{
  "context": "Evaluation checks how well the solution uses gensim's cross-lingual mapping tools to align embedding spaces, translate tokens, and score top-k accuracy for the provided spec. Emphasis is on using TranslationMatrix/KeyedVectors APIs rather than hand-rolled linear algebra or search.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "TranslationMatrix fit",
      "description": "Creates and trains `gensim.models.translation_matrix.TranslationMatrix` (or `BackMappingTranslationMatrix` when appropriate) with provided source/target `KeyedVectors` and seed pairs instead of manual regression.",
      "max_score": 35
    },
    {
      "name": "Seed filtering",
      "description": "Leverages `KeyedVectors` vocabulary checks or TranslationMatrix preprocessing to drop seed pairs missing from either model while still fitting when enough valid pairs remain.",
      "max_score": 15
    },
    {
      "name": "Translate topn",
      "description": "Uses `TranslationMatrix.translate(..., topn=...)` to return ordered candidate translations per source token and handles out-of-vocabulary queries by returning an empty list before applying further logic.",
      "max_score": 25
    },
    {
      "name": "Accuracy from translate",
      "description": "Computes top-k accuracy by reusing `TranslationMatrix.translate` outputs (or BackMapping translate) instead of manual similarity loops, counting hits where the gold target appears within `k`.",
      "max_score": 15
    },
    {
      "name": "KeyedVectors usage",
      "description": "Retrieves vectors and vocabulary membership via `gensim.models.KeyedVectors` APIs (indexing, `__contains__`, `get_vector`) rather than custom storage when preparing seeds or translations.",
      "max_score": 10
    }
  ]
}