I am using spacy for NER, and later the S2V standalone on a smaller portion of the NER hits.
In the class that implements the NER model, there is a call to require_gpu to ensure transformer inference is fast. Attempting to use the s2v standalone in the same process afterwards results in an exception from cupy complaining about implicit conversion from the cupy tensor to a numpy array.
TypeError Traceback (most recent call last)
Cell In[2], line 7
3 import sense2vec
5 s2v = sense2vec.Sense2Vec().from_disk("s2v_reddit_2019_lg")
----> 7 s2v.most_similar("Bart_Simpson|PERSON")
File [c:\Users\LPB\anaconda3\envs\sherlock\lib\site-packages\sense2vec\sense2vec.py:226](file:///C:/Users/LPB/anaconda3/envs/sherlock/lib/site-packages/sense2vec/sense2vec.py:226), in Sense2Vec.most_similar(self, keys, n, batch_size)
224 # Always ask for more because we'll always get the keys themselves
225 n = min(len(self.vectors), n + len(keys))
--> 226 rows = numpy.asarray(self.vectors.find(keys=keys))
227 vecs = self.vectors.data[rows]
228 average = vecs.mean(axis=0, keepdims=True)
File cupy\_core\core.pyx:1397, in cupy._core.core.ndarray.__array__()
TypeError: Implicit conversion to a NumPy array is not allowed. Please use `.get()` to construct a NumPy array explicitly.
I am using spacy for NER, and later the S2V standalone on a smaller portion of the NER hits. In the class that implements the NER model, there is a call to require_gpu to ensure transformer inference is fast. Attempting to use the s2v standalone in the same process afterwards results in an exception from cupy complaining about implicit conversion from the cupy tensor to a numpy array.
Code snippet:
Traceback:
Versions: cupy-cuda112 10.6.0 sense2vec 2.0.1 spacy 3.4.3 spacy-alignments 0.8.6 spacy-transformers 1.1.8