datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
peft-unit-test-generation-experiments
PEFT Unit Test Generation Experiments
Dataset description
The PEFT Unit Test Generation Experiments dataset contains metadata and details about a set of trained models used for generating unit tests with parameter-efficient fine-tuning (PEFT) methods. This dataset includes models from multiple namespaces and various sizes, trained with different tuning methods to provide a comprehensive resource for unit test generation research.
Dataset Structure
Data… See the full description on the dataset page: https://huggingface.co/datasets/andstor/peft-unit-test-generation-experiments.peft-unit-test-generation-experiments
PEFT Unit Test Generation Experiments
Dataset description
The PEFT Unit Test Generation Experiments dataset contains metadata and details about a set of trained models used for generating unit tests with parameter-efficient fine-tuning (PEFT) methods. This dataset includes models from multiple namespaces and various sizes, trained with different tuning methods to provide a comprehensive resource for unit test generation research.
Dataset Structure
Data… See the full description on the dataset page: https://huggingface.co/datasets/fals3/peft-unit-test-generation-experiments.llama-3b-gold-15M-student-generations_PRESAMPLING_1024_TESTunit_test_generation⚠️ Note: The dataset symprompt_supp.jsonl is not created by us. We only supplemented this dataset with additional branch-level metadata (e.g., has_branch, total_branches) to enable coverage testing.
This helps users keep their workflows clean when determining whether branches exist, simplifying branch coverage calculation.
It originates from the paper:
Code-Aware Prompting: A Study of Coverage Guided Test Generation in Regression Setting using LLM
— Gabriel Ryan, Siddhartha Jain, Mingyue… See the full description on the dataset page: https://huggingface.co/datasets/Code-TREAT/unit_test_generation.llama-3b-gold-15M-student-generations_SNIS_1024_TEST_N150.00Kimage-generation-dataset-testllama-3b-gold-15M-student-generations_RS_1024_TEST_N150.00Kgitbug-java-unit-test-generationQuestion_generation_test-088d3464-f299-492c-9793-e2ad385a31cfllama-3b-gold-15M-student-generations_SNIS_2048_TEST_baseN10.00K_N30.00K_T2.0llama-3b-gold-15M-student-generations_PRESAMPLING_2048_TEST_baseN10.00KQuestion_generation_test_01eval_metrics_5000_generations_test_text2struc_text_code_cif_1116eval_metrics_5000_generations_test_text2struc_text_code_1116eval_metrics_5000_generations_test_text2struc_text_code_cif_1116Question_generation_testopen-australian-legal-qa-test-analysis-generation-single-shotelix_generations_autolabel_gpt4o_pref_testshaer-sft-test-generations-k5-meter-count-300-splitelix_generations_autolabel_gemini_pref_testllama-3b-gold-15M-student-generations_PRESAMPLING_2048_TESTshaer-sft-test-generations-k5
Shaer SFT Test Generations K=5
This dataset contains generated outputs from the final Shaer SFT adapter on the held-out test split.
Source dataset: Shaer-AI/ashaar-with-enhanced-descriptions-baseform-final-sft-lte20-min500-splits
Source split: test
Source rows: 3,481
Samples per source row: 5
Total generated rows: 17,405
Base model: Navid-AI/Yehia-7B-preview
Adapter repo: Shaer-AI/Shaer-adapters
Adapter subfolder: adapters/fresh_sft/train/best
Generation backend: vLLM with LoRA… See the full description on the dataset page: https://huggingface.co/datasets/Shaer-AI-2/shaer-sft-test-generations-k5.results_joke_generation_of_mistral_bm_jo_multiclass_testelix_generations_gpt4o_pref_testeval_metrics_5000_generations_test_text2struc_text_code_1116shaer-sft-test-generations-k5-meter-count
Shaer SFT Test Generations K=5 Meter/Count Scores
This dataset enriches Shaer-AI/shaer-sft-test-generations-k5 with automatic meter and requested-line-count evaluation.
Rows: 17,405
Metric pass: 4BiLSTM meter classifier plus line-count adherence
Core metric columns: meter, count_adherence, parsed_num_lines, requested_num_lines, meter_eval_status, count_eval_status
Overall meter mean: 0.6520384872952881
Overall count-adherence mean: 0.9783539094873928
The automatic meter score… See the full description on the dataset page: https://huggingface.co/datasets/Shaer-AI/shaer-sft-test-generations-k5-meter-count.shaer-sft-test-generations-k5-meter-count-selected-evaluated