Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_word_doc.py

@@ -0,0 +1,43 @@
+import numpy as np
+from typing import List
+from bertopic.backend._base import BaseEmbedder
+from bertopic.backend._utils import select_backend
+
+
+class WordDocEmbedder(BaseEmbedder):
+    """Combine a document- and word-level embedder."""
+
+    def __init__(self, embedding_model, word_embedding_model):
+        super().__init__()
+
+        self.embedding_model = select_backend(embedding_model)
+        self.word_embedding_model = select_backend(word_embedding_model)
+
+    def embed_words(self, words: List[str], verbose: bool = False) -> np.ndarray:
+        """Embed a list of n words into an n-dimensional
+        matrix of embeddings.
+
+        Arguments:
+            words: A list of words to be embedded
+            verbose: Controls the verbosity of the process
+
+        Returns:
+            Word embeddings with shape (n, m) with `n` words
+            that each have an embeddings size of `m`
+
+        """
+        return self.word_embedding_model.embed(words, verbose)
+
+    def embed_documents(self, document: List[str], verbose: bool = False) -> np.ndarray:
+        """Embed a list of n words into an n-dimensional
+        matrix of embeddings.
+
+        Arguments:
+            document: A list of documents to be embedded
+            verbose: Controls the verbosity of the process
+
+        Returns:
+            Document embeddings with shape (n, m) with `n` documents
+            that each have an embeddings size of `m`
+        """
+        return self.embedding_model.embed(document, verbose)