KeyBERTInspired¶
Source code in bertopic\representation\_keybert.py
class KeyBERTInspired(BaseRepresentation):
def __init__(
self,
top_n_words: int = 10,
nr_repr_docs: int = 5,
nr_samples: int = 500,
nr_candidate_words: int = 100,
random_state: int = 42,
):
"""Use a KeyBERT-like model to fine-tune the topic representations.
The algorithm follows KeyBERT but does some optimization in
order to speed up inference.
The steps are as follows. First, we extract the top n representative
documents per topic. To extract the representative documents, we
randomly sample a number of candidate documents per cluster
which is controlled by the `nr_samples` parameter. Then,
the top n representative documents are extracted by calculating
the c-TF-IDF representation for the candidate documents and finding,
through cosine similarity, which are closest to the topic c-TF-IDF representation.
Next, the top n words per topic are extracted based on their
c-TF-IDF representation, which is controlled by the `nr_repr_docs`
parameter.
Then, we extract the embeddings for words and representative documents
and create topic embeddings by averaging the representative documents.
Finally, the most similar words to each topic are extracted by
calculating the cosine similarity between word and topic embeddings.
Arguments:
top_n_words: The top n words to extract per topic.
nr_repr_docs: The number of representative documents to extract per cluster.
nr_samples: The number of candidate documents to extract per cluster.
nr_candidate_words: The number of candidate words per cluster.
random_state: The random state for randomly sampling candidate documents.
Usage:
```python
from bertopic.representation import KeyBERTInspired
from bertopic import BERTopic
# Create your representation model
representation_model = KeyBERTInspired()
# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)
```
"""
self.top_n_words = top_n_words
self.nr_repr_docs = nr_repr_docs
self.nr_samples = nr_samples
self.nr_candidate_words = nr_candidate_words
self.random_state = random_state
def extract_topics(
self,
topic_model,
documents: pd.DataFrame,
c_tf_idf: csr_matrix,
topics: Mapping[str, List[Tuple[str, float]]],
) -> Mapping[str, List[Tuple[str, float]]]:
"""Extract topics.
Arguments:
topic_model: A BERTopic model
documents: All input documents
c_tf_idf: The topic c-TF-IDF representation
topics: The candidate topics as calculated with c-TF-IDF
Returns:
updated_topics: Updated topic representations
"""
# We extract the top n representative documents per class
_, representative_docs, repr_doc_indices, _ = topic_model._extract_representative_docs(
c_tf_idf, documents, topics, self.nr_samples, self.nr_repr_docs
)
# We extract the top n words per class
topics = self._extract_candidate_words(topic_model, c_tf_idf, topics)
# We calculate the similarity between word and document embeddings and create
# topic embeddings from the representative document embeddings
sim_matrix, words = self._extract_embeddings(topic_model, topics, representative_docs, repr_doc_indices)
# Find the best matching words based on the similarity matrix for each topic
updated_topics = self._extract_top_words(words, topics, sim_matrix)
return updated_topics
def _extract_candidate_words(
self,
topic_model,
c_tf_idf: csr_matrix,
topics: Mapping[str, List[Tuple[str, float]]],
) -> Mapping[str, List[Tuple[str, float]]]:
"""For each topic, extract candidate words based on the c-TF-IDF
representation.
Arguments:
topic_model: A BERTopic model
c_tf_idf: The topic c-TF-IDF representation
topics: The top words per topic
Returns:
topics: The `self.top_n_words` per topic
"""
labels = [int(label) for label in sorted(list(topics.keys()))]
# Scikit-Learn Deprecation: get_feature_names is deprecated in 1.0
# and will be removed in 1.2. Please use get_feature_names_out instead.
if version.parse(sklearn_version) >= version.parse("1.0.0"):
words = topic_model.vectorizer_model.get_feature_names_out()
else:
words = topic_model.vectorizer_model.get_feature_names()
indices = topic_model._top_n_idx_sparse(c_tf_idf, self.nr_candidate_words)
scores = topic_model._top_n_values_sparse(c_tf_idf, indices)
sorted_indices = np.argsort(scores, 1)
indices = np.take_along_axis(indices, sorted_indices, axis=1)
scores = np.take_along_axis(scores, sorted_indices, axis=1)
# Get top 30 words per topic based on c-TF-IDF score
topics = {
label: [
(words[word_index], score) if word_index is not None and score > 0 else ("", 0.00001)
for word_index, score in zip(indices[index][::-1], scores[index][::-1])
]
for index, label in enumerate(labels)
}
topics = {label: list(zip(*values[: self.nr_candidate_words]))[0] for label, values in topics.items()}
return topics
def _extract_embeddings(
self,
topic_model,
topics: Mapping[str, List[Tuple[str, float]]],
representative_docs: List[str],
repr_doc_indices: List[List[int]],
) -> Union[np.ndarray, List[str]]:
"""Extract the representative document embeddings and create topic embeddings.
Then extract word embeddings and calculate the cosine similarity between topic
embeddings and the word embeddings. Topic embeddings are the average of
representative document embeddings.
Arguments:
topic_model: A BERTopic model
topics: The top words per topic
representative_docs: A flat list of representative documents
repr_doc_indices: The indices of representative documents
that belong to each topic
Returns:
sim: The similarity matrix between word and topic embeddings
vocab: The complete vocabulary of input documents
"""
# Calculate representative docs embeddings and create topic embeddings
repr_embeddings = topic_model._extract_embeddings(representative_docs, method="document", verbose=False)
topic_embeddings = [np.mean(repr_embeddings[i[0] : i[-1] + 1], axis=0) for i in repr_doc_indices]
# Calculate word embeddings and extract best matching with updated topic_embeddings
vocab = list(set([word for words in topics.values() for word in words]))
word_embeddings = topic_model._extract_embeddings(vocab, method="document", verbose=False)
sim = cosine_similarity(topic_embeddings, word_embeddings)
return sim, vocab
def _extract_top_words(
self,
vocab: List[str],
topics: Mapping[str, List[Tuple[str, float]]],
sim: np.ndarray,
) -> Mapping[str, List[Tuple[str, float]]]:
"""Extract the top n words per topic based on the
similarity matrix between topics and words.
Arguments:
vocab: The complete vocabulary of input documents
labels: All topic labels
topics: The top words per topic
sim: The similarity matrix between word and topic embeddings
Returns:
updated_topics: The updated topic representations
"""
labels = [int(label) for label in sorted(list(topics.keys()))]
updated_topics = {}
for i, topic in enumerate(labels):
indices = [vocab.index(word) for word in topics[topic]]
values = sim[:, indices][i]
word_indices = [indices[index] for index in np.argsort(values)[-self.top_n_words :]]
updated_topics[topic] = [
(vocab[index], val) for val, index in zip(np.sort(values)[-self.top_n_words :], word_indices)
][::-1]
return updated_topics
__init__(self, top_n_words=10, nr_repr_docs=5, nr_samples=500, nr_candidate_words=100, random_state=42)
special
¶
Use a KeyBERT-like model to fine-tune the topic representations.
The algorithm follows KeyBERT but does some optimization in order to speed up inference.
The steps are as follows. First, we extract the top n representative
documents per topic. To extract the representative documents, we
randomly sample a number of candidate documents per cluster
which is controlled by the nr_samples parameter. Then,
the top n representative documents are extracted by calculating
the c-TF-IDF representation for the candidate documents and finding,
through cosine similarity, which are closest to the topic c-TF-IDF representation.
Next, the top n words per topic are extracted based on their
c-TF-IDF representation, which is controlled by the nr_repr_docs
parameter.
Then, we extract the embeddings for words and representative documents and create topic embeddings by averaging the representative documents. Finally, the most similar words to each topic are extracted by calculating the cosine similarity between word and topic embeddings.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
top_n_words |
int |
The top n words to extract per topic. |
10 |
nr_repr_docs |
int |
The number of representative documents to extract per cluster. |
5 |
nr_samples |
int |
The number of candidate documents to extract per cluster. |
500 |
nr_candidate_words |
int |
The number of candidate words per cluster. |
100 |
random_state |
int |
The random state for randomly sampling candidate documents. |
42 |
Usage:
from bertopic.representation import KeyBERTInspired
from bertopic import BERTopic
# Create your representation model
representation_model = KeyBERTInspired()
# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)
Source code in bertopic\representation\_keybert.py
def __init__(
self,
top_n_words: int = 10,
nr_repr_docs: int = 5,
nr_samples: int = 500,
nr_candidate_words: int = 100,
random_state: int = 42,
):
"""Use a KeyBERT-like model to fine-tune the topic representations.
The algorithm follows KeyBERT but does some optimization in
order to speed up inference.
The steps are as follows. First, we extract the top n representative
documents per topic. To extract the representative documents, we
randomly sample a number of candidate documents per cluster
which is controlled by the `nr_samples` parameter. Then,
the top n representative documents are extracted by calculating
the c-TF-IDF representation for the candidate documents and finding,
through cosine similarity, which are closest to the topic c-TF-IDF representation.
Next, the top n words per topic are extracted based on their
c-TF-IDF representation, which is controlled by the `nr_repr_docs`
parameter.
Then, we extract the embeddings for words and representative documents
and create topic embeddings by averaging the representative documents.
Finally, the most similar words to each topic are extracted by
calculating the cosine similarity between word and topic embeddings.
Arguments:
top_n_words: The top n words to extract per topic.
nr_repr_docs: The number of representative documents to extract per cluster.
nr_samples: The number of candidate documents to extract per cluster.
nr_candidate_words: The number of candidate words per cluster.
random_state: The random state for randomly sampling candidate documents.
Usage:
```python
from bertopic.representation import KeyBERTInspired
from bertopic import BERTopic
# Create your representation model
representation_model = KeyBERTInspired()
# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)
```
"""
self.top_n_words = top_n_words
self.nr_repr_docs = nr_repr_docs
self.nr_samples = nr_samples
self.nr_candidate_words = nr_candidate_words
self.random_state = random_state
extract_topics(self, topic_model, documents, c_tf_idf, topics)
¶
Extract topics.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
topic_model |
A BERTopic model |
required | |
documents |
DataFrame |
All input documents |
required |
c_tf_idf |
csr_matrix |
The topic c-TF-IDF representation |
required |
topics |
Mapping[str, List[Tuple[str, float]]] |
The candidate topics as calculated with c-TF-IDF |
required |
Returns:
| Type | Description |
|---|---|
updated_topics |
Updated topic representations |
Source code in bertopic\representation\_keybert.py
def extract_topics(
self,
topic_model,
documents: pd.DataFrame,
c_tf_idf: csr_matrix,
topics: Mapping[str, List[Tuple[str, float]]],
) -> Mapping[str, List[Tuple[str, float]]]:
"""Extract topics.
Arguments:
topic_model: A BERTopic model
documents: All input documents
c_tf_idf: The topic c-TF-IDF representation
topics: The candidate topics as calculated with c-TF-IDF
Returns:
updated_topics: Updated topic representations
"""
# We extract the top n representative documents per class
_, representative_docs, repr_doc_indices, _ = topic_model._extract_representative_docs(
c_tf_idf, documents, topics, self.nr_samples, self.nr_repr_docs
)
# We extract the top n words per class
topics = self._extract_candidate_words(topic_model, c_tf_idf, topics)
# We calculate the similarity between word and document embeddings and create
# topic embeddings from the representative document embeddings
sim_matrix, words = self._extract_embeddings(topic_model, topics, representative_docs, repr_doc_indices)
# Find the best matching words based on the similarity matrix for each topic
updated_topics = self._extract_top_words(words, topics, sim_matrix)
return updated_topics