{
  "context": "Evaluates how well the solution uses gensim's vector weighting and projection models to deliver TF-IDF/log-entropy transforms, BM25 ranking, optional random projection, and top-term inspection. Confirms the implementation leans on built-in components instead of reimplementing the algorithms.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Dictionary setup",
      "description": "Builds the corpus with gensim.corpora.Dictionary and doc2bow, reuses the same dictionary/id2word mapping for all transformations and queries, and ignores unseen tokens instead of inventing ids.",
      "max_score": 15
    },
    {
      "name": "TF-IDF transform",
      "description": "Instantiates gensim.models.TfidfModel on the training corpus (with normalization enabled) and applies it to documents to yield sorted TF-IDF weight vectors for the specified weighting mode.",
      "max_score": 20
    },
    {
      "name": "Log-entropy transform",
      "description": "Uses gensim.models.LogEntropyModel (with normalize=True or wrapped in NormModel) on the same dictionary to produce log-entropy vectors and verifies their L2 norm when normalization is requested.",
      "max_score": 15
    },
    {
      "name": "BM25 ranking",
      "description": "Creates a BM25 model (OkapiBM25Model/LuceneBM25Model/AtireBM25Model) from the training corpus and dictionary, uses get_scores/get_batch_scores to rank documents for a query, and sorts results in descending score order.",
      "max_score": 20
    },
    {
      "name": "Random projection",
      "description": "Builds a gensim.models.RpModel over a weighted corpus with the requested projection_dim and applies it to transform documents into fixed-length dense vectors, keeping projections deterministic for repeated inputs.",
      "max_score": 15
    },
    {
      "name": "Normalization & ordering",
      "description": "Applies gensim.models.NormModel or matutils.unitvec to enforce unit-length vectors when normalize=True and consistently sorts weight tuples from highest to lowest value before returning them.",
      "max_score": 10
    },
    {
      "name": "Top-term mapping",
      "description": "Maps weighted ids back to tokens via dictionary.id2token/id2word for top_terms, honoring the requested limit and preserving descending weight order.",
      "max_score": 5
    }
  ]
}