Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_sentencetransformers.py

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+import numpy as np
+from typing import List, Union
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.models import StaticEmbedding
+
+from bertopic.backend import BaseEmbedder
+
+
+class SentenceTransformerBackend(BaseEmbedder):
+    """Sentence-transformers embedding model.
+
+    The sentence-transformers embedding model used for generating document and
+    word embeddings.
+
+    Arguments:
+        embedding_model: A sentence-transformers embedding model
+        model2vec: Indicates whether `embedding_model` is a model2vec model.
+                   NOTE: Only works if `embedding_model` is a string.
+                   Otherwise, you can pass the model2vec model directly to `embedding_model`.
+
+    Examples:
+    To create a model, you can load in a string pointing to a
+    sentence-transformers model:
+
+    ```python
+    from bertopic.backend import SentenceTransformerBackend
+
+    sentence_model = SentenceTransformerBackend("all-MiniLM-L6-v2")
+    ```
+
+    or  you can instantiate a model yourself:
+
+    ```python
+    from bertopic.backend import SentenceTransformerBackend
+    from sentence_transformers import SentenceTransformer
+
+    embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+    sentence_model = SentenceTransformerBackend(embedding_model)
+    ```
+
+    If you want to use a model2vec model without having to install model2vec,
+    you can pass the model2vec model as a string:
+
+    ```python
+    from bertopic.backend import SentenceTransformerBackend
+    from sentence_transformers import SentenceTransformer
+
+    embedding_model = SentenceTransformer("minishlab/potion-base-8M", model2vec=True)
+    sentence_model = SentenceTransformerBackend(embedding_model)
+    ```
+    """
+
+    def __init__(self, embedding_model: Union[str, SentenceTransformer], model2vec: bool = False):
+        super().__init__()
+
+        self._hf_model = None
+        if model2vec and isinstance(embedding_model, str):
+            static_embedding = StaticEmbedding.from_model2vec(embedding_model)
+            self.embedding_model = SentenceTransformer(modules=[static_embedding])
+        elif isinstance(embedding_model, SentenceTransformer):
+            self.embedding_model = embedding_model
+        elif isinstance(embedding_model, str):
+            self.embedding_model = SentenceTransformer(embedding_model)
+            self._hf_model = embedding_model
+        else:
+            raise ValueError(
+                "Please select a correct SentenceTransformers model: \n"
+                "`from sentence_transformers import SentenceTransformer` \n"
+                "`model = SentenceTransformer('all-MiniLM-L6-v2')`"
+            )
+
+    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`
+        """
+        embeddings = self.embedding_model.encode(documents, show_progress_bar=verbose)
+        return embeddings