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      "norm_label": "download_all_huggingface_datasets.py"
    },
    {
      "label": "Downloads all publicly available HuggingFace datasets automatically.\nDatasets re",
      "file_type": "rationale",
      "source_file": "scripts/download_all_huggingface_datasets.py",
      "id": "scripts_download_all_huggingface_datasets_py_docstring",
      "community": 22,
      "norm_label": "downloads all publicly available huggingface datasets automatically.\ndatasets re"
    },
    {
      "label": "main()",
      "file_type": "code",
      "source_file": "scripts/download_all_huggingface_datasets.py",
      "source_location": "line 27",
      "id": "scripts_download_all_huggingface_datasets_py_main",
      "community": 22,
      "norm_label": "main()"
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    {
      "label": "evaluate.py",
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      "source_file": "scripts/evaluate.py",
      "id": "scripts_evaluate_py",
      "community": 1,
      "norm_label": "evaluate.py"
    },
    {
      "label": "Evaluation script.\nRuns all evaluation metrics on the test set.\nRun: python scri",
      "file_type": "rationale",
      "source_file": "scripts/evaluate.py",
      "id": "scripts_evaluate_py_docstring",
      "community": 1,
      "norm_label": "evaluation script.\nruns all evaluation metrics on the test set.\nrun: python scri"
    },
    {
      "label": "evaluate()",
      "file_type": "code",
      "source_file": "scripts/evaluate.py",
      "source_location": "line 30",
      "id": "scripts_evaluate_py_evaluate",
      "community": 1,
      "norm_label": "evaluate()"
    },
    {
      "label": "Run evaluation on the specified data split.",
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      "source_file": "scripts/evaluate.py",
      "source_location": "line 30",
      "id": "scripts_evaluate_py_evaluate_doc",
      "community": 1,
      "norm_label": "run evaluation on the specified data split."
    },
    {
      "label": "preprocess_data.py",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "id": "scripts_preprocess_data_py",
      "community": 15,
      "norm_label": "preprocess_data.py"
    },
    {
      "label": "Converts all raw dataset formats into unified JSONL training format.\nOutput sche",
      "file_type": "rationale",
      "source_file": "scripts/preprocess_data.py",
      "id": "scripts_preprocess_data_py_docstring",
      "community": 15,
      "norm_label": "converts all raw dataset formats into unified jsonl training format.\noutput sche"
    },
    {
      "label": "apply_bea19_edits()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 19",
      "id": "scripts_preprocess_data_py_apply_bea19_edits",
      "community": 15,
      "norm_label": "apply_bea19_edits()"
    },
    {
      "label": "Apply BEA-2019 character-level edits to produce corrected text.\n\nedits_block for",
      "file_type": "rationale",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 19",
      "id": "scripts_preprocess_data_py_apply_bea19_edits_doc",
      "community": 15,
      "norm_label": "apply bea-2019 character-level edits to produce corrected text.\n\nedits_block for"
    },
    {
      "label": "process_bea19_json()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 51",
      "id": "scripts_preprocess_data_py_process_bea19_json",
      "community": 15,
      "norm_label": "process_bea19_json()"
    },
    {
      "label": "Process a BEA-2019 format JSON file (FCE or W&I+LOCNESS).\nEach line is a JSON ob",
      "file_type": "rationale",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 51",
      "id": "scripts_preprocess_data_py_process_bea19_json_doc",
      "community": 15,
      "norm_label": "process a bea-2019 format json file (fce or w&i+locness).\neach line is a json ob"
    },
    {
      "label": "process_fce()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 79",
      "id": "scripts_preprocess_data_py_process_fce",
      "community": 15,
      "norm_label": "process_fce()"
    },
    {
      "label": "Process all FCE JSON files.",
      "file_type": "rationale",
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      "source_location": "line 79",
      "id": "scripts_preprocess_data_py_process_fce_doc",
      "community": 15,
      "norm_label": "process all fce json files."
    },
    {
      "label": "process_wi_locness()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 93",
      "id": "scripts_preprocess_data_py_process_wi_locness",
      "community": 15,
      "norm_label": "process_wi_locness()"
    },
    {
      "label": "Process all W&I+LOCNESS JSON files.",
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      "source_location": "line 93",
      "id": "scripts_preprocess_data_py_process_wi_locness_doc",
      "community": 15,
      "norm_label": "process all w&i+locness json files."
    },
    {
      "label": "process_jfleg()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 107",
      "id": "scripts_preprocess_data_py_process_jfleg",
      "community": 15,
      "norm_label": "process_jfleg()"
    },
    {
      "label": "JFLEG: .src files (original) and .ref0..ref3 (4 human corrections).\nEach referen",
      "file_type": "rationale",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 107",
      "id": "scripts_preprocess_data_py_process_jfleg_doc",
      "community": 15,
      "norm_label": "jfleg: .src files (original) and .ref0..ref3 (4 human corrections).\neach referen"
    },
    {
      "label": "create_splits()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 137",
      "id": "scripts_preprocess_data_py_create_splits",
      "community": 15,
      "norm_label": "create_splits()"
    },
    {
      "label": "Split train.jsonl into train and val sets.",
      "file_type": "rationale",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 137",
      "id": "scripts_preprocess_data_py_create_splits_doc",
      "community": 15,
      "norm_label": "split train.jsonl into train and val sets."
    },
    {
      "label": "main()",
      "file_type": "code",
      "source_file": "scripts/preprocess_data.py",
      "source_location": "line 167",
      "id": "scripts_preprocess_data_py_main",
      "community": 15,
      "norm_label": "main()"
    },
    {
      "label": "pretrain_human_pattern_classifier.py",
      "file_type": "code",
      "source_file": "scripts/pretrain_human_pattern_classifier.py",
      "id": "scripts_pretrain_human_pattern_classifier_py",
      "community": 7,
      "norm_label": "pretrain_human_pattern_classifier.py"
    },
    {
      "label": "Pre-trains the HumanPatternClassifier on both Kaggle datasets.\nRun this BEFORE t",
      "file_type": "rationale",
      "source_file": "scripts/pretrain_human_pattern_classifier.py",
      "id": "scripts_pretrain_human_pattern_classifier_py_docstring",
      "community": 7,
      "norm_label": "pre-trains the humanpatternclassifier on both kaggle datasets.\nrun this before t"
    },
    {
      "label": "train_classifier()",
      "file_type": "code",
      "source_file": "scripts/pretrain_human_pattern_classifier.py",
      "source_location": "line 32",
      "id": "scripts_pretrain_human_pattern_classifier_py_train_classifier",
      "community": 7,
      "norm_label": "train_classifier()"
    },
    {
      "label": "Pre-train the human pattern classifier on Kaggle datasets.",
      "file_type": "rationale",
      "source_file": "scripts/pretrain_human_pattern_classifier.py",
      "source_location": "line 32",
      "id": "scripts_pretrain_human_pattern_classifier_py_train_classifier_doc",
      "community": 7,
      "norm_label": "pre-train the human pattern classifier on kaggle datasets."
    },
    {
      "label": "run_inference.py",
      "file_type": "code",
      "source_file": "scripts/run_inference.py",
      "id": "scripts_run_inference_py",
      "community": 9,
      "norm_label": "run_inference.py"
    },
    {
      "label": "Interactive inference script.\nRun: python scripts/run_inference.py --config conf",
      "file_type": "rationale",
      "source_file": "scripts/run_inference.py",
      "id": "scripts_run_inference_py_docstring",
      "community": 9,
      "norm_label": "interactive inference script.\nrun: python scripts/run_inference.py --config conf"
    },
    {
      "label": "run_inference()",
      "file_type": "code",
      "source_file": "scripts/run_inference.py",
      "source_location": "line 21",
      "id": "scripts_run_inference_py_run_inference",
      "community": 9,
      "norm_label": "run_inference()"
    },
    {
      "label": "Run inference on text input.",
      "file_type": "rationale",
      "source_file": "scripts/run_inference.py",
      "source_location": "line 21",
      "id": "scripts_run_inference_py_run_inference_doc",
      "community": 9,
      "norm_label": "run inference on text input."
    },
    {
      "label": "train.py",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "id": "scripts_train_py",
      "community": 4,
      "norm_label": "train.py"
    },
    {
      "label": "Full training entry point.\nRun: python scripts/train.py --config configs/trainin",
      "file_type": "rationale",
      "source_file": "scripts/train.py",
      "id": "scripts_train_py_docstring",
      "community": 4,
      "norm_label": "full training entry point.\nrun: python scripts/train.py --config configs/trainin"
    },
    {
      "label": "_setup_device()",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 31",
      "id": "scripts_train_py__setup_device",
      "community": 4,
      "norm_label": "_setup_device()"
    },
    {
      "label": "Detect GPU and configure hybrid VRAM management.\n\nReturns (device, gpu_info) whe",
      "file_type": "rationale",
      "source_file": "scripts/train.py",
      "source_location": "line 31",
      "id": "scripts_train_py__setup_device_doc",
      "community": 4,
      "norm_label": "detect gpu and configure hybrid vram management.\n\nreturns (device, gpu_info) whe"
    },
    {
      "label": "_auto_batch_size()",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 82",
      "id": "scripts_train_py__auto_batch_size",
      "community": 4,
      "norm_label": "_auto_batch_size()"
    },
    {
      "label": "Pick optimal batch size based on model size and available resources.",
      "file_type": "rationale",
      "source_file": "scripts/train.py",
      "source_location": "line 82",
      "id": "scripts_train_py__auto_batch_size_doc",
      "community": 4,
      "norm_label": "pick optimal batch size based on model size and available resources."
    },
    {
      "label": "train()",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 126",
      "id": "scripts_train_py_train",
      "community": 4,
      "norm_label": "train()"
    },
    {
      "label": "Launch the full training pipeline.",
      "file_type": "rationale",
      "source_file": "scripts/train.py",
      "source_location": "line 126",
      "id": "scripts_train_py_train_doc",
      "community": 4,
      "norm_label": "launch the full training pipeline."
    },
    {
      "label": "CEOnlyLoss",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 221",
      "id": "scripts_train_py_CEOnlyLoss",
      "community": 4,
      "norm_label": "ceonlyloss"
    },
    {
      "label": "Cross-entropy only loss \u2014 the only loss that provides gradient signal.",
      "file_type": "rationale",
      "source_file": "scripts/train.py",
      "source_location": "line 221",
      "id": "scripts_train_py_CEOnlyLoss_doc",
      "community": 4,
      "norm_label": "cross-entropy only loss \u2014 the only loss that provides gradient signal."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 223",
      "id": "scripts_train_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "forward()",
      "file_type": "code",
      "source_file": "scripts/train.py",
      "source_location": "line 227",
      "id": "scripts_train_py_forward",
      "community": 7,
      "norm_label": "forward()"
    },
    {
      "label": "main.py",
      "file_type": "code",
      "source_file": "src/api/main.py",
      "id": "src_api_main_py",
      "community": 9,
      "norm_label": "main.py"
    },
    {
      "label": "FastAPI server for the Dyslexia Academic Writing Corrector API.\nProvides RESTful",
      "file_type": "rationale",
      "source_file": "src/api/main.py",
      "id": "src_api_main_py_docstring",
      "community": 9,
      "norm_label": "fastapi server for the dyslexia academic writing corrector api.\nprovides restful"
    },
    {
      "label": "startup()",
      "file_type": "code",
      "source_file": "src/api/main.py",
      "source_location": "line 31",
      "id": "src_api_main_py_startup",
      "community": 9,
      "norm_label": "startup()"
    },
    {
      "label": "Load config and initialise corrector on startup.",
      "file_type": "rationale",
      "source_file": "src/api/main.py",
      "source_location": "line 31",
      "id": "src_api_main_py_startup_doc",
      "community": 9,
      "norm_label": "load config and initialise corrector on startup."
    },
    {
      "label": "correct_text()",
      "file_type": "code",
      "source_file": "src/api/main.py",
      "source_location": "line 45",
      "id": "src_api_main_py_correct_text",
      "community": 9,
      "norm_label": "correct_text()"
    },
    {
      "label": "Correct dyslectic text with style preservation and academic elevation.",
      "file_type": "rationale",
      "source_file": "src/api/main.py",
      "source_location": "line 45",
      "id": "src_api_main_py_correct_text_doc",
      "community": 9,
      "norm_label": "correct dyslectic text with style preservation and academic elevation."
    },
    {
      "label": "health()",
      "file_type": "code",
      "source_file": "src/api/main.py",
      "source_location": "line 70",
      "id": "src_api_main_py_health",
      "community": 9,
      "norm_label": "health()"
    },
    {
      "label": "Health check endpoint.",
      "file_type": "rationale",
      "source_file": "src/api/main.py",
      "source_location": "line 70",
      "id": "src_api_main_py_health_doc",
      "community": 9,
      "norm_label": "health check endpoint."
    },
    {
      "label": "middleware.py",
      "file_type": "code",
      "source_file": "src/api/middleware.py",
      "id": "src_api_middleware_py",
      "community": 17,
      "norm_label": "middleware.py"
    },
    {
      "label": "API middleware for request logging, rate limiting, and error handling.",
      "file_type": "rationale",
      "source_file": "src/api/middleware.py",
      "id": "src_api_middleware_py_docstring",
      "community": 17,
      "norm_label": "api middleware for request logging, rate limiting, and error handling."
    },
    {
      "label": "RequestLoggingMiddleware",
      "file_type": "code",
      "source_file": "src/api/middleware.py",
      "source_location": "line 13",
      "id": "src_api_middleware_py_RequestLoggingMiddleware",
      "community": 17,
      "norm_label": "requestloggingmiddleware"
    },
    {
      "label": "Logs all incoming requests with timing information.",
      "file_type": "rationale",
      "source_file": "src/api/middleware.py",
      "source_location": "line 13",
      "id": "src_api_middleware_py_RequestLoggingMiddleware_doc",
      "community": 17,
      "norm_label": "logs all incoming requests with timing information."
    },
    {
      "label": "RateLimitMiddleware",
      "file_type": "code",
      "source_file": "src/api/middleware.py",
      "source_location": "line 35",
      "id": "src_api_middleware_py_RateLimitMiddleware",
      "community": 17,
      "norm_label": "ratelimitmiddleware"
    },
    {
      "label": "Simple in-memory rate limiting.",
      "file_type": "rationale",
      "source_file": "src/api/middleware.py",
      "source_location": "line 35",
      "id": "src_api_middleware_py_RateLimitMiddleware_doc",
      "community": 17,
      "norm_label": "simple in-memory rate limiting."
    },
    {
      "label": "dispatch()",
      "file_type": "code",
      "source_file": "src/api/middleware.py",
      "source_location": "line 45",
      "id": "src_api_middleware_py_dispatch",
      "community": 17,
      "norm_label": "dispatch()"
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/api/middleware.py",
      "source_location": "line 38",
      "id": "src_api_middleware_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "schemas.py",
      "file_type": "code",
      "source_file": "src/api/schemas.py",
      "id": "src_api_schemas_py",
      "community": 20,
      "norm_label": "schemas.py"
    },
    {
      "label": "Pydantic schemas for API request/response validation.",
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      "source_file": "src/api/schemas.py",
      "id": "src_api_schemas_py_docstring",
      "community": 20,
      "norm_label": "pydantic schemas for api request/response validation."
    },
    {
      "label": "CorrectionRequest",
      "file_type": "code",
      "source_file": "src/api/schemas.py",
      "source_location": "line 9",
      "id": "src_api_schemas_py_CorrectionRequest",
      "community": 20,
      "norm_label": "correctionrequest"
    },
    {
      "label": "CorrectionResponse",
      "file_type": "code",
      "source_file": "src/api/schemas.py",
      "source_location": "line 15",
      "id": "src_api_schemas_py_CorrectionResponse",
      "community": 20,
      "norm_label": "correctionresponse"
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    {
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      "file_type": "code",
      "source_file": "src/evaluation/authorship_verifier.py",
      "id": "src_evaluation_authorship_verifier_py",
      "community": 8,
      "norm_label": "authorship_verifier.py"
    },
    {
      "label": "Authorship verification module.\nUses a fine-tuned model to verify whether the co",
      "file_type": "rationale",
      "source_file": "src/evaluation/authorship_verifier.py",
      "id": "src_evaluation_authorship_verifier_py_docstring",
      "community": 8,
      "norm_label": "authorship verification module.\nuses a fine-tuned model to verify whether the co"
    },
    {
      "label": "AuthorshipVerifier",
      "file_type": "code",
      "source_file": "src/evaluation/authorship_verifier.py",
      "source_location": "line 14",
      "id": "src_evaluation_authorship_verifier_py_AuthorshipVerifier",
      "community": 8,
      "norm_label": "authorshipverifier"
    },
    {
      "label": "Verifies authorship consistency between input and output text.",
      "file_type": "rationale",
      "source_file": "src/evaluation/authorship_verifier.py",
      "source_location": "line 14",
      "id": "src_evaluation_authorship_verifier_py_AuthorshipVerifier_doc",
      "community": 8,
      "norm_label": "verifies authorship consistency between input and output text."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/evaluation/authorship_verifier.py",
      "source_location": "line 17",
      "id": "src_evaluation_authorship_verifier_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "verify()",
      "file_type": "code",
      "source_file": "src/evaluation/authorship_verifier.py",
      "source_location": "line 26",
      "id": "src_evaluation_authorship_verifier_py_verify",
      "community": 8,
      "norm_label": "verify()"
    },
    {
      "label": "Return probability that both texts were written by the same author.\n\nUses senten",
      "file_type": "rationale",
      "source_file": "src/evaluation/authorship_verifier.py",
      "source_location": "line 26",
      "id": "src_evaluation_authorship_verifier_py_verify_doc",
      "community": 8,
      "norm_label": "return probability that both texts were written by the same author.\n\nuses senten"
    },
    {
      "label": "errant_evaluator.py",
      "file_type": "code",
      "source_file": "src/evaluation/errant_evaluator.py",
      "id": "src_evaluation_errant_evaluator_py",
      "community": 1,
      "norm_label": "errant_evaluator.py"
    },
    {
      "label": "ERRANT-based grammatical error evaluation.\nUses the ERRANT toolkit for standardi",
      "file_type": "rationale",
      "source_file": "src/evaluation/errant_evaluator.py",
      "id": "src_evaluation_errant_evaluator_py_docstring",
      "community": 1,
      "norm_label": "errant-based grammatical error evaluation.\nuses the errant toolkit for standardi"
    },
    {
      "label": "ERRANTEvaluator",
      "file_type": "code",
      "source_file": "src/evaluation/errant_evaluator.py",
      "source_location": "line 11",
      "id": "src_evaluation_errant_evaluator_py_ERRANTEvaluator",
      "community": 1,
      "norm_label": "errantevaluator"
    },
    {
      "label": "Evaluates grammar correction quality using ERRANT annotations.",
      "file_type": "rationale",
      "source_file": "src/evaluation/errant_evaluator.py",
      "source_location": "line 11",
      "id": "src_evaluation_errant_evaluator_py_ERRANTEvaluator_doc",
      "community": 1,
      "norm_label": "evaluates grammar correction quality using errant annotations."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/evaluation/errant_evaluator.py",
      "source_location": "line 14",
      "id": "src_evaluation_errant_evaluator_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "evaluate()",
      "file_type": "code",
      "source_file": "src/evaluation/errant_evaluator.py",
      "source_location": "line 23",
      "id": "src_evaluation_errant_evaluator_py_evaluate",
      "community": 1,
      "norm_label": "evaluate()"
    },
    {
      "label": "Compute ERRANT precision, recall, F0.5.",
      "file_type": "rationale",
      "source_file": "src/evaluation/errant_evaluator.py",
      "source_location": "line 23",
      "id": "src_evaluation_errant_evaluator_py_evaluate_doc",
      "community": 1,
      "norm_label": "compute errant precision, recall, f0.5."
    },
    {
      "label": "gleu_scorer.py",
      "file_type": "code",
      "source_file": "src/evaluation/gleu_scorer.py",
      "id": "src_evaluation_gleu_scorer_py",
      "community": 1,
      "norm_label": "gleu_scorer.py"
    },
    {
      "label": "GLEU (Generalized Language Evaluation Understanding) score.\nPreferred over BLEU ",
      "file_type": "rationale",
      "source_file": "src/evaluation/gleu_scorer.py",
      "id": "src_evaluation_gleu_scorer_py_docstring",
      "community": 1,
      "norm_label": "gleu (generalized language evaluation understanding) score.\npreferred over bleu "
    },
    {
      "label": "GLEUScorer",
      "file_type": "code",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 13",
      "id": "src_evaluation_gleu_scorer_py_GLEUScorer",
      "community": 1,
      "norm_label": "gleuscorer"
    },
    {
      "label": "Computes GLEU and BERTScore metrics for GEC evaluation.",
      "file_type": "rationale",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 13",
      "id": "src_evaluation_gleu_scorer_py_GLEUScorer_doc",
      "community": 1,
      "norm_label": "computes gleu and bertscore metrics for gec evaluation."
    },
    {
      "label": "compute_gleu()",
      "file_type": "code",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 16",
      "id": "src_evaluation_gleu_scorer_py_compute_gleu",
      "community": 1,
      "norm_label": "compute_gleu()"
    },
    {
      "label": "Corpus-level GLEU score (0-100).\n\nGLEU is the geometric mean of n-gram precision",
      "file_type": "rationale",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 16",
      "id": "src_evaluation_gleu_scorer_py_compute_gleu_doc",
      "community": 1,
      "norm_label": "corpus-level gleu score (0-100).\n\ngleu is the geometric mean of n-gram precision"
    },
    {
      "label": "compute_bert_score()",
      "file_type": "code",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 43",
      "id": "src_evaluation_gleu_scorer_py_compute_bert_score",
      "community": 1,
      "norm_label": "compute_bert_score()"
    },
    {
      "label": "Returns (precision, recall, F1) as averages over the batch.",
      "file_type": "rationale",
      "source_file": "src/evaluation/gleu_scorer.py",
      "source_location": "line 43",
      "id": "src_evaluation_gleu_scorer_py_compute_bert_score_doc",
      "community": 1,
      "norm_label": "returns (precision, recall, f1) as averages over the batch."
    },
    {
      "label": "style_metrics.py",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "id": "src_evaluation_style_metrics_py",
      "community": 1,
      "norm_label": "style_metrics.py"
    },
    {
      "label": "Measures style preservation between input and output.\n\nKey metrics:\n  - Style Ve",
      "file_type": "rationale",
      "source_file": "src/evaluation/style_metrics.py",
      "id": "src_evaluation_style_metrics_py_docstring",
      "community": 1,
      "norm_label": "measures style preservation between input and output.\n\nkey metrics:\n  - style ve"
    },
    {
      "label": "StyleEvaluator",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 19",
      "id": "src_evaluation_style_metrics_py_StyleEvaluator",
      "community": 1,
      "norm_label": "styleevaluator"
    },
    {
      "label": "Evaluates style preservation and academic vocabulary coverage.",
      "file_type": "rationale",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 19",
      "id": "src_evaluation_style_metrics_py_StyleEvaluator_doc",
      "community": 1,
      "norm_label": "evaluates style preservation and academic vocabulary coverage."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 22",
      "id": "src_evaluation_style_metrics_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "style_similarity()",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 26",
      "id": "src_evaluation_style_metrics_py_style_similarity",
      "community": 1,
      "norm_label": "style_similarity()"
    },
    {
      "label": "Cosine similarity between style vectors. Target: > 0.85.",
      "file_type": "rationale",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 26",
      "id": "src_evaluation_style_metrics_py_style_similarity_doc",
      "community": 1,
      "norm_label": "cosine similarity between style vectors. target: > 0.85."
    },
    {
      "label": "awl_coverage()",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 39",
      "id": "src_evaluation_style_metrics_py_awl_coverage",
      "community": 1,
      "norm_label": "awl_coverage()"
    },
    {
      "label": "Fraction of content words in AWL. Target: > 0.25.",
      "file_type": "rationale",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 39",
      "id": "src_evaluation_style_metrics_py_awl_coverage_doc",
      "community": 1,
      "norm_label": "fraction of content words in awl. target: > 0.25."
    },
    {
      "label": "evaluate_batch()",
      "file_type": "code",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 54",
      "id": "src_evaluation_style_metrics_py_evaluate_batch",
      "community": 1,
      "norm_label": "evaluate_batch()"
    },
    {
      "label": "Compute style and AWL metrics for a batch.",
      "file_type": "rationale",
      "source_file": "src/evaluation/style_metrics.py",
      "source_location": "line 54",
      "id": "src_evaluation_style_metrics_py_evaluate_batch_doc",
      "community": 1,
      "norm_label": "compute style and awl metrics for a batch."
    },
    {
      "label": "corrector.py",
      "file_type": "code",
      "source_file": "src/inference/corrector.py",
      "id": "src_inference_corrector_py",
      "community": 9,
      "norm_label": "corrector.py"
    },
    {
      "label": "End-to-end inference pipeline.\nAccepts raw dyslectic text (and optionally a mast",
      "file_type": "rationale",
      "source_file": "src/inference/corrector.py",
      "id": "src_inference_corrector_py_docstring",
      "community": 9,
      "norm_label": "end-to-end inference pipeline.\naccepts raw dyslectic text (and optionally a mast"
    },
    {
      "label": "CorrectionResult",
      "file_type": "code",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 34",
      "id": "src_inference_corrector_py_CorrectionResult",
      "community": 9,
      "norm_label": "correctionresult"
    },
    {
      "label": "AcademicCorrector",
      "file_type": "code",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 44",
      "id": "src_inference_corrector_py_AcademicCorrector",
      "community": 9,
      "norm_label": "academiccorrector"
    },
    {
      "label": "Full inference pipeline: preprocess \u2192 fingerprint \u2192 generate \u2192 elevate \u2192 filter.",
      "file_type": "rationale",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 44",
      "id": "src_inference_corrector_py_AcademicCorrector_doc",
      "community": 9,
      "norm_label": "full inference pipeline: preprocess \u2192 fingerprint \u2192 generate \u2192 elevate \u2192 filter."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 47",
      "id": "src_inference_corrector_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "correct()",
      "file_type": "code",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 147",
      "id": "src_inference_corrector_py_correct",
      "community": 9,
      "norm_label": "correct()"
    },
    {
      "label": "Full correction pipeline:\n1. Pre-process (spell correct + parse)\n2. Style finger",
      "file_type": "rationale",
      "source_file": "src/inference/corrector.py",
      "source_location": "line 147",
      "id": "src_inference_corrector_py_correct_doc",
      "community": 9,
      "norm_label": "full correction pipeline:\n1. pre-process (spell correct + parse)\n2. style finger"
    },
    {
      "label": "postprocessor.py",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "id": "src_inference_postprocessor_py",
      "community": 9,
      "norm_label": "postprocessor.py"
    },
    {
      "label": "Post-processing utilities for generated text.\nHandles cleanup, formatting, and f",
      "file_type": "rationale",
      "source_file": "src/inference/postprocessor.py",
      "id": "src_inference_postprocessor_py_docstring",
      "community": 9,
      "norm_label": "post-processing utilities for generated text.\nhandles cleanup, formatting, and f"
    },
    {
      "label": "PostProcessor",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 11",
      "id": "src_inference_postprocessor_py_PostProcessor",
      "community": 9,
      "norm_label": "postprocessor"
    },
    {
      "label": "Cleans and formats generated text after model output.",
      "file_type": "rationale",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 11",
      "id": "src_inference_postprocessor_py_PostProcessor_doc",
      "community": 9,
      "norm_label": "cleans and formats generated text after model output."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 26",
      "id": "src_inference_postprocessor_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "clean()",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 33",
      "id": "src_inference_postprocessor_py_clean",
      "community": 9,
      "norm_label": "clean()"
    },
    {
      "label": "Remove generation artifacts and normalise whitespace.",
      "file_type": "rationale",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 33",
      "id": "src_inference_postprocessor_py_clean_doc",
      "community": 9,
      "norm_label": "remove generation artifacts and normalise whitespace."
    },
    {
      "label": "restore_entities()",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 62",
      "id": "src_inference_postprocessor_py_restore_entities",
      "community": 9,
      "norm_label": "restore_entities()"
    },
    {
      "label": "Restore named entities that may have been altered during generation.\n\nUses fuzzy",
      "file_type": "rationale",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 62",
      "id": "src_inference_postprocessor_py_restore_entities_doc",
      "community": 9,
      "norm_label": "restore named entities that may have been altered during generation.\n\nuses fuzzy"
    },
    {
      "label": "format_output()",
      "file_type": "code",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 90",
      "id": "src_inference_postprocessor_py_format_output",
      "community": 9,
      "norm_label": "format_output()"
    },
    {
      "label": "Apply final formatting (capitalisation, punctuation, spacing).",
      "file_type": "rationale",
      "source_file": "src/inference/postprocessor.py",
      "source_location": "line 90",
      "id": "src_inference_postprocessor_py_format_output_doc",
      "community": 9,
      "norm_label": "apply final formatting (capitalisation, punctuation, spacing)."
    },
    {
      "label": "base_model.py",
      "file_type": "code",
      "source_file": "src/model/base_model.py",
      "id": "src_model_base_model_py",
      "community": 6,
      "norm_label": "base_model.py"
    },
    {
      "label": "Loads and wraps the base pretrained model.\nSupported architectures:\n  - google/f",
      "file_type": "rationale",
      "source_file": "src/model/base_model.py",
      "id": "src_model_base_model_py_docstring",
      "community": 6,
      "norm_label": "loads and wraps the base pretrained model.\nsupported architectures:\n  - google/f"
    },
    {
      "label": "load_model_and_tokenizer()",
      "file_type": "code",
      "source_file": "src/model/base_model.py",
      "source_location": "line 32",
      "id": "src_model_base_model_py_load_model_and_tokenizer",
      "community": 6,
      "norm_label": "load_model_and_tokenizer()"
    },
    {
      "label": "Load a pretrained model with optional LoRA and quantization.\n\nArgs:\n    model_ke",
      "file_type": "rationale",
      "source_file": "src/model/base_model.py",
      "source_location": "line 32",
      "id": "src_model_base_model_py_load_model_and_tokenizer_doc",
      "community": 6,
      "norm_label": "load a pretrained model with optional lora and quantization.\n\nargs:\n    model_ke"
    },
    {
      "label": "generation_utils.py",
      "file_type": "code",
      "source_file": "src/model/generation_utils.py",
      "id": "src_model_generation_utils_py",
      "community": 1,
      "norm_label": "generation_utils.py"
    },
    {
      "label": "Generation utilities for text correction.\nHandles beam search, constrained decod",
      "file_type": "rationale",
      "source_file": "src/model/generation_utils.py",
      "id": "src_model_generation_utils_py_docstring",
      "community": 1,
      "norm_label": "generation utilities for text correction.\nhandles beam search, constrained decod"
    },
    {
      "label": "generate_correction()",
      "file_type": "code",
      "source_file": "src/model/generation_utils.py",
      "source_location": "line 12",
      "id": "src_model_generation_utils_py_generate_correction",
      "community": 1,
      "norm_label": "generate_correction()"
    },
    {
      "label": "Generate corrected text from input tokens.",
      "file_type": "rationale",
      "source_file": "src/model/generation_utils.py",
      "source_location": "line 12",
      "id": "src_model_generation_utils_py_generate_correction_doc",
      "community": 1,
      "norm_label": "generate corrected text from input tokens."
    },
    {
      "label": "batch_generate()",
      "file_type": "code",
      "source_file": "src/model/generation_utils.py",
      "source_location": "line 48",
      "id": "src_model_generation_utils_py_batch_generate",
      "community": 1,
      "norm_label": "batch_generate()"
    },
    {
      "label": "Generate corrections for a batch of texts.",
      "file_type": "rationale",
      "source_file": "src/model/generation_utils.py",
      "source_location": "line 48",
      "id": "src_model_generation_utils_py_batch_generate_doc",
      "community": 1,
      "norm_label": "generate corrections for a batch of texts."
    },
    {
      "label": "lora_adapter.py",
      "file_type": "code",
      "source_file": "src/model/lora_adapter.py",
      "id": "src_model_lora_adapter_py",
      "community": 6,
      "norm_label": "lora_adapter.py"
    },
    {
      "label": "LoRA adapter configuration and management.\nWraps PEFT LoRA utilities for applyin",
      "file_type": "rationale",
      "source_file": "src/model/lora_adapter.py",
      "id": "src_model_lora_adapter_py_docstring",
      "community": 6,
      "norm_label": "lora adapter configuration and management.\nwraps peft lora utilities for applyin"
    },
    {
      "label": "create_lora_config()",
      "file_type": "code",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 12",
      "id": "src_model_lora_adapter_py_create_lora_config",
      "community": 6,
      "norm_label": "create_lora_config()"
    },
    {
      "label": "Create a LoRA configuration for the given task type.",
      "file_type": "rationale",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 12",
      "id": "src_model_lora_adapter_py_create_lora_config_doc",
      "community": 6,
      "norm_label": "create a lora configuration for the given task type."
    },
    {
      "label": "apply_lora()",
      "file_type": "code",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 36",
      "id": "src_model_lora_adapter_py_apply_lora",
      "community": 6,
      "norm_label": "apply_lora()"
    },
    {
      "label": "Apply LoRA adapters to a model and return the wrapped model.",
      "file_type": "rationale",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 36",
      "id": "src_model_lora_adapter_py_apply_lora_doc",
      "community": 6,
      "norm_label": "apply lora adapters to a model and return the wrapped model."
    },
    {
      "label": "merge_lora_weights()",
      "file_type": "code",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 45",
      "id": "src_model_lora_adapter_py_merge_lora_weights",
      "community": 6,
      "norm_label": "merge_lora_weights()"
    },
    {
      "label": "Merge LoRA weights into the base model for inference.\n\nAfter merging, the model ",
      "file_type": "rationale",
      "source_file": "src/model/lora_adapter.py",
      "source_location": "line 45",
      "id": "src_model_lora_adapter_py_merge_lora_weights_doc",
      "community": 6,
      "norm_label": "merge lora weights into the base model for inference.\n\nafter merging, the model "
    },
    {
      "label": "style_conditioner.py",
      "file_type": "code",
      "source_file": "src/model/style_conditioner.py",
      "id": "src_model_style_conditioner_py",
      "community": 6,
      "norm_label": "style_conditioner.py"
    },
    {
      "label": "Injects the style vector into the model via soft prompt conditioning.\nThe style ",
      "file_type": "rationale",
      "source_file": "src/model/style_conditioner.py",
      "id": "src_model_style_conditioner_py_docstring",
      "community": 6,
      "norm_label": "injects the style vector into the model via soft prompt conditioning.\nthe style "
    },
    {
      "label": "StyleConditioner",
      "file_type": "code",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 19",
      "id": "src_model_style_conditioner_py_StyleConditioner",
      "community": 6,
      "norm_label": "styleconditioner"
    },
    {
      "label": "Projects a 512-dim style vector to n_prefix_tokens virtual tokens\nin the model's",
      "file_type": "rationale",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 19",
      "id": "src_model_style_conditioner_py_StyleConditioner_doc",
      "community": 6,
      "norm_label": "projects a 512-dim style vector to n_prefix_tokens virtual tokens\nin the model's"
    },
    {
      "label": "prepend_style_prefix()",
      "file_type": "code",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 61",
      "id": "src_model_style_conditioner_py_prepend_style_prefix",
      "community": 6,
      "norm_label": "prepend_style_prefix()"
    },
    {
      "label": "Concatenates style prefix to input embeddings along sequence dimension.\n\nArgs:\n ",
      "file_type": "rationale",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 61",
      "id": "src_model_style_conditioner_py_prepend_style_prefix_doc",
      "community": 6,
      "norm_label": "concatenates style prefix to input embeddings along sequence dimension.\n\nargs:\n "
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 25",
      "id": "src_model_style_conditioner_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "forward()",
      "file_type": "code",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 44",
      "id": "src_model_style_conditioner_py_forward",
      "community": 6,
      "norm_label": "forward()"
    },
    {
      "label": "Args:\n    style_vector: [batch_size, 512]\nReturns:\n    prefix_embeddings: [batch",
      "file_type": "rationale",
      "source_file": "src/model/style_conditioner.py",
      "source_location": "line 44",
      "id": "src_model_style_conditioner_py_forward_doc",
      "community": 6,
      "norm_label": "args:\n    style_vector: [batch_size, 512]\nreturns:\n    prefix_embeddings: [batch"
    },
    {
      "label": "dependency_parser.py",
      "file_type": "code",
      "source_file": "src/preprocessing/dependency_parser.py",
      "id": "src_preprocessing_dependency_parser_py",
      "community": 13,
      "norm_label": "dependency_parser.py"
    },
    {
      "label": "Dependency parser module.\nExtracts grammatical skeletons (subject-verb-object) f",
      "file_type": "rationale",
      "source_file": "src/preprocessing/dependency_parser.py",
      "id": "src_preprocessing_dependency_parser_py_docstring",
      "community": 13,
      "norm_label": "dependency parser module.\nextracts grammatical skeletons (subject-verb-object) f"
    },
    {
      "label": "DependencyParser",
      "file_type": "code",
      "source_file": "src/preprocessing/dependency_parser.py",
      "source_location": "line 12",
      "id": "src_preprocessing_dependency_parser_py_DependencyParser",
      "community": 13,
      "norm_label": "dependencyparser"
    },
    {
      "label": "Extracts dependency trees and SVO triples from text.",
      "file_type": "rationale",
      "source_file": "src/preprocessing/dependency_parser.py",
      "source_location": "line 12",
      "id": "src_preprocessing_dependency_parser_py_DependencyParser_doc",
      "community": 13,
      "norm_label": "extracts dependency trees and svo triples from text."
    },
    {
      "label": "__init__()",
      "file_type": "code",
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      "norm_label": "programmatically generates dyslectic training data from clean text.\nused to augm"
    },
    {
      "label": "DyslexiaSimulator",
      "file_type": "code",
      "source_file": "src/preprocessing/dyslexia_simulator.py",
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      "community": 5,
      "norm_label": "dyslexiasimulator"
    },
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      "file_type": "rationale",
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      "source_location": "line 31",
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      "source_location": "line 46",
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    },
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    },
    {
      "label": "EntitySpan",
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      "label": "PreprocessedDoc",
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      "source_location": "line 12",
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      "label": "_languagetool_pass()",
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      "source_location": "line 87",
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      "community": 0,
      "norm_label": "_languagetool_pass()"
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      "community": 0,
      "norm_label": "correct()"
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      "label": "close()",
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      "source_location": "line 126",
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      "community": 0,
      "norm_label": "close()"
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    {
      "label": "Clean up LanguageTool resources.",
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      "source_location": "line 126",
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      "community": 0,
      "norm_label": "clean up languagetool resources."
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      "source_location": "line 53",
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      "community": 0,
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      "community": 19,
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    },
    {
      "label": "EmotionClassifier",
      "file_type": "code",
      "source_file": "src/style/emotion_classifier.py",
      "source_location": "line 11",
      "id": "src_style_emotion_classifier_py_EmotionClassifier",
      "community": 19,
      "norm_label": "emotionclassifier"
    },
    {
      "label": "Classifies emotional register of text using keyword-based analysis.",
      "file_type": "rationale",
      "source_file": "src/style/emotion_classifier.py",
      "source_location": "line 11",
      "id": "src_style_emotion_classifier_py_EmotionClassifier_doc",
      "community": 19,
      "norm_label": "classifies emotional register of text using keyword-based analysis."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/style/emotion_classifier.py",
      "source_location": "line 46",
      "id": "src_style_emotion_classifier_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "classify()",
      "file_type": "code",
      "source_file": "src/style/emotion_classifier.py",
      "source_location": "line 49",
      "id": "src_style_emotion_classifier_py_classify",
      "community": 19,
      "norm_label": "classify()"
    },
    {
      "label": "Return emotion distribution over register categories.\n\nReturns a dict with keys:",
      "file_type": "rationale",
      "source_file": "src/style/emotion_classifier.py",
      "source_location": "line 49",
      "id": "src_style_emotion_classifier_py_classify_doc",
      "community": 19,
      "norm_label": "return emotion distribution over register categories.\n\nreturns a dict with keys:"
    },
    {
      "label": "fingerprinter.py",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "id": "src_style_fingerprinter_py",
      "community": 2,
      "norm_label": "fingerprinter.py"
    },
    {
      "label": "Extracts a numerical style vector from any text sample.\nThe style vector encodes",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "id": "src_style_fingerprinter_py_docstring",
      "community": 2,
      "norm_label": "extracts a numerical style vector from any text sample.\nthe style vector encodes"
    },
    {
      "label": "StyleProjectionMLP",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 63",
      "id": "src_style_fingerprinter_py_StyleProjectionMLP",
      "community": 2,
      "norm_label": "styleprojectionmlp"
    },
    {
      "label": "Projects raw feature vector to 512-dim style embedding.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 63",
      "id": "src_style_fingerprinter_py_StyleProjectionMLP_doc",
      "community": 2,
      "norm_label": "projects raw feature vector to 512-dim style embedding."
    },
    {
      "label": "StyleFingerprinter",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 81",
      "id": "src_style_fingerprinter_py_StyleFingerprinter",
      "community": 2,
      "norm_label": "stylefingerprinter"
    },
    {
      "label": "Extracts style fingerprint vectors from text samples.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 81",
      "id": "src_style_fingerprinter_py_StyleFingerprinter_doc",
      "community": 2,
      "norm_label": "extracts style fingerprint vectors from text samples."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 84",
      "id": "src_style_fingerprinter_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "forward()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 77",
      "id": "src_style_fingerprinter_py_forward",
      "community": 2,
      "norm_label": "forward()"
    },
    {
      "label": "_load_awl()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 102",
      "id": "src_style_fingerprinter_py__load_awl",
      "community": 2,
      "norm_label": "_load_awl()"
    },
    {
      "label": "Load Academic Word List from file.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 102",
      "id": "src_style_fingerprinter_py__load_awl_doc",
      "community": 2,
      "norm_label": "load academic word list from file."
    },
    {
      "label": "_passive_voice_ratio()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 115",
      "id": "src_style_fingerprinter_py__passive_voice_ratio",
      "community": 2,
      "norm_label": "_passive_voice_ratio()"
    },
    {
      "label": "Compute ratio of passive voice constructions.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 115",
      "id": "src_style_fingerprinter_py__passive_voice_ratio_doc",
      "community": 2,
      "norm_label": "compute ratio of passive voice constructions."
    },
    {
      "label": "_avg_dep_tree_depth()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 128",
      "id": "src_style_fingerprinter_py__avg_dep_tree_depth",
      "community": 2,
      "norm_label": "_avg_dep_tree_depth()"
    },
    {
      "label": "Compute average dependency tree depth across all tokens.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 128",
      "id": "src_style_fingerprinter_py__avg_dep_tree_depth_doc",
      "community": 2,
      "norm_label": "compute average dependency tree depth across all tokens."
    },
    {
      "label": "_lexical_density()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 145",
      "id": "src_style_fingerprinter_py__lexical_density",
      "community": 2,
      "norm_label": "_lexical_density()"
    },
    {
      "label": "Compute ratio of content words to total words.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 145",
      "id": "src_style_fingerprinter_py__lexical_density_doc",
      "community": 2,
      "norm_label": "compute ratio of content words to total words."
    },
    {
      "label": "_count_syllables()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 160",
      "id": "src_style_fingerprinter_py__count_syllables",
      "community": 2,
      "norm_label": "_count_syllables()"
    },
    {
      "label": "Count syllables in a word using a vowel-group heuristic.\nAvoids NLTK cmudict whi",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 160",
      "id": "src_style_fingerprinter_py__count_syllables_doc",
      "community": 2,
      "norm_label": "count syllables in a word using a vowel-group heuristic.\navoids nltk cmudict whi"
    },
    {
      "label": "_avg_syllables_per_word()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 180",
      "id": "src_style_fingerprinter_py__avg_syllables_per_word",
      "community": 2,
      "norm_label": "_avg_syllables_per_word()"
    },
    {
      "label": "Average syllables per word.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 180",
      "id": "src_style_fingerprinter_py__avg_syllables_per_word_doc",
      "community": 2,
      "norm_label": "average syllables per word."
    },
    {
      "label": "_flesch_reading_ease()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 188",
      "id": "src_style_fingerprinter_py__flesch_reading_ease",
      "community": 2,
      "norm_label": "_flesch_reading_ease()"
    },
    {
      "label": "Compute Flesch Reading Ease score without textstat.\nFormula: 206.835 - 1.015 * A",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 188",
      "id": "src_style_fingerprinter_py__flesch_reading_ease_doc",
      "community": 2,
      "norm_label": "compute flesch reading ease score without textstat.\nformula: 206.835 - 1.015 * a"
    },
    {
      "label": "extract_raw_features()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 212",
      "id": "src_style_fingerprinter_py_extract_raw_features",
      "community": 2,
      "norm_label": "extract_raw_features()"
    },
    {
      "label": "Extract ~40 raw style features from text.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 212",
      "id": "src_style_fingerprinter_py_extract_raw_features_doc",
      "community": 2,
      "norm_label": "extract ~40 raw style features from text."
    },
    {
      "label": "extract_vector()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 309",
      "id": "src_style_fingerprinter_py_extract_vector",
      "community": 2,
      "norm_label": "extract_vector()"
    },
    {
      "label": "Returns a 512-dim style embedding tensor.",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 309",
      "id": "src_style_fingerprinter_py_extract_vector_doc",
      "community": 2,
      "norm_label": "returns a 512-dim style embedding tensor."
    },
    {
      "label": "blend_vectors()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 333",
      "id": "src_style_fingerprinter_py_blend_vectors",
      "community": 2,
      "norm_label": "blend_vectors()"
    },
    {
      "label": "Blend user style with master copy style.\nalpha = weight given to user's own styl",
      "file_type": "rationale",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 333",
      "id": "src_style_fingerprinter_py_blend_vectors_doc",
      "community": 2,
      "norm_label": "blend user style with master copy style.\nalpha = weight given to user's own styl"
    },
    {
      "label": "_depth()",
      "file_type": "code",
      "source_file": "src/style/fingerprinter.py",
      "source_location": "line 130",
      "id": "src_style_fingerprinter_py__depth",
      "community": 2,
      "norm_label": "_depth()"
    },
    {
      "label": "formality_classifier.py",
      "file_type": "code",
      "source_file": "src/style/formality_classifier.py",
      "id": "src_style_formality_classifier_py",
      "community": 14,
      "norm_label": "formality_classifier.py"
    },
    {
      "label": "Formality classifier module.\nClassifies text on a 0-1 formality scale using ling",
      "file_type": "rationale",
      "source_file": "src/style/formality_classifier.py",
      "id": "src_style_formality_classifier_py_docstring",
      "community": 14,
      "norm_label": "formality classifier module.\nclassifies text on a 0-1 formality scale using ling"
    },
    {
      "label": "FormalityClassifier",
      "file_type": "code",
      "source_file": "src/style/formality_classifier.py",
      "source_location": "line 11",
      "id": "src_style_formality_classifier_py_FormalityClassifier",
      "community": 14,
      "norm_label": "formalityclassifier"
    },
    {
      "label": "Scores text formality on a 0-1 scale using rule-based heuristics.",
      "file_type": "rationale",
      "source_file": "src/style/formality_classifier.py",
      "source_location": "line 11",
      "id": "src_style_formality_classifier_py_FormalityClassifier_doc",
      "community": 14,
      "norm_label": "scores text formality on a 0-1 scale using rule-based heuristics."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/style/formality_classifier.py",
      "source_location": "line 36",
      "id": "src_style_formality_classifier_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "score()",
      "file_type": "code",
      "source_file": "src/style/formality_classifier.py",
      "source_location": "line 39",
      "id": "src_style_formality_classifier_py_score",
      "community": 14,
      "norm_label": "score()"
    },
    {
      "label": "Return formality score in [0, 1]. Higher = more formal.\n\nScoring based on:\n- Con",
      "file_type": "rationale",
      "source_file": "src/style/formality_classifier.py",
      "source_location": "line 39",
      "id": "src_style_formality_classifier_py_score_doc",
      "community": 14,
      "norm_label": "return formality score in [0, 1]. higher = more formal.\n\nscoring based on:\n- con"
    },
    {
      "label": "style_vector.py",
      "file_type": "code",
      "source_file": "src/style/style_vector.py",
      "id": "src_style_style_vector_py",
      "community": 8,
      "norm_label": "style_vector.py"
    },
    {
      "label": "Style vector utilities.\nHelper functions for manipulating, comparing, and persis",
      "file_type": "rationale",
      "source_file": "src/style/style_vector.py",
      "id": "src_style_style_vector_py_docstring",
      "community": 8,
      "norm_label": "style vector utilities.\nhelper functions for manipulating, comparing, and persis"
    },
    {
      "label": "cosine_similarity()",
      "file_type": "code",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 11",
      "id": "src_style_style_vector_py_cosine_similarity",
      "community": 8,
      "norm_label": "cosine_similarity()"
    },
    {
      "label": "Compute cosine similarity between two style vectors.",
      "file_type": "rationale",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 11",
      "id": "src_style_style_vector_py_cosine_similarity_doc",
      "community": 8,
      "norm_label": "compute cosine similarity between two style vectors."
    },
    {
      "label": "average_style_vectors()",
      "file_type": "code",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 21",
      "id": "src_style_style_vector_py_average_style_vectors",
      "community": 8,
      "norm_label": "average_style_vectors()"
    },
    {
      "label": "Compute the mean style vector from a list of vectors.",
      "file_type": "rationale",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 21",
      "id": "src_style_style_vector_py_average_style_vectors_doc",
      "community": 8,
      "norm_label": "compute the mean style vector from a list of vectors."
    },
    {
      "label": "save_style_vector()",
      "file_type": "code",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 31",
      "id": "src_style_style_vector_py_save_style_vector",
      "community": 8,
      "norm_label": "save_style_vector()"
    },
    {
      "label": "Persist a style vector to disk.",
      "file_type": "rationale",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 31",
      "id": "src_style_style_vector_py_save_style_vector_doc",
      "community": 8,
      "norm_label": "persist a style vector to disk."
    },
    {
      "label": "load_style_vector()",
      "file_type": "code",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 36",
      "id": "src_style_style_vector_py_load_style_vector",
      "community": 8,
      "norm_label": "load_style_vector()"
    },
    {
      "label": "Load a style vector from disk.",
      "file_type": "rationale",
      "source_file": "src/style/style_vector.py",
      "source_location": "line 36",
      "id": "src_style_style_vector_py_load_style_vector_doc",
      "community": 8,
      "norm_label": "load a style vector from disk."
    },
    {
      "label": "callbacks.py",
      "file_type": "code",
      "source_file": "src/training/callbacks.py",
      "id": "src_training_callbacks_py",
      "community": 18,
      "norm_label": "callbacks.py"
    },
    {
      "label": "Training callbacks for monitoring and checkpointing.\nIntegrates with Weights & B",
      "file_type": "rationale",
      "source_file": "src/training/callbacks.py",
      "id": "src_training_callbacks_py_docstring",
      "community": 18,
      "norm_label": "training callbacks for monitoring and checkpointing.\nintegrates with weights & b"
    },
    {
      "label": "StyleMetricsCallback",
      "file_type": "code",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 16",
      "id": "src_training_callbacks_py_StyleMetricsCallback",
      "community": 18,
      "norm_label": "stylemetricscallback"
    },
    {
      "label": "Logs style similarity metrics during evaluation.",
      "file_type": "rationale",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 16",
      "id": "src_training_callbacks_py_StyleMetricsCallback_doc",
      "community": 18,
      "norm_label": "logs style similarity metrics during evaluation."
    },
    {
      "label": "EarlyStoppingOnStyleDrift",
      "file_type": "code",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 34",
      "id": "src_training_callbacks_py_EarlyStoppingOnStyleDrift",
      "community": 18,
      "norm_label": "earlystoppingonstyledrift"
    },
    {
      "label": "Stops training if style similarity drops below threshold.",
      "file_type": "rationale",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 34",
      "id": "src_training_callbacks_py_EarlyStoppingOnStyleDrift_doc",
      "community": 18,
      "norm_label": "stops training if style similarity drops below threshold."
    },
    {
      "label": "on_evaluate()",
      "file_type": "code",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 43",
      "id": "src_training_callbacks_py_on_evaluate",
      "community": 18,
      "norm_label": "on_evaluate()"
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/training/callbacks.py",
      "source_location": "line 37",
      "id": "src_training_callbacks_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "dataset.py",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "id": "src_training_dataset_py",
      "community": 5,
      "norm_label": "dataset.py"
    },
    {
      "label": "Dataset class that handles all data sources and produces training triplets:\n(inp",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "id": "src_training_dataset_py_docstring",
      "community": 5,
      "norm_label": "dataset class that handles all data sources and produces training triplets:\n(inp"
    },
    {
      "label": "WritingCorrectionDataset",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 45",
      "id": "src_training_dataset_py_WritingCorrectionDataset",
      "community": 5,
      "norm_label": "writingcorrectiondataset"
    },
    {
      "label": "PyTorch dataset for writing correction training triplets.\n\nFully pre-computed at",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "source_location": "line 45",
      "id": "src_training_dataset_py_WritingCorrectionDataset_doc",
      "community": 5,
      "norm_label": "pytorch dataset for writing correction training triplets.\n\nfully pre-computed at"
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 54",
      "id": "src_training_dataset_py___init__",
      "community": 5,
      "norm_label": "__init__()"
    },
    {
      "label": "_compute_cache_key()",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 95",
      "id": "src_training_dataset_py__compute_cache_key",
      "community": 5,
      "norm_label": "_compute_cache_key()"
    },
    {
      "label": "Generate a cache key based on data file content and processing params.",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "source_location": "line 95",
      "id": "src_training_dataset_py__compute_cache_key_doc",
      "community": 5,
      "norm_label": "generate a cache key based on data file content and processing params."
    },
    {
      "label": "_load()",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 107",
      "id": "src_training_dataset_py__load",
      "community": 5,
      "norm_label": "_load()"
    },
    {
      "label": "Load JSONL data file.",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "source_location": "line 107",
      "id": "src_training_dataset_py__load_doc",
      "community": 5,
      "norm_label": "load jsonl data file."
    },
    {
      "label": "_add_synthetic()",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 126",
      "id": "src_training_dataset_py__add_synthetic",
      "community": 5,
      "norm_label": "_add_synthetic()"
    },
    {
      "label": "Augment dataset with synthetic dyslexia examples.",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "source_location": "line 126",
      "id": "src_training_dataset_py__add_synthetic_doc",
      "community": 5,
      "norm_label": "augment dataset with synthetic dyslexia examples."
    },
    {
      "label": "_precompute_all()",
      "file_type": "code",
      "source_file": "src/training/dataset.py",
      "source_location": "line 150",
      "id": "src_training_dataset_py__precompute_all",
      "community": 5,
      "norm_label": "_precompute_all()"
    },
    {
      "label": "Pre-compute tokenisation + style vectors for ALL examples.\nThis makes __getitem_",
      "file_type": "rationale",
      "source_file": "src/training/dataset.py",
      "source_location": "line 150",
      "id": "src_training_dataset_py__precompute_all_doc",
      "community": 5,
      "norm_label": "pre-compute tokenisation + style vectors for all examples.\nthis makes __getitem_"
    },
    {
      "label": "__len__()",
      "file_type": "code",
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      "community": 7,
      "norm_label": "_compute_text_features()"
    },
    {
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      "community": 7,
      "norm_label": "humanpatternfeatureextractor"
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    {
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      "community": 7,
      "norm_label": "kagglehumanpatterndataset"
    },
    {
      "label": "Loads both Kaggle datasets and produces (feature_vector, label) pairs.\nlabel = 1",
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      "community": 7,
      "norm_label": "humanpatternclassifier"
    },
    {
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      "norm_label": "_perplexity()"
    },
    {
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      "community": 7,
      "norm_label": "gpt-2 perplexity for a single text. lower = more ai-like."
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      "community": 7,
      "norm_label": "_perplexity_batch()"
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      "norm_label": "compute gpt-2 perplexity for a batch of texts efficiently on gpu.\n\nprocesses tex"
    },
    {
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      "community": 7,
      "norm_label": "extract()"
    },
    {
      "label": "Extract full 17-dimensional feature vector for a single text.",
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      "source_location": "line 266",
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      "community": 7,
      "norm_label": "extract full 17-dimensional feature vector for a single text."
    },
    {
      "label": "extract_batch()",
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      "source_location": "line 284",
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      "community": 7,
      "norm_label": "extract_batch()"
    },
    {
      "label": "Extract features for many texts efficiently.\n\nStrategy:\n  1. Compute perplexity ",
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      "community": 7,
      "norm_label": "extract features for many texts efficiently.\n\nstrategy:\n  1. compute perplexity "
    },
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      "id": "src_training_human_pattern_extractor_py_precompute_features",
      "community": 7,
      "norm_label": "precompute_features()"
    },
    {
      "label": "Pre-compute all features using optimised batched extraction.",
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      "community": 7,
      "norm_label": "pre-compute all features using optimised batched extraction."
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    {
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      "norm_label": "__len__()"
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      "community": 7,
      "norm_label": "forward()"
    },
    {
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      "source_location": "line 529",
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      "norm_label": "returns human-likeness score in [0, 1]. higher = more human."
    },
    {
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      "id": "src_training_human_pattern_extractor_py_score",
      "community": 7,
      "norm_label": "score()"
    },
    {
      "label": "Convenience: score a single text string.",
      "file_type": "rationale",
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      "source_location": "line 534",
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      "norm_label": "convenience: score a single text string."
    },
    {
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      "id": "src_training_loss_functions_py",
      "community": 8,
      "norm_label": "loss_functions.py"
    },
    {
      "label": "Combined training loss with Human-Pattern Term:\n\nL_total = L_CE + \u03bb\u2081 \u00b7 L_style +",
      "file_type": "rationale",
      "source_file": "src/training/loss_functions.py",
      "id": "src_training_loss_functions_py_docstring",
      "community": 8,
      "norm_label": "combined training loss with human-pattern term:\n\nl_total = l_ce + \u03bb1 \u00b7 l_style +"
    },
    {
      "label": "CombinedCorrectionLoss",
      "file_type": "code",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 24",
      "id": "src_training_loss_functions_py_CombinedCorrectionLoss",
      "community": 8,
      "norm_label": "combinedcorrectionloss"
    },
    {
      "label": "V1 combined loss: L_CE + \u03bb\u2081\u00b7L_style + \u03bb\u2082\u00b7L_semantic.",
      "file_type": "rationale",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 24",
      "id": "src_training_loss_functions_py_CombinedCorrectionLoss_doc",
      "community": 8,
      "norm_label": "v1 combined loss: l_ce + \u03bb1\u00b7l_style + \u03bb2\u00b7l_semantic."
    },
    {
      "label": "CombinedCorrectionLossV2",
      "file_type": "code",
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      "source_location": "line 119",
      "id": "src_training_loss_functions_py_CombinedCorrectionLossV2",
      "community": 8,
      "norm_label": "combinedcorrectionlossv2"
    },
    {
      "label": "V2 combined loss with human-pattern term: L_CE + \u03bb\u2081\u00b7L_style + \u03bb\u2082\u00b7L_semantic + \u03bb\u2083",
      "file_type": "rationale",
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      "community": 8,
      "norm_label": "v2 combined loss with human-pattern term: l_ce + \u03bb1\u00b7l_style + \u03bb2\u00b7l_semantic + \u03bb3"
    },
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      "label": "__init__()",
      "file_type": "code",
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      "source_location": "line 122",
      "id": "src_training_loss_functions_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "_style_loss()",
      "file_type": "code",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 50",
      "id": "src_training_loss_functions_py__style_loss",
      "community": 8,
      "norm_label": "_style_loss()"
    },
    {
      "label": "1 - cosine_similarity(output_style, target_style).",
      "file_type": "rationale",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 50",
      "id": "src_training_loss_functions_py__style_loss_doc",
      "community": 8,
      "norm_label": "1 - cosine_similarity(output_style, target_style)."
    },
    {
      "label": "_semantic_loss()",
      "file_type": "code",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 63",
      "id": "src_training_loss_functions_py__semantic_loss",
      "community": 8,
      "norm_label": "_semantic_loss()"
    },
    {
      "label": "Penalises meaning change between input and output.",
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      "source_file": "src/training/loss_functions.py",
      "source_location": "line 63",
      "id": "src_training_loss_functions_py__semantic_loss_doc",
      "community": 8,
      "norm_label": "penalises meaning change between input and output."
    },
    {
      "label": "forward()",
      "file_type": "code",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 172",
      "id": "src_training_loss_functions_py_forward",
      "community": 8,
      "norm_label": "forward()"
    },
    {
      "label": "Compute combined loss with human pattern term.",
      "file_type": "rationale",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 172",
      "id": "src_training_loss_functions_py_forward_doc",
      "community": 8,
      "norm_label": "compute combined loss with human pattern term."
    },
    {
      "label": "_human_pattern_loss()",
      "file_type": "code",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 162",
      "id": "src_training_loss_functions_py__human_pattern_loss",
      "community": 8,
      "norm_label": "_human_pattern_loss()"
    },
    {
      "label": "Loss = 1 - human_score. Penalise AI-like outputs.",
      "file_type": "rationale",
      "source_file": "src/training/loss_functions.py",
      "source_location": "line 162",
      "id": "src_training_loss_functions_py__human_pattern_loss_doc",
      "community": 8,
      "norm_label": "loss = 1 - human_score. penalise ai-like outputs."
    },
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      "file_type": "code",
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      "norm_label": "trainer.py"
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    {
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      "norm_label": "custom huggingface trainer subclass.\nuses the model's built-in cross-entropy los"
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    {
      "label": "CorrectionTrainer",
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      "norm_label": "correctiontrainer"
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    {
      "label": "Custom trainer \u2014 uses model's built-in loss directly.",
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      "source_location": "line 12",
      "id": "src_training_trainer_py_CorrectionTrainer_doc",
      "community": 16,
      "norm_label": "custom trainer \u2014 uses model's built-in loss directly."
    },
    {
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    {
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      "file_type": "code",
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      "community": 16,
      "norm_label": "_strip_custom_fields()"
    },
    {
      "label": "Remove dataset fields that T5 doesn't accept.",
      "file_type": "rationale",
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      "community": 16,
      "norm_label": "remove dataset fields that t5 doesn't accept."
    },
    {
      "label": "compute_loss()",
      "file_type": "code",
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      "source_location": "line 28",
      "id": "src_training_trainer_py_compute_loss",
      "community": 16,
      "norm_label": "compute_loss()"
    },
    {
      "label": "Use model's built-in CE loss \u2014 avoids double-computing logits loss.",
      "file_type": "rationale",
      "source_file": "src/training/trainer.py",
      "source_location": "line 28",
      "id": "src_training_trainer_py_compute_loss_doc",
      "community": 16,
      "norm_label": "use model's built-in ce loss \u2014 avoids double-computing logits loss."
    },
    {
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      "file_type": "code",
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      "id": "src_training_trainer_py_prediction_step",
      "community": 16,
      "norm_label": "prediction_step()"
    },
    {
      "label": "Compute eval loss directly \u2014 strips custom fields and runs forward.\n\nThe parent'",
      "file_type": "rationale",
      "source_file": "src/training/trainer.py",
      "source_location": "line 39",
      "id": "src_training_trainer_py_prediction_step_doc",
      "community": 16,
      "norm_label": "compute eval loss directly \u2014 strips custom fields and runs forward.\n\nthe parent'"
    },
    {
      "label": "awl_loader.py",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "id": "src_vocabulary_awl_loader_py",
      "community": 3,
      "norm_label": "awl_loader.py"
    },
    {
      "label": "Academic Word List (AWL) loader.\nLoads the Coxhead AWL and supplementary domain-",
      "file_type": "rationale",
      "source_file": "src/vocabulary/awl_loader.py",
      "id": "src_vocabulary_awl_loader_py_docstring",
      "community": 3,
      "norm_label": "academic word list (awl) loader.\nloads the coxhead awl and supplementary domain-"
    },
    {
      "label": "AWLLoader",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 13",
      "id": "src_vocabulary_awl_loader_py_AWLLoader",
      "community": 3,
      "norm_label": "awlloader"
    },
    {
      "label": "Loads and manages Academic Word List data.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 13",
      "id": "src_vocabulary_awl_loader_py_AWLLoader_doc",
      "community": 3,
      "norm_label": "loads and manages academic word list data."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 16",
      "id": "src_vocabulary_awl_loader_py___init__",
      "community": 4,
      "norm_label": "__init__()"
    },
    {
      "label": "_load_word_list()",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 39",
      "id": "src_vocabulary_awl_loader_py__load_word_list",
      "community": 3,
      "norm_label": "_load_word_list()"
    },
    {
      "label": "Load a word list file into a set of lowercase words.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 39",
      "id": "src_vocabulary_awl_loader_py__load_word_list_doc",
      "community": 3,
      "norm_label": "load a word list file into a set of lowercase words."
    },
    {
      "label": "_load_synonyms()",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 52",
      "id": "src_vocabulary_awl_loader_py__load_synonyms",
      "community": 3,
      "norm_label": "_load_synonyms()"
    },
    {
      "label": "Load academic synonym mappings from JSON.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 52",
      "id": "src_vocabulary_awl_loader_py__load_synonyms_doc",
      "community": 3,
      "norm_label": "load academic synonym mappings from json."
    },
    {
      "label": "is_academic()",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 66",
      "id": "src_vocabulary_awl_loader_py_is_academic",
      "community": 3,
      "norm_label": "is_academic()"
    },
    {
      "label": "Check if a word (or its lemma) is in the AWL.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 66",
      "id": "src_vocabulary_awl_loader_py_is_academic_doc",
      "community": 3,
      "norm_label": "check if a word (or its lemma) is in the awl."
    },
    {
      "label": "get_academic_synonyms()",
      "file_type": "code",
      "source_file": "src/vocabulary/awl_loader.py",
      "source_location": "line 70",
      "id": "src_vocabulary_awl_loader_py_get_academic_synonyms",
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    },
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      "norm_label": "return academic synonyms for a colloquial word."
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      "norm_label": "all_words()"
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      "source_location": "line 75",
      "id": "src_vocabulary_awl_loader_py_all_words_doc",
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      "norm_label": "return the full set of academic words."
    },
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      "label": "lexical_substitution.py",
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      "label": "Post-generation academic vocabulary elevation module.\n\nPipeline:\n1. POS-tag the ",
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    {
      "label": "LexicalElevator",
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      "id": "src_vocabulary_lexical_substitution_py_LexicalElevator",
      "community": 3,
      "norm_label": "lexicalelevator"
    },
    {
      "label": "Elevates vocabulary to academic register using BERT-based substitution.",
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      "norm_label": "elevates vocabulary to academic register using bert-based substitution."
    },
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      "label": "RegisterFilter",
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      "id": "src_vocabulary_lexical_substitution_py_RegisterFilter",
      "community": 3,
      "norm_label": "registerfilter"
    },
    {
      "label": "Applies register-level corrections to ensure academic tone:\n- Converts contracti",
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      "source_location": "line 178",
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      "norm_label": "applies register-level corrections to ensure academic tone:\n- converts contracti"
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      "id": "src_vocabulary_lexical_substitution_py__sem_similarity",
      "community": 3,
      "norm_label": "_sem_similarity()"
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    {
      "label": "Compute contextual semantic similarity using sentence embeddings.",
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      "source_location": "line 67",
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      "community": 3,
      "norm_label": "compute contextual semantic similarity using sentence embeddings."
    },
    {
      "label": "_get_awl_substitutions()",
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      "norm_label": "_get_awl_substitutions()"
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    {
      "label": "Generate candidate AWL substitutions using BERT fill-mask.",
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      "source_location": "line 77",
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      "community": 3,
      "norm_label": "generate candidate awl substitutions using bert fill-mask."
    },
    {
      "label": "elevate()",
      "file_type": "code",
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      "id": "src_vocabulary_lexical_substitution_py_elevate",
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      "norm_label": "elevate()"
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    {
      "label": "Main entry point: elevates vocabulary to academic register.",
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      "community": 3,
      "norm_label": "main entry point: elevates vocabulary to academic register."
    },
    {
      "label": "apply()",
      "file_type": "code",
      "source_file": "src/vocabulary/lexical_substitution.py",
      "source_location": "line 222",
      "id": "src_vocabulary_lexical_substitution_py_apply",
      "community": 3,
      "norm_label": "apply()"
    },
    {
      "label": "Apply contraction expansion and colloquial-to-academic substitution.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/lexical_substitution.py",
      "source_location": "line 222",
      "id": "src_vocabulary_lexical_substitution_py_apply_doc",
      "community": 3,
      "norm_label": "apply contraction expansion and colloquial-to-academic substitution."
    },
    {
      "label": "register_filter.py",
      "file_type": "code",
      "source_file": "src/vocabulary/register_filter.py",
      "id": "src_vocabulary_register_filter_py",
      "community": 14,
      "norm_label": "register_filter.py"
    },
    {
      "label": "Register filter module.\nApplies register-level post-processing to ensure output ",
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      "source_file": "src/vocabulary/register_filter.py",
      "id": "src_vocabulary_register_filter_py_docstring",
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      "norm_label": "register filter module.\napplies register-level post-processing to ensure output "
    },
    {
      "label": "RegisterFilterAdvanced",
      "file_type": "code",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 12",
      "id": "src_vocabulary_register_filter_py_RegisterFilterAdvanced",
      "community": 14,
      "norm_label": "registerfilteradvanced"
    },
    {
      "label": "Advanced register filtering with nominalisation and hedging passes.",
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      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 12",
      "id": "src_vocabulary_register_filter_py_RegisterFilterAdvanced_doc",
      "community": 14,
      "norm_label": "advanced register filtering with nominalisation and hedging passes."
    },
    {
      "label": "__init__()",
      "file_type": "code",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 54",
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      "norm_label": "__init__()"
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    {
      "label": "nominalise()",
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      "source_location": "line 57",
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      "community": 14,
      "norm_label": "nominalise()"
    },
    {
      "label": "Convert verbal phrases to nominal forms where appropriate.\n\nOnly applies to clea",
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      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 57",
      "id": "src_vocabulary_register_filter_py_nominalise_doc",
      "community": 14,
      "norm_label": "convert verbal phrases to nominal forms where appropriate.\n\nonly applies to clea"
    },
    {
      "label": "add_hedging()",
      "file_type": "code",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 76",
      "id": "src_vocabulary_register_filter_py_add_hedging",
      "community": 14,
      "norm_label": "add_hedging()"
    },
    {
      "label": "Add hedging language where claims are too absolute.\n\nOnly applies to the first o",
      "file_type": "rationale",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 76",
      "id": "src_vocabulary_register_filter_py_add_hedging_doc",
      "community": 14,
      "norm_label": "add hedging language where claims are too absolute.\n\nonly applies to the first o"
    },
    {
      "label": "check_formality()",
      "file_type": "code",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 93",
      "id": "src_vocabulary_register_filter_py_check_formality",
      "community": 14,
      "norm_label": "check_formality()"
    },
    {
      "label": "Score text formality on 0-1 scale.",
      "file_type": "rationale",
      "source_file": "src/vocabulary/register_filter.py",
      "source_location": "line 93",
      "id": "src_vocabulary_register_filter_py_check_formality_doc",
      "community": 14,
      "norm_label": "score text formality on 0-1 scale."
    },
    {
      "label": "test_evaluation.py",
      "file_type": "code",
      "source_file": "tests/test_evaluation.py",
      "id": "tests_test_evaluation_py",
      "community": 1,
      "norm_label": "test_evaluation.py"
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    {
      "label": "Tests for the evaluation framework.",
      "file_type": "rationale",
      "source_file": "tests/test_evaluation.py",
      "id": "tests_test_evaluation_py_docstring",
      "community": 1,
      "norm_label": "tests for the evaluation framework."
    },
    {
      "label": "test_gleu_scorer_instantiation()",
      "file_type": "code",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 7",
      "id": "tests_test_evaluation_py_test_gleu_scorer_instantiation",
      "community": 1,
      "norm_label": "test_gleu_scorer_instantiation()"
    },
    {
      "label": "Test that GLEU scorer can be created.",
      "file_type": "rationale",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 7",
      "id": "tests_test_evaluation_py_test_gleu_scorer_instantiation_doc",
      "community": 1,
      "norm_label": "test that gleu scorer can be created."
    },
    {
      "label": "test_gleu_perfect_score()",
      "file_type": "code",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 13",
      "id": "tests_test_evaluation_py_test_gleu_perfect_score",
      "community": 1,
      "norm_label": "test_gleu_perfect_score()"
    },
    {
      "label": "Test that identical predictions and references score high.",
      "file_type": "rationale",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 13",
      "id": "tests_test_evaluation_py_test_gleu_perfect_score_doc",
      "community": 1,
      "norm_label": "test that identical predictions and references score high."
    },
    {
      "label": "test_gleu_empty_input()",
      "file_type": "code",
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      "source_location": "line 22",
      "id": "tests_test_evaluation_py_test_gleu_empty_input",
      "community": 1,
      "norm_label": "test_gleu_empty_input()"
    },
    {
      "label": "Test empty input handling.",
      "file_type": "rationale",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 22",
      "id": "tests_test_evaluation_py_test_gleu_empty_input_doc",
      "community": 1,
      "norm_label": "test empty input handling."
    },
    {
      "label": "test_awl_coverage_score()",
      "file_type": "code",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 28",
      "id": "tests_test_evaluation_py_test_awl_coverage_score",
      "community": 1,
      "norm_label": "test_awl_coverage_score()"
    },
    {
      "label": "Test AWL coverage scoring.",
      "file_type": "rationale",
      "source_file": "tests/test_evaluation.py",
      "source_location": "line 28",
      "id": "tests_test_evaluation_py_test_awl_coverage_score_doc",
      "community": 1,
      "norm_label": "test awl coverage scoring."
    },
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      "label": "test_model.py",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "id": "tests_test_model_py",
      "community": 6,
      "norm_label": "test_model.py"
    },
    {
      "label": "Tests for the core model module.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "id": "tests_test_model_py_docstring",
      "community": 6,
      "norm_label": "tests for the core model module."
    },
    {
      "label": "test_model_registry_populated()",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "source_location": "line 11",
      "id": "tests_test_model_py_test_model_registry_populated",
      "community": 6,
      "norm_label": "test_model_registry_populated()"
    },
    {
      "label": "Test that model registries are defined.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "source_location": "line 11",
      "id": "tests_test_model_py_test_model_registry_populated_doc",
      "community": 6,
      "norm_label": "test that model registries are defined."
    },
    {
      "label": "test_invalid_model_key()",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "source_location": "line 17",
      "id": "tests_test_model_py_test_invalid_model_key",
      "community": 6,
      "norm_label": "test_invalid_model_key()"
    },
    {
      "label": "Test that unknown model keys raise ValueError.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "source_location": "line 17",
      "id": "tests_test_model_py_test_invalid_model_key_doc",
      "community": 6,
      "norm_label": "test that unknown model keys raise valueerror."
    },
    {
      "label": "test_style_conditioner_output_shape()",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "source_location": "line 23",
      "id": "tests_test_model_py_test_style_conditioner_output_shape",
      "community": 6,
      "norm_label": "test_style_conditioner_output_shape()"
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    {
      "label": "Test that style conditioner produces correct tensor shapes.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "source_location": "line 23",
      "id": "tests_test_model_py_test_style_conditioner_output_shape_doc",
      "community": 6,
      "norm_label": "test that style conditioner produces correct tensor shapes."
    },
    {
      "label": "test_prepend_style_prefix()",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "source_location": "line 32",
      "id": "tests_test_model_py_test_prepend_style_prefix",
      "community": 6,
      "norm_label": "test_prepend_style_prefix()"
    },
    {
      "label": "Test prefix prepending dimensions.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "source_location": "line 32",
      "id": "tests_test_model_py_test_prepend_style_prefix_doc",
      "community": 6,
      "norm_label": "test prefix prepending dimensions."
    },
    {
      "label": "test_lora_config_creation()",
      "file_type": "code",
      "source_file": "tests/test_model.py",
      "source_location": "line 40",
      "id": "tests_test_model_py_test_lora_config_creation",
      "community": 6,
      "norm_label": "test_lora_config_creation()"
    },
    {
      "label": "Test LoRA config creation.",
      "file_type": "rationale",
      "source_file": "tests/test_model.py",
      "source_location": "line 40",
      "id": "tests_test_model_py_test_lora_config_creation_doc",
      "community": 6,
      "norm_label": "test lora config creation."
    },
    {
      "label": "test_preprocessing.py",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "id": "tests_test_preprocessing_py",
      "community": 0,
      "norm_label": "test_preprocessing.py"
    },
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      "label": "Tests for the preprocessing pipeline.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "id": "tests_test_preprocessing_py_docstring",
      "community": 0,
      "norm_label": "tests for the preprocessing pipeline."
    },
    {
      "label": "simulator()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 9",
      "id": "tests_test_preprocessing_py_simulator",
      "community": 0,
      "norm_label": "simulator()"
    },
    {
      "label": "corrector()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 14",
      "id": "tests_test_preprocessing_py_corrector",
      "community": 0,
      "norm_label": "corrector()"
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    {
      "label": "test_spell_correction_phonetic()",
      "file_type": "code",
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      "community": 0,
      "norm_label": "test_spell_correction_phonetic()"
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    {
      "label": "Test that common dyslexic misspellings are corrected.",
      "file_type": "rationale",
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      "source_location": "line 20",
      "id": "tests_test_preprocessing_py_test_spell_correction_phonetic_doc",
      "community": 0,
      "norm_label": "test that common dyslexic misspellings are corrected."
    },
    {
      "label": "test_spell_correction_empty()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 27",
      "id": "tests_test_preprocessing_py_test_spell_correction_empty",
      "community": 0,
      "norm_label": "test_spell_correction_empty()"
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      "label": "Test empty input handling.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 27",
      "id": "tests_test_preprocessing_py_test_spell_correction_empty_doc",
      "community": 0,
      "norm_label": "test empty input handling."
    },
    {
      "label": "test_entity_protection()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 33",
      "id": "tests_test_preprocessing_py_test_entity_protection",
      "community": 0,
      "norm_label": "test_entity_protection()"
    },
    {
      "label": "Test that named entities are identified and protected.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 33",
      "id": "tests_test_preprocessing_py_test_entity_protection_doc",
      "community": 0,
      "norm_label": "test that named entities are identified and protected."
    },
    {
      "label": "test_sentence_segmentation()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 43",
      "id": "tests_test_preprocessing_py_test_sentence_segmentation",
      "community": 0,
      "norm_label": "test_sentence_segmentation()"
    },
    {
      "label": "Test that text is correctly split into sentences.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 43",
      "id": "tests_test_preprocessing_py_test_sentence_segmentation_doc",
      "community": 0,
      "norm_label": "test that text is correctly split into sentences."
    },
    {
      "label": "test_readability_scores()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 51",
      "id": "tests_test_preprocessing_py_test_readability_scores",
      "community": 0,
      "norm_label": "test_readability_scores()"
    },
    {
      "label": "Test that readability metrics are computed.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 51",
      "id": "tests_test_preprocessing_py_test_readability_scores_doc",
      "community": 0,
      "norm_label": "test that readability metrics are computed."
    },
    {
      "label": "test_dependency_trees()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 61",
      "id": "tests_test_preprocessing_py_test_dependency_trees",
      "community": 0,
      "norm_label": "test_dependency_trees()"
    },
    {
      "label": "Test that dependency trees are extracted.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 61",
      "id": "tests_test_preprocessing_py_test_dependency_trees_doc",
      "community": 0,
      "norm_label": "test that dependency trees are extracted."
    },
    {
      "label": "test_dyslexia_simulator()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 70",
      "id": "tests_test_preprocessing_py_test_dyslexia_simulator",
      "community": 0,
      "norm_label": "test_dyslexia_simulator()"
    },
    {
      "label": "Test that the simulator produces corrupted text.",
      "file_type": "rationale",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 70",
      "id": "tests_test_preprocessing_py_test_dyslexia_simulator_doc",
      "community": 0,
      "norm_label": "test that the simulator produces corrupted text."
    },
    {
      "label": "test_dyslexia_simulator_preserves_clean()",
      "file_type": "code",
      "source_file": "tests/test_preprocessing.py",
      "source_location": "line 79",
      "id": "tests_test_preprocessing_py_test_dyslexia_simulator_preserves_clean",
      "community": 0,
      "norm_label": "test_dyslexia_simulator_preserves_clean()"
    },
    {
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