戒酒的李白
130 lines
5.1 KiB
Python
130 lines
5.1 KiB
Python
import numpy as np
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from typing import List, Union
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from model2vec import StaticModel
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from sklearn.feature_extraction.text import CountVectorizer
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from bertopic.backend import BaseEmbedder
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class Model2VecBackend(BaseEmbedder):
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"""Model2Vec embedding model.
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Arguments:
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embedding_model: Either a model2vec model or a
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string pointing to a model2vec model
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distill: Indicates whether to distill a sentence-transformers compatible model.
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The distillation will happen during fitting of the topic model.
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NOTE: Only works if `embedding_model` is a string.
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distill_kwargs: Keyword arguments to pass to the distillation process
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of `model2vec.distill.distill`
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distill_vectorizer: A CountVectorizer used for creating a custom vocabulary
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based on the same documents used for topic modeling.
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NOTE: If "vocabulary" is in `distill_kwargs`, this will be ignored.
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Examples:
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To create a model, you can load in a string pointing to a
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model2vec model:
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```python
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from bertopic.backend import Model2VecBackend
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sentence_model = Model2VecBackend("minishlab/potion-base-8M")
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```
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or you can instantiate a model yourself:
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```python
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from bertopic.backend import Model2VecBackend
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from model2vec import StaticModel
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embedding_model = StaticModel.from_pretrained("minishlab/potion-base-8M")
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sentence_model = Model2VecBackend(embedding_model)
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```
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If you want to distill a sentence-transformers model with the vocabulary of the documents,
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run the following:
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```python
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from bertopic.backend import Model2VecBackend
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sentence_model = Model2VecBackend("sentence-transformers/all-MiniLM-L6-v2", distill=True)
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```
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"""
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def __init__(
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self,
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embedding_model: Union[str, StaticModel],
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distill: bool = False,
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distill_kwargs: dict = {},
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distill_vectorizer: str = None,
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):
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super().__init__()
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self.distill = distill
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self.distill_kwargs = distill_kwargs
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self.distill_vectorizer = distill_vectorizer
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self._has_distilled = False
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# When we distill, we need a string pointing to a sentence-transformer model
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if self.distill:
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self._check_model2vec_installation()
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if not self.distill_vectorizer:
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self.distill_vectorizer = CountVectorizer()
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if isinstance(embedding_model, str):
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self.embedding_model = embedding_model
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else:
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raise ValueError("Please pass a string pointing to a sentence-transformer model when distilling.")
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# If we don't distill, we can pass a model2vec model directly or load from a string
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elif isinstance(embedding_model, StaticModel):
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self.embedding_model = embedding_model
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elif isinstance(embedding_model, str):
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self.embedding_model = StaticModel.from_pretrained(embedding_model)
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else:
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raise ValueError(
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"Please select a correct Model2Vec model: \n"
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"`from model2vec import StaticModel` \n"
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"`model = StaticModel.from_pretrained('minishlab/potion-base-8M')`"
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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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# Distill the model
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if self.distill and not self._has_distilled:
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from model2vec.distill import distill
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# Distill with the vocabulary of the documents
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if not self.distill_kwargs.get("vocabulary"):
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X = self.distill_vectorizer.fit_transform(documents)
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word_counts = np.array(X.sum(axis=0)).flatten()
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words = self.distill_vectorizer.get_feature_names_out()
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vocabulary = [word for word, _ in sorted(zip(words, word_counts), key=lambda x: x[1], reverse=True)]
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self.distill_kwargs["vocabulary"] = vocabulary
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# Distill the model
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self.embedding_model = distill(self.embedding_model, **self.distill_kwargs)
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# Distillation should happen only once and not for every embed call
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# The distillation should only happen the first time on the entire vocabulary
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self._has_distilled = True
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# Embed the documents
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embeddings = self.embedding_model.encode(documents, show_progress_bar=verbose)
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return embeddings
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def _check_model2vec_installation(self):
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try:
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from model2vec.distill import distill # noqa: F401
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except ImportError:
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raise ImportError("To distill a model using model2vec, you need to run `pip install model2vec[distill]`")
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