I have big gensim Doc2vec model, I only need to infer vectors while i am loading the training documents vectors from other source. Is it possible to load it as is without the big npy file
I did
Edit:
from gensim.models.doc2vec import Doc2Vec
model_path = r'C:\model/model'
model = Doc2Vec.load(model_path)
model.delete_temporary_training_data(keep_doctags_vectors=False, keep_inference=True)
model.save(model_path)
remove the files (model.trainables.syn1neg.npy,model.wv.vectors.npy) manually
model = Doc2Vec.load(model_path)
but it ask for
Traceback (most recent call last):
File "<ipython-input-5-7f868a7dbe0c>", line 1, in <module>
model = Doc2Vec.load(model_path)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\models\doc2vec.py", line 1113, in load
return super(Doc2Vec, cls).load(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\models\base_any2vec.py", line 1244, in load
model = super(BaseWordEmbeddingsModel, cls).load(*args, **kwargs)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\models\base_any2vec.py", line 603, in load
return super(BaseAny2VecModel, cls).load(fname_or_handle, **kwargs)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\utils.py", line 427, in load
obj._load_specials(fname, mmap, compress, subname)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\utils.py", line 458, in _load_specials
getattr(self, attrib)._load_specials(cfname, mmap, compress, subname)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\gensim\utils.py", line 469, in _load_specials
val = np.load(subname(fname, attrib), mmap_mode=mmap)
File "C:\ProgramData\Anaconda3\envs\py\lib\site-packages\numpy\lib\npyio.py", line 428, in load
fid = open(os_fspath(file), "rb")
FileNotFoundError: [Errno 2] No such file or directory: 'C:\\model/model.trainables.syn1neg.npy'
Note: Those files not exists in the directory, The model run on a server and download the model file from the storage My question is, Do the model must have those files for inference? I want to run it as low memory consumption as possible. Thanks.
Edit: Is the file model.trainables.syn1neg.npy is the model weights? Is the file model.wv.vectors.npy is necessary for running an inference?
I'm not a fan of the delete_temporary_training_data() method. It implies there's a clearer separation between training-state and that needed for later uses. (Inference is very similar to training, though it doesn't need the cached doc-vectors for training texts.)
That said, if you've used that method, you shouldn't then be deleting any of the side-files that were still part of the save. If they were written by .save(), they'll be expected, by name, by the .load(). They must be kept with the main model file. (There might be fewer such files, or smaller such files, after the delete_temporary_training_data() call - but any written must be kept for reading.)
The syn1neg file is absolutely required for inference: it's the model's hidden-to-output weights, needed to perform new forward-predictions (and thus also backpropagated inference-adjustments). The wv.vectors file is definitely needed in default dm=1 mode, where word-vectors are part of the doc-vector calculation. (It might be optional in dm=0 mode, but I'm not sure the code is armored against them being absent - not via in-memory trimming, and definitely not against the expected file being deleted out-of-band.)