Spacy models with different word2vec embeddings give the same scores

[kmariael]

I am trying to improve the performance of my spacy NER model by implementing my pretrained vectors. I have created my own vectors with word2vec using different texts and I have saved them in .txt files. However I get the exact same scores and this doesn't seem right.

Here are the steps I have been following for one file with custom pretrained embeddings:

!python -m spacy init vectors en /content/drive/MyDrive/MODELS_W2V/JSTOR_uncleaned_sents_model.txt ./uncl_txt --name JSTOR_unlceaned_sents_model

nlp = spacy.load("./uncl_txt")
nlp.add_pipe("ner")
nlp.to_disk("./uncl_txt")

!python -m spacy train /content/uncl_txt/config.cfg --paths.train ./Spacy/train.spacy --paths.dev ./Spacy/dev.spacy --output ./uncl_txt --paths.vectors ./uncl_txt

!python -m spacy evaluate /content/uncl_txt/model-best ./Spacy/eval.spacy --output ner_with_uncleaned_sents_vectors.jsonl

Here are the steps for the other embeddings file:

!python -m spacy init vectors en /content/drive/MyDrive/MODELS_W2V/JSTOR_abs_model.txt ./abs --name JSTOR_abs_model

nlp = spacy.load("./abs")
nlp.add_pipe("ner")
nlp.to_disk("./abs")

!python -m spacy train /content/abs/config.cfg --paths.train ./Spacy/train.spacy --paths.dev ./Spacy/dev.spacy --output ./abs/ --paths.vectors ./abs

!python -m spacy evaluate ./abs/model-best ./Spacy/eval.spacy --output ner_with_abs_vectors.jsonl

Am I doing something wrong? Should I add something in the config file?

[polm]

Answered here - sounds like use of word vectors wasn't enabled.