BaseEmbedder
¶
The Base Embedder used for creating embedding models
Parameters:
Name | Type | Description | Default |
---|---|---|---|
embedding_model |
The main embedding model to be used for extracting document and word embedding |
None
|
|
word_embedding_model |
The embedding model used for extracting word
embeddings only. If this model is selected,
then the |
None
|
Source code in bertopic\backend\_base.py
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|
embed(documents, verbose=False)
¶
Embed a list of n documents/words into an n-dimensional matrix of embeddings
Parameters:
Name | Type | Description | Default |
---|---|---|---|
documents |
List[str]
|
A list of documents or words to be embedded |
required |
verbose |
bool
|
Controls the verbosity of the process |
False
|
Returns:
Type | Description |
---|---|
ndarray
|
Document/words embeddings with shape (n, m) with |
ndarray
|
that each have an embeddings size of |
Source code in bertopic\backend\_base.py
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|
embed_documents(document, verbose=False)
¶
Embed a list of n words into an n-dimensional matrix of embeddings
Parameters:
Name | Type | Description | Default |
---|---|---|---|
document |
List[str]
|
A list of documents to be embedded |
required |
verbose |
bool
|
Controls the verbosity of the process |
False
|
Returns:
Type | Description |
---|---|
ndarray
|
Document embeddings with shape (n, m) with |
ndarray
|
that each have an embeddings size of |
Source code in bertopic\backend\_base.py
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|
embed_words(words, verbose=False)
¶
Embed a list of n words into an n-dimensional matrix of embeddings
Parameters:
Name | Type | Description | Default |
---|---|---|---|
words |
List[str]
|
A list of words to be embedded |
required |
verbose |
bool
|
Controls the verbosity of the process |
False
|
Returns:
Type | Description |
---|---|
ndarray
|
Word embeddings with shape (n, m) with |
ndarray
|
that each have an embeddings size of |
Source code in bertopic\backend\_base.py
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|