representation_model: 'NoneType' object is not iterable

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Hey there!

First of all: thank you for developing BERTopic, it's neat! However, I am encountering an issue with `representation_model`, when trying to rename my cluster representations. Everything works fine as long as I am using just an embedding_model. However, as soon as I start using a representation_model I get the same error consistently.

Here is some sample code, inspired by this [documentation](https://maartengr.github.io/BERTopic/getting_started/representation/llm.html#transformers).

```
# Import the necessary libraries
from bertopic import BERTopic
import pandas as pd
from transformers import pipeline
from bertopic.representation import TextGeneration

# prompt = f"I have a topic described by the following keywords: [KEYWORDS]. Based on the previous keywords, what is this topic about?"

# Create your representation model
generator = pipeline('text2text-generation', model='google/flan-t5-base')
representation_model = TextGeneration(generator)

# 4. Get some sample data
data = pd.read_excel(testdata.xlsx')

# 5. Initialize BERTopic with the representation model
topic_model = BERTopic(
embedding_model= 'paraphrase-multilingual-mpnet-base-v2',
representation_model = representation_model # if commented, code works
)

# 6. Fit BERTopic to the sample texts
topics, _ = topic_model.fit_transform(data['text'])

# 6. Get the topic information
topic_info = topic_model.get_topic_info()

# 7. Print the topic information
print(topic_info)
```

The error I get is:

```
TypeError Traceback (most recent call last)
Cell In[3], line 26
20 topic_model = BERTopic(
21 embedding_model= 'paraphrase-multilingual-mpnet-base-v2',
22 representation_model = representation_model
23 )
25 # 6. Fit BERTopic to the sample texts
---> 26 topics, _ = topic_model.fit_transform(data['Absatz'])
28 # 6. Get the topic information
29 topic_info = topic_model.get_topic_info()

File ~/Code/NDR/.venv/lib/python3.11/site-packages/bertopic/_bertopic.py:433, in BERTopic.fit_transform(self, documents, embeddings, images, y)
430 self._save_representative_docs(custom_documents)
431 else:
432 # Extract topics by calculating c-TF-IDF
--> 433 self._extract_topics(documents, embeddings=embeddings, verbose=self.verbose)
435 # Reduce topics
436 if self.nr_topics:

File ~/Code/NDR/.venv/lib/python3.11/site-packages/bertopic/_bertopic.py:3637, in BERTopic._extract_topics(self, documents, embeddings, mappings, verbose)
3635 documents_per_topic = documents.groupby(['Topic'], as_index=False).agg({'Document': ' '.join})
3636 self.c_tf_idf_, words = self._c_tf_idf(documents_per_topic)
-> 3637 self.topic_representations_ = self._extract_words_per_topic(words, documents)
3638 self._create_topic_vectors(documents=documents, embeddings=embeddings, mappings=mappings)
3639 self.topic_labels_ = {key: f"{key}_" + "_".join([word[0] for word in values[:4]])
3640 for key, values in
3641 self.topic_representations_.items()}

File ~/Code/NDR/.venv/lib/python3.11/site-packages/bertopic/_bertopic.py:3922, in BERTopic._extract_words_per_topic(self, words, documents, c_tf_idf, calculate_aspects)
3920 topics = tuner.extract_topics(self, documents, c_tf_idf, topics)
3921 elif isinstance(self.representation_model, BaseRepresentation):
-> 3922 topics = self.representation_model.extract_topics(self, documents, c_tf_idf, topics)
3923 elif isinstance(self.representation_model, dict):
3924 if self.representation_model.get("Main"):

File ~/Code/NDR/.venv/lib/python3.11/site-packages/bertopic/representation/_textgeneration.py:147, in TextGeneration.extract_topics(self, topic_model, documents, c_tf_idf, topics)
143 updated_topics = {}
144 for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose):
145
146 # Prepare prompt
--> 147 truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs]
148 prompt = self._create_prompt(truncated_docs, topic, topics)
149 self.prompts_.append(prompt)

TypeError: 'NoneType' object is not iterable
```

Running it on an M1 Mac, if that helps. Any help appreciated. Also tried copying all code from the [best practise](https://maartengr.github.io/BERTopic/getting_started/best_practices/best_practices.html) and got the same error.

Best regards!
Alex Mühlhausen

Reproduce the example with BERTopic and TextGeneration, then inspect bertopic/_bertopic.py at _extract_words_per_topic and bertopic/representation/_textgeneration.py at extract_topics, especially the repr_docs_mappings iteration. Compare the failing representation-model path with the embedding-only path. Done means the sample fit_transform completes without the NoneType iteration error and topic information can be retrieved.

Written by the indexing model from the issue text.