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	Skip duplicate lexeme rank setting (#5401)
Skip duplicate lexeme rank setting within `_fix_pretrained_vectors_name()`.
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				|  | @ -279,13 +279,14 @@ class PrecomputableAffine(Model): | |||
|                 break | ||||
| 
 | ||||
| 
 | ||||
| def link_vectors_to_models(vocab): | ||||
| def link_vectors_to_models(vocab, skip_rank=False): | ||||
|     vectors = vocab.vectors | ||||
|     if vectors.name is None: | ||||
|         vectors.name = VECTORS_KEY | ||||
|         if vectors.data.size != 0: | ||||
|             warnings.warn(Warnings.W020.format(shape=vectors.data.shape)) | ||||
|     ops = Model.ops | ||||
|     if not skip_rank: | ||||
|         for word in vocab: | ||||
|             if word.orth in vectors.key2row: | ||||
|                 word.rank = vectors.key2row[word.orth] | ||||
|  |  | |||
|  | @ -1072,7 +1072,7 @@ def _fix_pretrained_vectors_name(nlp): | |||
|     else: | ||||
|         raise ValueError(Errors.E092) | ||||
|     if nlp.vocab.vectors.size != 0: | ||||
|         link_vectors_to_models(nlp.vocab) | ||||
|         link_vectors_to_models(nlp.vocab, skip_rank=True) | ||||
|     for name, proc in nlp.pipeline: | ||||
|         if not hasattr(proc, "cfg"): | ||||
|             continue | ||||
|  |  | |||
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