Top2Vec universal sentence encoder creating a float somewhere?

[Aisling-Kinsella]

I'm training a top2vec model using universal sentence encoder and I'm getting Type Error:
'numpy.float64' object cannot be interpreted as an integer

All my input text is string type, I've quadruple checked and this has worked before so I'm at a loss as to what is causing this error to be thrown or where it's initiating from. Is it coming from hdbscan?

TypeError                                 Traceback (most recent call last)
Cell In[12], line 2
      1 # Train the top2vec model
----> 2 model = Top2Vec(df.Q10.values, embedding_model='universal-sentence-encoder') #,  embedding_model='universal-sentence-encoder'

File ~/opt/anaconda3/envs/top2vec/lib/python3.10/site-packages/top2vec/Top2Vec.py:666, in Top2Vec.__init__(self, documents, min_count, topic_merge_delta, ngram_vocab, ngram_vocab_args, embedding_model, embedding_model_path, embedding_batch_size, split_documents, document_chunker, chunk_length, max_num_chunks, chunk_overlap_ratio, chunk_len_coverage_ratio, sentencizer, speed, use_corpus_file, document_ids, keep_documents, workers, tokenizer, use_embedding_model_tokenizer, umap_args, hdbscan_args, verbose)
    663 else:
    664     raise ValueError(f"{embedding_model} is an invalid embedding model.")
--> 666 self.compute_topics(umap_args=umap_args, hdbscan_args=hdbscan_args, topic_merge_delta=topic_merge_delta)
    668 # initialize document indexing variables
    669 self.document_index = None

File ~/opt/anaconda3/envs/top2vec/lib/python3.10/site-packages/top2vec/Top2Vec.py:1266, in Top2Vec.compute_topics(self, umap_args, hdbscan_args, topic_merge_delta)
   1261 if hdbscan_args is None:
   1262     hdbscan_args = {'min_cluster_size': 15,
   1263                     'metric': 'euclidean',
   1264                     'cluster_selection_method': 'eom'}
-> 1266 cluster = hdbscan.HDBSCAN(**hdbscan_args).fit(umap_model.embedding_)
   1268 # calculate topic vectors from dense areas of documents
   1269 logger.info('Finding topics')

File ~/opt/anaconda3/envs/top2vec/lib/python3.10/site-packages/hdbscan/hdbscan_.py:1205, in HDBSCAN.fit(self, X, y)
   1195 kwargs.pop("prediction_data", None)
   1196 kwargs.update(self._metric_kwargs)
   1198 (
   1199     self.labels_,
   1200     self.probabilities_,
   1201     self.cluster_persistence_,
   1202     self._condensed_tree,
   1203     self._single_linkage_tree,
   1204     self._min_spanning_tree,
-> 1205 ) = hdbscan(clean_data, **kwargs)
   1207 if self.metric != "precomputed" and not self._all_finite:
   1208     # remap indices to align with original data in the case of non-finite entries.
   1209     self._condensed_tree = remap_condensed_tree(
   1210         self._condensed_tree, internal_to_raw, outliers
   1211     )

File ~/opt/anaconda3/envs/top2vec/lib/python3.10/site-packages/hdbscan/hdbscan_.py:884, in hdbscan(X, min_cluster_size, min_samples, alpha, cluster_selection_epsilon, max_cluster_size, metric, p, leaf_size, algorithm, memory, approx_min_span_tree, gen_min_span_tree, core_dist_n_jobs, cluster_selection_method, allow_single_cluster, match_reference_implementation, **kwargs)
    867         else:
    868             (single_linkage_tree, result_min_span_tree) = memory.cache(
    869                 _hdbscan_boruvka_balltree
    870             )(
   (...)
    880                 **kwargs
    881             )
    883 return (
--> 884     _tree_to_labels(
    885         X,
    886         single_linkage_tree,
    887         min_cluster_size,
    888         cluster_selection_method,
    889         allow_single_cluster,
    890         match_reference_implementation,
    891         cluster_selection_epsilon,
    892         max_cluster_size,
    893     )
    894     + (result_min_span_tree,)
    895 )

File ~/opt/anaconda3/envs/top2vec/lib/python3.10/site-packages/hdbscan/hdbscan_.py:80, in _tree_to_labels(X, single_linkage_tree, min_cluster_size, cluster_selection_method, allow_single_cluster, match_reference_implementation, cluster_selection_epsilon, max_cluster_size)
     78 condensed_tree = condense_tree(single_linkage_tree, min_cluster_size)
     79 stability_dict = compute_stability(condensed_tree)
---> 80 labels, probabilities, stabilities = get_clusters(
     81     condensed_tree,
     82     stability_dict,
     83     cluster_selection_method,
     84     allow_single_cluster,
     85     match_reference_implementation,
     86     cluster_selection_epsilon,
     87     max_cluster_size,
     88 )
     90 return (labels, probabilities, stabilities, condensed_tree, single_linkage_tree)

File hdbscan/_hdbscan_tree.pyx:659, in hdbscan._hdbscan_tree.get_clusters()

File hdbscan/_hdbscan_tree.pyx:733, in hdbscan._hdbscan_tree.get_clusters()

TypeError: 'numpy.float64' object cannot be interpreted as an integer