Add BERTopic.

LLMTopicDetection_BERTopic/bertopic/backend/_spacy.py

@@ -0,0 +1,94 @@
+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)