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