approach based on PCA and SVM, achieving results in the range of 5 to 15% equal error rate(EER) depending on the experimental conditions of quantity of data used in the reference models.</code> |
+  | <code>Is there a maximum duration of study specified in the text?</code>            | <code>Topic: Duration of Study<br>Summary: A bachelor's degree at the Faculty of Computers and Information requires at least four years of study, contingent on fulfilling degree requirements.<br>Chunk: "Duration of study<br>• The duration of study at the Faculty of Computers and Information to obtain a bachelor's degree is not less than 4 years, provided that the requirements for obtaining the scientific degree are completed."</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### Unnamed Dataset
+
+* Size: 575 evaluation samples
+* Columns: <code>Question</code> and <code>chunk</code>
+* Approximate statistics based on the first 575 samples:
+  |         | Question                                                                          | chunk                                                                                |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 9 tokens</li><li>mean: 15.97 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 134.83 tokens</li><li>max: 484 tokens</li></ul> |
+* Samples:
+  | Question                                                                                                             | chunk                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:---------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Are there projects that use machine learning for automatic brain tumor identification?</code>                  | <code># **Abstract**<br><br>## **Brain and Tumor Description**<br>A human brain is center of the nervous system; it is a collection of white mass of cells. A tumor of brain is collection of uncontrolled increasing of these cells abnormally found in different part of the brain namely Glial cells, neurons, lymphatic tissues, blood vessels, pituitary glands and other part of brain which lead to the cancer.<br><br>## **Detection and Identification**<br>Manually it is not so easily possible to detect and identify the tumor. Programming division method by MRI is way to detect and identify the tumor. In order to give precise output a strong segmentation method is needed. Brain tumor identification is really challenging task in early stages of life. But now it became advanced with various machine learning and deep learning algorithms. Now a day's issue of brain tumor automatic identification is of great interest. In Order to detect the brain tumor of a patient we consider the data of patients like MRI images of a pat...</code> |
+  | <code>Are there studies that propose solutions to the challenges of plant pest detection using deep learning?</code> | <code>**Abstract**<br><br>**Introduction**<br>Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. through deep learning methodologies, plant diseases can be detected and diagnosed.<br><br>**Study Discussion**<br>On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.<br><br>5 | Page</code>                                                                                                                                                                                          |
+  | <code>Is there a link available for the 2025 Calc 1 course exam?</code>                                              | <code>The final exam for the calculus1 course, offered by the general department, from 2025, is available at the following link: [https://drive.google.com/file/d/1g8iiGUo4HCUzNNWBJJrW1QZAsz-RYehw/view?usp=sharing].</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `learning_rate`: 1e-06
+- `warmup_ratio`: 0.2
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 1e-06
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 3
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.2
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `tp_size`: 0
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | ai-college-validation_cosine_ndcg@10 |
+|:------:|:----:|:-------------:|:---------------:|:------------------------------------:|
+| -1     | -1   | -             | -               | 0.4208                               |
+| 0.3968 | 100  | 0.1371        | 0.0785          | 0.4483                               |
+| 0.7937 | 200  | 0.0575        | 0.0357          | 0.4600                               |
+| 1.1905 | 300  | 0.0346        | 0.0286          | 0.4640                               |
+| 1.5873 | 400  | 0.0313        | 0.0264          | 0.4698                               |
+| 1.9841 | 500  | 0.0189        | 0.0256          | 0.4716                               |
+| 2.3810 | 600  | 0.021         | 0.0249          | 0.4703                               |
+| 2.7778 | 700  | 0.0264        | 0.0247          | 0.4726                               |
+| -1     | -1   | -             | -               | 0.4252                               |
+
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.51.1
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.5.0
+- Tokenizers: 0.21.0
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,27 @@
+{
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89d250b883c477b81eda6cc4637d5773dd1a8d251fe9c0ef3098c342d64eefb4
+size 328485128

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}