Datasets:

Abdullahdadata
/

project-qa-test

id stringlengths 11 11	title stringclasses 1 value	context stringlengths 18 240	question stringlengths 26 102	answers dict
project-001	Content Tagging and Competency Mapping	It is an AI-powered Content Tagging and Competency Mapping system that analyzes learning content and maps it to competencies from the Saudi Skills Taxonomy.	What is the purpose of the project?	{ "text": [ "It is an AI-powered Content Tagging and Competency Mapping system that analyzes learning content and maps it to competencies from the Saudi Skills Taxonomy." ], "answer_start": [ 0 ] }
project-002	Content Tagging and Competency Mapping	It was developed during an SDA bootcamp with WeCloud and BeamData.	In what program was the project developed?	{ "text": [ "It was developed during an SDA bootcamp with WeCloud and BeamData." ], "answer_start": [ 0 ] }
project-003	Content Tagging and Competency Mapping	It produces topic tags, proposed competencies, difficulty, confidence, notes, retrieval candidates, and chunk count.	What outputs does the system produce for learning content?	{ "text": [ "It produces topic tags, proposed competencies, difficulty, confidence, notes, retrieval candidates, and chunk count." ], "answer_start": [ 0 ] }
project-004	Content Tagging and Competency Mapping	Competencies are mapped to the Saudi Skills Taxonomy.	Which taxonomy are competencies mapped to?	{ "text": [ "Competencies are mapped to the Saudi Skills Taxonomy." ], "answer_start": [ 0 ] }
project-005	Content Tagging and Competency Mapping	The Saudi Skills Taxonomy contains 134 competency entries.	How many competency entries are in the Saudi Skills Taxonomy?	{ "text": [ "The Saudi Skills Taxonomy contains 134 competency entries." ], "answer_start": [ 0 ] }
project-006	Content Tagging and Competency Mapping	No. 34 is the number of validated ground-truth competency mappings used in evaluation. The taxonomy contains 134 competency entries.	Is 34 the number of competencies in the taxonomy?	{ "text": [ "No. 34 is the number of validated ground-truth competency mappings used in evaluation. The taxonomy contains 134 competency entries." ], "answer_start": [ 0 ] }
project-007	Content Tagging and Competency Mapping	It is the number of validated competency mappings (ground truth) used in the final evaluation.	What does the number 34 represent in the project?	{ "text": [ "It is the number of validated competency mappings (ground truth) used in the final evaluation." ], "answer_start": [ 0 ] }
project-008	Content Tagging and Competency Mapping	The final evaluation used 12 official evaluation files.	How many official evaluation files were used in the final evaluation?	{ "text": [ "The final evaluation used 12 official evaluation files." ], "answer_start": [ 0 ] }
project-009	Content Tagging and Competency Mapping	The taxonomy is in saudi_skills_taxonomy_v1_final.csv.	Which file contains the Saudi Skills Taxonomy?	{ "text": [ "The taxonomy is in saudi_skills_taxonomy_v1_final.csv." ], "answer_start": [ 0 ] }
project-010	Content Tagging and Competency Mapping	No. They are project, data and evaluation files, not a ready-made QA dataset.	Are the project data files a ready-made QA dataset?	{ "text": [ "No. They are project, data and evaluation files, not a ready-made QA dataset." ], "answer_start": [ 0 ] }
project-011	Content Tagging and Competency Mapping	Qwen2.5-7B-Instruct is used for topic tagging, semantic summarization, and final competency selection and validation.	Which model is used for topic tagging, semantic summarization and final competency selection?	{ "text": [ "Qwen2.5-7B-Instruct is used for topic tagging, semantic summarization, and final competency selection and validation." ], "answer_start": [ 0 ] }
project-012	Content Tagging and Competency Mapping	It uses 4-bit NF4 quantization.	What quantization does Qwen2.5-7B-Instruct use in this project?	{ "text": [ "It uses 4-bit NF4 quantization." ], "answer_start": [ 0 ] }
project-013	Content Tagging and Competency Mapping	intfloat/multilingual-e5-base (E5) performs semantic competency retrieval.	Which model performs semantic competency retrieval?	{ "text": [ "intfloat/multilingual-e5-base (E5) performs semantic competency retrieval." ], "answer_start": [ 0 ] }
project-014	Content Tagging and Competency Mapping	Content is divided into 6,000-character chunks.	What is the chunk size used to divide content?	{ "text": [ "Content is divided into 6,000-character chunks." ], "answer_start": [ 0 ] }
project-015	Content Tagging and Competency Mapping	Chunk overlap is 500 characters.	How much do chunks overlap?	{ "text": [ "Chunk overlap is 500 characters." ], "answer_start": [ 0 ] }
project-016	Content Tagging and Competency Mapping	Lexical matching is also used alongside E5 semantic retrieval.	Besides E5 semantic retrieval, what other matching is used in retrieval?	{ "text": [ "Lexical matching is also used alongside E5 semantic retrieval." ], "answer_start": [ 0 ] }
project-017	Content Tagging and Competency Mapping	The E5 semantic score weight is 0.6 and the lexical score weight is 0.4.	What are the weights of the semantic and lexical scores?	{ "text": [ "The E5 semantic score weight is 0.6 and the lexical score weight is 0.4." ], "answer_start": [ 0 ] }
project-018	Content Tagging and Competency Mapping	The Top-5 candidates are retrieved per chunk.	How many candidates are retrieved per chunk?	{ "text": [ "The Top-5 candidates are retrieved per chunk." ], "answer_start": [ 0 ] }
project-019	Content Tagging and Competency Mapping	Candidates are aggregated across chunks, and a final Top-10 candidate pool is formed.	How is the final candidate pool formed?	{ "text": [ "Candidates are aggregated across chunks, and a final Top-10 candidate pool is formed." ], "answer_start": [ 0 ] }
project-020	Content Tagging and Competency Mapping	Qwen selects and validates the final competencies from the Top-10 pool.	What does Qwen do after the Top-10 candidate pool is formed?	{ "text": [ "Qwen selects and validates the final competencies from the Top-10 pool." ], "answer_start": [ 0 ] }
project-021	Content Tagging and Competency Mapping	At Top-3: Micro Precision 63.89%, Micro Recall 67.65%, and Micro F1 65.71%.	What are the final competency-mapping Micro Precision, Micro Recall and Micro F1 at Top-3?	{ "text": [ "At Top-3: Micro Precision 63.89%, Micro Recall 67.65%, and Micro F1 65.71%." ], "answer_start": [ 0 ] }
project-022	Content Tagging and Competency Mapping	At Top-1: Micro Precision 83.33%, Micro Recall 29.41%, and Micro F1 43.48%.	What are the final competency-mapping results at Top-1 for micro precision, micro recall and micro F1?	{ "text": [ "At Top-1: Micro Precision 83.33%, Micro Recall 29.41%, and Micro F1 43.48%." ], "answer_start": [ 0 ] }
project-023	Content Tagging and Competency Mapping	At Top-5: Micro Precision 45.00%, Micro Recall 79.41%, and Micro F1 57.45%.	What are the final competency-mapping results at Top-5 for micro precision, micro recall and micro F1?	{ "text": [ "At Top-5: Micro Precision 45.00%, Micro Recall 79.41%, and Micro F1 57.45%." ], "answer_start": [ 0 ] }
project-024	Content Tagging and Competency Mapping	There were 23 correct matches at Top-3 and 27 correct matches at Top-5.	How many correct matches were found at Top-3 and at Top-5?	{ "text": [ "There were 23 correct matches at Top-3 and 27 correct matches at Top-5." ], "answer_start": [ 0 ] }
project-025	Content Tagging and Competency Mapping	Macro Recall is 33.33% at Top-1, 70.14% at Top-3, and 81.25% at Top-5.	What is the Macro Recall at Top-1, Top-3 and Top-5?	{ "text": [ "Macro Recall is 33.33% at Top-1, 70.14% at Top-3, and 81.25% at Top-5." ], "answer_start": [ 0 ] }
project-026	Content Tagging and Competency Mapping	Top-3 has the highest Micro F1, at 65.71%.	At which K does the final mapping reach the highest Micro F1?	{ "text": [ "Top-3 has the highest Micro F1, at 65.71%." ], "answer_start": [ 0 ] }
project-027	Content Tagging and Competency Mapping	Hit@1 is 83.33%, Hit@3 is 100.00%, and Hit@5 is 100.00%.	What are the retrieval Hit@1, Hit@3 and Hit@5 values?	{ "text": [ "Hit@1 is 83.33%, Hit@3 is 100.00%, and Hit@5 is 100.00%." ], "answer_start": [ 0 ] }
project-028	Content Tagging and Competency Mapping	Precision@5 is 45.00%, Recall@5 is 81.25%, and F1@5 is 56.85%.	What are the retrieval Precision@5, Recall@5 and F1@5?	{ "text": [ "Precision@5 is 45.00%, Recall@5 is 81.25%, and F1@5 is 56.85%." ], "answer_start": [ 0 ] }
project-029	Content Tagging and Competency Mapping	The MRR is 90.28%.	What is the retrieval MRR?	{ "text": [ "The MRR is 90.28%." ], "answer_start": [ 0 ] }
project-030	Content Tagging and Competency Mapping	No. Hit@3 is a retrieval metric. The final competency-mapping metrics are the Top-K micro precision, micro recall, micro F1, macro recall and correct matches.	Is the Hit@3 value of 100.00% a final competency-mapping metric?	{ "text": [ "No. Hit@3 is a retrieval metric. The final competency-mapping metrics are the Top-K micro precision, micro recall, micro F1, macro recall and correct matches." ], "answer_start": [ 0 ] }
project-031	Content Tagging and Competency Mapping	Micro precision decreases from 83.33% at Top-1 to 63.89% at Top-3 and 45.00% at Top-5, while micro recall increases from 29.41% to 67.65% to 79.41%. Returning more candidates finds more correct competencies but includes more incorrect ones.	How does precision change as K increases in the final mapping results, and what does that suggest?	{ "text": [ "Micro precision decreases from 83.33% at Top-1 to 63.89% at Top-3 and 45.00% at Top-5, while micro recall increases from 29.41% to 67.65% to 79.41%. Returning more candidates finds more correct competencies but includes more incorrect ones." ], "answer_start": [ 0 ] }
project-032	Content Tagging and Competency Mapping	Top-3 has the highest Micro F1 (65.71%), balancing Micro Precision (63.89%) and Micro Recall (67.65%).	Why is Top-3 considered the best balance in the final mapping results?	{ "text": [ "Top-3 has the highest Micro F1 (65.71%), balancing Micro Precision (63.89%) and Micro Recall (67.65%)." ], "answer_start": [ 0 ] }
project-033	Content Tagging and Competency Mapping	It is containerized and deployed using Docker and Kubernetes with GPU model serving, and connected to a working AI Hub interface.	How is the project deployed?	{ "text": [ "It is containerized and deployed using Docker and Kubernetes with GPU model serving, and connected to a working AI Hub interface." ], "answer_start": [ 0 ] }
project-034	Content Tagging and Competency Mapping	User → AI Hub Interface → API / Model Serving → AI Pipeline → Structured Results.	What is the high-level flow of the deployed system?	{ "text": [ "User → AI Hub Interface → API / Model Serving → AI Pipeline → Structured Results." ], "answer_start": [ 0 ] }
project-035	Content Tagging and Competency Mapping	The evaluation set is small (12 files, 34 validated mappings), Top-1 recall is low (29.41%), and precision drops as K increases. The project materials do not provide an explicit limitations list.	What limitations can be observed from the reported evaluation results?	{ "text": [ "The evaluation set is small (12 files, 34 validated mappings), Top-1 recall is low (29.41%), and precision drops as K increases. The project materials do not provide an explicit limitations list." ], "answer_start": [ 0 ] }
project-036	Content Tagging and Competency Mapping	This information is not specified in the project materials. Specific planned future work is not specified.	What future work is planned for the project?	{ "text": [ "This information is not specified in the project materials. Specific planned future work is not specified." ], "answer_start": [ 0 ] }
project-037	Content Tagging and Competency Mapping	This information is not specified in the project materials.	Which GPU type is used for model serving?	{ "text": [ "This information is not specified in the project materials." ], "answer_start": [ 0 ] }