TypeError:'NoneType' object is not subscriptable while calling topic_model.hierarchical_topics

[rashigupta8496]

Hi, I am trying zero-shot topic modelling with BERTopic. The following fit_transform ran successfully:

    topic_model = BERTopic(
        embedding_model="thenlper/gte-small", 
        min_topic_size=50,
        zeroshot_topic_list=zeroshot_topic_list,
        zeroshot_min_similarity=.85,
        representation_model=KeyBERTInspired()
    )
    topics, _ = topic_model.fit_transform(docs)

topic_model.get_topic_info()

While running hierarchical_topics = topic_model.hierarchical_topics(docs), getting the following error:

TypeError:'NoneType' object is not subscriptable
Open Traceback
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[87], line 1
----> 1 hierarchical_topics = topic_model.hierarchical_topics(docs)
File /mnt/xarfuse/uid-564347/e4f2f620-seed-nspid4026531836_cgpid15010019-ns-4026531841/bertopic/_bertopic.py:975, in BERTopic.hierarchical_topics(self, docs, linkage_function, distance_function)
    972     linkage_function = lambda x: sch.linkage(x, 'ward', optimal_ordering=True)
    974 # Calculate distance
--> 975 embeddings = self.c_tf_idf_[self._outliers:]
    976 X = distance_function(embeddings)
    977 X = validate_distance_matrix(X, embeddings.shape[0])
TypeError: 'NoneType' object is not subscriptable

zeroshot_topic_list contains 251 topics, docs are 15k. Similar code without zeroshot works fine. Please let me know if you have any insights, thanks!

[MaartenGr]

At the moment it is not possible to use hierarchical topic modeling with zero-shot topic modeling. Instead, you will have to run .update_topics in order to get the c-TF-IDF representations that are needed for hierarchical topic modeling.

There is currently some work being done on this but I can't be sure when it releases.

[rashigupta8496]

Thanks a lot for your response! I tried running the following update but it failed, "topics" are output of topic_model.fit_transform(docs):

topic_model.update_topics(docs, topics=topics)

Error:
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[98], line 1
----> 1 topic_model.update_topics(docs, topics=topics)
File /mnt/xarfuse/uid-564347/e4f2f620-seed-nspid4026531836_cgpid15010019-ns-4026531841/bertopic/_bertopic.py:1429, in BERTopic.update_topics(self, docs, images, topics, top_n_words, n_gram_range, vectorizer_model, ctfidf_model, representation_model)
   1427 self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
   1428 self.topic_representations_ = self._extract_words_per_topic(words, documents)
-> 1429 if set(topics) != self.topics_:
   1430     self._create_topic_vectors()
   1431 self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
   1432                       for key, values in
   1433                       self.topic_representations_.items()}
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Any insights how to run update for this case will be helpful, thanks.

[MaartenGr]

Thanks a lot for your response! I tried running the following update but it failed, "topics" are output of

Could you share the full code? It is not entirely clear to me which variables/attributes you used.

[rashigupta8496]

Thanks for your response. I am running the following basic version for now:

from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired
from data.docs import FetchDataToCluster
from data.seed import FetchSeedForCluster

docs = FetchDataToCluster()
topic_model = BERTopic(
    embedding_model="thenlper/gte-small", 
    min_topic_size=10,
    zeroshot_topic_list=FetchSeedForCluster(),
    zeroshot_min_similarity=.85,
    representation_model=KeyBERTInspired()
)
topics, _ = topic_model.fit_transform(docs)

topic_model.update_topics(docs, topics=topics)

hierarchical_topics = topic_model.hierarchical_topics(docs)

topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)

And getting the following error:

ValueError                                Traceback (most recent call last)
Cell In[10], line 1
----> 1 topic_model.update_topics(docs, topics=topics)
File /mnt/xarfuse/uid-564347/6641696b-seed-nspid4026531836_cgpid20335105-ns-4026531841/bertopic/_bertopic.py:1429, in BERTopic.update_topics(self, docs, images, topics, top_n_words, n_gram_range, vectorizer_model, ctfidf_model, representation_model)
   1427 self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
   1428 self.topic_representations_ = self._extract_words_per_topic(words, documents)
-> 1429 if set(topics) != self.topics_:
   1430     self._create_topic_vectors()
   1431 self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
   1432                       for key, values in
   1433                       self.topic_representations_.items()}
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

[MaartenGr]

Aside from the potential issue that you are facing, using the PR that was just pushed to the main branch you can now remove the .update_topics line and instead do it as follows:

from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired
from data.docs import FetchDataToCluster
from data.seed import FetchSeedForCluster

docs = FetchDataToCluster()
topic_model = BERTopic(
    embedding_model="thenlper/gte-small", 
    min_topic_size=10,
    zeroshot_topic_list=FetchSeedForCluster(),
    zeroshot_min_similarity=.85,
    representation_model=KeyBERTInspired()
)
topics, _ = topic_model.fit_transform(docs)

hierarchical_topics = topic_model.hierarchical_topics(docs, use_ctfidf=False)
topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics, use_ctfidf=False)

Having said that, it is strange that you get this issue since I cannot seem to reproduce it with this:

from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from bertopic.representation import KeyBERTInspired

dataset = load_dataset("CShorten/ML-ArXiv-Papers")["train"]

# Extract abstracts to train on and corresponding titles
abstracts = dataset["abstract"][:10_000]
titles = dataset["title"][:10_000]

# Pre-calculate embeddings
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = embedding_model.encode(abstracts, show_progress_bar=True)

# Train model
zeroshot_topic_list = ["Clustering", "Topic Modeling", "Large Language Models"]
topic_model = BERTopic(
    embedding_model=embedding_model,
    min_topic_size=10,
    zeroshot_topic_list=zeroshot_topic_list,
    zeroshot_min_similarity=.85,
    representation_model=KeyBERTInspired()
)
topics, _ = topic_model.fit_transform(abstracts, embeddings)

# Update topics
topic_model.update_topics(abstracts, topics=topics)

Note that I'm using BERTopic v0.16.2 for this piece of code.