flan-t5 for

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Hi @MaartenGr,

I hope you are doing well. I am getting the following error when using the flan-t5 model for topic representation. Any solution for this? Thanks

```
from transformers import pipeline
from bertopic.representation import TextGeneration

prompt = "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)

topic_model_t5 = BERTopic(representation_model=representation_model)
```

Error:

----> 2 topics, probs = topic_model_t5.fit_transform(docs)
3 print(topic_model_t5.get_topic_info())

[/usr/local/lib/python3.10/dist-packages/bertopic/representation/_textgeneration.py](https://localhost:8080/#) in extract_topics(self, topic_model, documents, c_tf_idf, topics)
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

Start with the traceback entry point in bertopic/representation/_textgeneration.py, especially extract_topics around line 147, and reproduce the provided flan-t5 example. Trace why truncate_document receives or produces None, then verify that BERTopic.fit_transform completes without the reported TypeError.

Written by the indexing model from the issue text.