_spacy.py
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import numpy as np
from tqdm import tqdm
from typing import List
from bertopic.backend import BaseEmbedder
class SpacyBackend(BaseEmbedder):
"""Spacy embedding model.
The Spacy embedding model used for generating document and
word embeddings.
Arguments:
embedding_model: A spacy embedding model
Examples:
To create a Spacy backend, you need to create an nlp object and
pass it through this backend:
```python
import spacy
from bertopic.backend import SpacyBackend
nlp = spacy.load("en_core_web_md", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
spacy_model = SpacyBackend(nlp)
```
To load in a transformer model use the following:
```python
import spacy
from thinc.api import set_gpu_allocator, require_gpu
from bertopic.backend import SpacyBackend
nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
set_gpu_allocator("pytorch")
require_gpu(0)
spacy_model = SpacyBackend(nlp)
```
If you run into gpu/memory-issues, please use:
```python
import spacy
from bertopic.backend import SpacyBackend
spacy.prefer_gpu()
nlp = spacy.load("en_core_web_trf", exclude=['tagger', 'parser', 'ner', 'attribute_ruler', 'lemmatizer'])
spacy_model = SpacyBackend(nlp)
```
"""
def __init__(self, embedding_model):
super().__init__()
if "spacy" in str(type(embedding_model)):
self.embedding_model = embedding_model
else:
raise ValueError(
"Please select a correct Spacy model by either using a string such as 'en_core_web_md' "
"or create a nlp model using: `nlp = spacy.load('en_core_web_md')"
)
def embed(self, documents: List[str], verbose: bool = False) -> np.ndarray:
"""Embed a list of n documents/words into an n-dimensional
matrix of embeddings.
Arguments:
documents: A list of documents or words to be embedded
verbose: Controls the verbosity of the process
Returns:
Document/words embeddings with shape (n, m) with `n` documents/words
that each have an embeddings size of `m`
"""
# Handle empty documents, spaCy models automatically map
# empty strings to the zero vector
empty_document = " "
# Extract embeddings
embeddings = []
for doc in tqdm(documents, position=0, leave=True, disable=not verbose):
embedding = self.embedding_model(doc or empty_document)
if embedding.has_vector:
embedding = embedding.vector
else:
embedding = embedding._.trf_data.tensors[-1][0]
if not isinstance(embedding, np.ndarray) and hasattr(embedding, "get"):
# Convert cupy array to numpy array
embedding = embedding.get()
embeddings.append(embedding)
return np.array(embeddings)