Tensor sizes not matching

I'm trying to use this model in Google Colab with BERTopic for topic modeling and am unable to run the model. I'm using a subset of the Arxiv dataset with concatenated title and abstract for the data.

```python
from transformers import *
ASPIRE = pipeline("feature-extraction", model="allenai/aspire-sentence-embedder")

less_docs = arxiv_docs[:200]
topic_model = BERTopic(embedding_model=ASPIRE, language="english", nr_topics="auto", verbose=True )
topics, probs = topic_model.fit_transform(less_docs)
```

When the fit_transform() method is called the following error occurs:
RuntimeError Traceback (most recent call last)
[](https://localhost:8080/#) in
5
6 topic_model = BERTopic(embedding_model=ASPIRE, language="english", nr_topics="auto", verbose=True )
----> 7 topics, probs = topic_model.fit_transform(less_docs)

12 frames
[/usr/local/lib/python3.8/dist-packages/transformers/models/bert/modeling_bert.py](https://localhost:8080/#) in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
235 if self.position_embedding_type == "absolute":
236 position_embeddings = self.position_embeddings(position_ids)
--> 237 embeddings += position_embeddings
238 embeddings = self.LayerNorm(embeddings)
239 embeddings = self.dropout(embeddings)

RuntimeError: The size of tensor a (541) must match the size of tensor b (512) at non-singleton dimension 1