Introducing BERTopic Integration with the Hugging Face Hub

We are thrilled to announce a significant update to the BERTopic Python library, expanding its capabilities and further streamlining the workflow for topic modelling enthusiasts and practitioners. BERTopic now supports pushing and pulling trained topic models directly to and from the Hugging Face Hub. This new integration opens up exciting possibilities for leveraging the power of BERTopic in production use cases with ease.

What is Topic Modelling?

Topic modelling is a method that can help uncover hidden themes or "topics" within a group of documents. By analyzing the words in the documents, we can find patterns and connections that reveal these underlying topics. For example, a document about machine learning is more likely to use words like "gradient" and "embedding" compared to a document about baking bread.

Each document usually covers multiple topics in different proportions. By examining the word statistics, we can identify clusters of related words that represent these topics. This allows us to analyze a set of documents and determine the topics they discuss, as well as the balance of topics within each document. More recently, new approaches to topic modelling have moved beyond using words to using more rich representations such as those offered through Transformer based models.

What is BERTopic?

BERTopic is a state-of-the-art Python library that simplifies the topic modelling process using various embedding techniques and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.

An overview of the BERTopic library

Whilst BERTopic is easy to get started with, it supports a range of advanced approaches to topic modelling including guided, supervised, semi-supervised and manual topic modelling. More recently BERTopic has added support for multi-modal topic models. BERTopic also have a rich set of tools for producing visualizations.

BERTopic provides a powerful tool for users to uncover significant topics within text collections, thereby gaining valuable insights. With BERTopic, users can analyze customer reviews, explore research papers, or categorize news articles with ease, making it an essential tool for anyone looking to extract meaningful information from their text data.

BERTopic Model Management with the Hugging Face Hub

With the latest integration, BERTopic users can seamlessly push and pull their trained topic models to and from the Hugging Face Hub. This integration marks a significant milestone in simplifying the deployment and management of BERTopic models across different environments.

The process of training and pushing a BERTopic model to the Hub can be done in a few lines

from bertopic import BERTopic

topic_model = BERTopic("english")
topics, probs = topic_model.fit_transform(docs)
topic_model.push_to_hf_hub('davanstrien/transformers_issues_topics')

You can then load this model in two lines and use it to predict against new data.

from bertopic import BERTopic
topic_model = BERTopic.load("davanstrien/transformers_issues_topics")

By leveraging the power of the Hugging Face Hub, BERTopic users can effortlessly share, version, and collaborate on their topic models. The Hub acts as a central repository, allowing users to store and organize their models, making it easier to deploy models in production, share them with colleagues, or even showcase them to the broader NLP community.

You can use the libraries filter on the hub to find BERTopic models.

Once you have found a BERTopic model you are interested in you can use the Hub inference widget to try out the model and see if it might be a good fit for your use case.

Once you have a trained topic model, you can push it to the Hugging Face Hub in one line. Pushing your model to the Hub will automatically create an initial model card for your model, including an overview of the topics created. Below you can see an example of the topics resulting from a model trained on ArXiv data.

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	language - models - model - data - based	20	-1_language_models_model_data
0	dialogue - dialog - response - responses - intent	14247	0_dialogue_dialog_response_responses
1	speech - asr - speech recognition - recognition - end	1833	1_speech_asr_speech recognition_recognition
2	tuning - tasks - prompt - models - language	1369	2_tuning_tasks_prompt_models
3	summarization - summaries - summary - abstractive - document	1109	3_summarization_summaries_summary_abstractive
4	question - answer - qa - answering - question answering	893	4_question_answer_qa_answering
5	sentiment - sentiment analysis - aspect - analysis - opinion	837	5_sentiment_sentiment analysis_aspect_analysis
6	clinical - medical - biomedical - notes - patient	691	6_clinical_medical_biomedical_notes
7	translation - nmt - machine translation - neural machine - neural machine translation	586	7_translation_nmt_machine translation_neural machine
8	generation - text generation - text - language generation - nlg	558	8_generation_text generation_text_language generation
9	hate - hate speech - offensive - speech - detection	484	9_hate_hate speech_offensive_speech
10	news - fake - fake news - stance - fact	455	10_news_fake_fake news_stance
11	relation - relation extraction - extraction - relations - entity	450	11_relation_relation extraction_extraction_relations
12	ner - named - named entity - entity - named entity recognition	376	12_ner_named_named entity_entity
13	parsing - parser - dependency - treebank - parsers	370	13_parsing_parser_dependency_treebank
14	event - temporal - events - event extraction - extraction	314	14_event_temporal_events_event extraction
15	emotion - emotions - multimodal - emotion recognition - emotional	300	15_emotion_emotions_multimodal_emotion recognition
16	word - embeddings - word embeddings - embedding - words	292	16_word_embeddings_word embeddings_embedding
17	explanations - explanation - rationales - rationale - interpretability	212	17_explanations_explanation_rationales_rationale
18	morphological - arabic - morphology - languages - inflection	204	18_morphological_arabic_morphology_languages
19	topic - topics - topic models - lda - topic modeling	200	19_topic_topics_topic models_lda
20	bias - gender - biases - gender bias - debiasing	195	20_bias_gender_biases_gender bias
21	law - frequency - zipf - words - length	185	21_law_frequency_zipf_words
22	legal - court - law - legal domain - case	182	22_legal_court_law_legal domain
23	adversarial - attacks - attack - adversarial examples - robustness	181	23_adversarial_attacks_attack_adversarial examples
24	commonsense - commonsense knowledge - reasoning - knowledge - commonsense reasoning	180	24_commonsense_commonsense knowledge_reasoning_knowledge
25	quantum - semantics - calculus - compositional - meaning	171	25_quantum_semantics_calculus_compositional
26	correction - error - error correction - grammatical - grammatical error	161	26_correction_error_error correction_grammatical
27	argument - arguments - argumentation - argumentative - mining	160	27_argument_arguments_argumentation_argumentative
28	sarcasm - humor - sarcastic - detection - humorous	157	28_sarcasm_humor_sarcastic_detection
29	coreference - resolution - coreference resolution - mentions - mention	156	29_coreference_resolution_coreference resolution_mentions
30	sense - word sense - wsd - word - disambiguation	153	30_sense_word sense_wsd_word
31	knowledge - knowledge graph - graph - link prediction - entities	149	31_knowledge_knowledge graph_graph_link prediction
32	parsing - semantic parsing - amr - semantic - parser	146	32_parsing_semantic parsing_amr_semantic
33	cross lingual - lingual - cross - transfer - languages	146	33_cross lingual_lingual_cross_transfer
34	mt - translation - qe - quality - machine translation	139	34_mt_translation_qe_quality
35	sql - text sql - queries - spider - schema	138	35_sql_text sql_queries_spider
36	classification - text classification - label - text - labels	136	36_classification_text classification_label_text
37	style - style transfer - transfer - text style - text style transfer	136	37_style_style transfer_transfer_text style
38	question - question generation - questions - answer - generation	129	38_question_question generation_questions_answer
39	authorship - authorship attribution - attribution - author - authors	127	39_authorship_authorship attribution_attribution_author
40	sentence - sentence embeddings - similarity - sts - sentence embedding	123	40_sentence_sentence embeddings_similarity_sts
41	code - identification - switching - cs - code switching	121	41_code_identification_switching_cs
42	story - stories - story generation - generation - storytelling	118	42_story_stories_story generation_generation
43	discourse - discourse relation - discourse relations - rst - discourse parsing	117	43_discourse_discourse relation_discourse relations_rst
44	code - programming - source code - code generation - programming languages	117	44_code_programming_source code_code generation
45	paraphrase - paraphrases - paraphrase generation - paraphrasing - generation	114	45_paraphrase_paraphrases_paraphrase generation_paraphrasing
46	agent - games - environment - instructions - agents	111	46_agent_games_environment_instructions
47	covid - covid 19 - 19 - tweets - pandemic	108	47_covid_covid 19_19_tweets
48	linking - entity linking - entity - el - entities	107	48_linking_entity linking_entity_el
49	poetry - poems - lyrics - poem - music	103	49_poetry_poems_lyrics_poem
50	image - captioning - captions - visual - caption	100	50_image_captioning_captions_visual
51	nli - entailment - inference - natural language inference - language inference	96	51_nli_entailment_inference_natural language inference
52	keyphrase - keyphrases - extraction - document - phrases	95	52_keyphrase_keyphrases_extraction_document
53	simplification - text simplification - ts - sentence - simplified	95	53_simplification_text simplification_ts_sentence
54	empathetic - emotion - emotional - empathy - emotions	95	54_empathetic_emotion_emotional_empathy
55	depression - mental - health - mental health - social media	93	55_depression_mental_health_mental health
56	segmentation - word segmentation - chinese - chinese word segmentation - chinese word	93	56_segmentation_word segmentation_chinese_chinese word segmentation
57	citation - scientific - papers - citations - scholarly	85	57_citation_scientific_papers_citations
58	agreement - syntactic - verb - grammatical - subject verb	85	58_agreement_syntactic_verb_grammatical
59	metaphor - literal - figurative - metaphors - idiomatic	83	59_metaphor_literal_figurative_metaphors
60	srl - semantic role - role labeling - semantic role labeling - role	82	60_srl_semantic role_role labeling_semantic role labeling
61	privacy - private - federated - privacy preserving - federated learning	82	61_privacy_private_federated_privacy preserving
62	change - semantic change - time - semantic - lexical semantic	82	62_change_semantic change_time_semantic
63	bilingual - lingual - cross lingual - cross - embeddings	80	63_bilingual_lingual_cross lingual_cross
64	political - media - news - bias - articles	77	64_political_media_news_bias
65	medical - qa - question - questions - clinical	75	65_medical_qa_question_questions
66	math - mathematical - math word - word problems - problems	73	66_math_mathematical_math word_word problems
67	financial - stock - market - price - news	69	67_financial_stock_market_price
68	table - tables - tabular - reasoning - qa	69	68_table_tables_tabular_reasoning
69	readability - complexity - assessment - features - reading	65	69_readability_complexity_assessment_features
70	layout - document - documents - document understanding - extraction	64	70_layout_document_documents_document understanding
71	brain - cognitive - reading - syntactic - language	62	71_brain_cognitive_reading_syntactic
72	sign - gloss - language - signed - language translation	61	72_sign_gloss_language_signed
73	vqa - visual - visual question - visual question answering - question	59	73_vqa_visual_visual question_visual question answering
74	biased - biases - spurious - nlp - debiasing	57	74_biased_biases_spurious_nlp
75	visual - dialogue - multimodal - image - dialog	55	75_visual_dialogue_multimodal_image
76	translation - machine translation - machine - smt - statistical	54	76_translation_machine translation_machine_smt
77	multimodal - visual - image - translation - machine translation	52	77_multimodal_visual_image_translation
78	geographic - location - geolocation - geo - locations	51	78_geographic_location_geolocation_geo
79	reasoning - prompting - llms - chain thought - chain	48	79_reasoning_prompting_llms_chain thought
80	essay - scoring - aes - essay scoring - essays	45	80_essay_scoring_aes_essay scoring
81	crisis - disaster - traffic - tweets - disasters	45	81_crisis_disaster_traffic_tweets
82	graph - text classification - text - gcn - classification	44	82_graph_text classification_text_gcn
83	annotation - tools - linguistic - resources - xml	43	83_annotation_tools_linguistic_resources
84	entity alignment - alignment - kgs - entity - ea	43	84_entity alignment_alignment_kgs_entity
85	personality - traits - personality traits - evaluative - text	42	85_personality_traits_personality traits_evaluative
86	ad - alzheimer - alzheimer disease - disease - speech	40	86_ad_alzheimer_alzheimer disease_disease
87	taxonomy - hypernymy - taxonomies - hypernym - hypernyms	39	87_taxonomy_hypernymy_taxonomies_hypernym
88	active learning - active - al - learning - uncertainty	37	88_active learning_active_al_learning
89	reviews - summaries - summarization - review - opinion	36	89_reviews_summaries_summarization_review
90	emoji - emojis - sentiment - message - anonymous	35	90_emoji_emojis_sentiment_message
91	table - table text - tables - table text generation - text generation	35	91_table_table text_tables_table text generation
92	domain - domain adaptation - adaptation - domains - source	35	92_domain_domain adaptation_adaptation_domains
93	alignment - word alignment - parallel - pairs - alignments	34	93_alignment_word alignment_parallel_pairs
94	indo - languages - indo european - names - family	34	94_indo_languages_indo european_names
95	patent - claim - claim generation - chemical - technical	32	95_patent_claim_claim generation_chemical
96	agents - emergent - communication - referential - games	32	96_agents_emergent_communication_referential
97	graph - amr - graph text - graphs - text generation	31	97_graph_amr_graph text_graphs
98	moral - ethical - norms - values - social	29	98_moral_ethical_norms_values
99	acronym - acronyms - abbreviations - abbreviation - disambiguation	27	99_acronym_acronyms_abbreviations_abbreviation
100	typing - entity typing - entity - type - types	27	100_typing_entity typing_entity_type
101	coherence - discourse - discourse coherence - coherence modeling - text	26	101_coherence_discourse_discourse coherence_coherence modeling
102	pos - taggers - tagging - tagger - pos tagging	25	102_pos_taggers_tagging_tagger
103	drug - social - social media - media - health	25	103_drug_social_social media_media
104	gender - translation - bias - gender bias - mt	24	104_gender_translation_bias_gender bias
105	job - resume - skills - skill - soft	21	105_job_resume_skills_skill

Due to the improved saving procedure, training on large datasets generates small model sizes. In the example below, a BERTopic model was trained on 100,000 documents, resulting in a ~50MB model keeping all of the original’s model functionality. For inference, the model can be further reduced to only ~3MB!

The benefits of this integration are particularly notable for production use cases. Users can now effortlessly deploy BERTopic models into their existing applications or systems, ensuring seamless integration within their data pipelines. This streamlined workflow enables faster iteration and efficient model updates and ensures consistency across different environments.

safetensors: Ensuring Secure Model Management

In addition to the Hugging Face Hub integration, BERTopic now supports serialization using the safetensors library. Safetensors is a new simple format for storing tensors safely (instead of pickle), which is still fast (zero-copy). We’re excited to see more and more libraries leveraging safetensors for safe serialization. You can read more about a recent audit of the library in this blog post.

An example of using BERTopic to explore RLHF datasets

To illustrate some of the power of BERTopic let's look at an example of how it can be used to monitor changes in topics in datasets used to train chat models.

The last year has seen several datasets for Reinforcement Learning with Human Feedback released. One of these datasets is the OpenAssistant Conversations dataset. This dataset was produced via a worldwide crowd-sourcing effort involving over 13,500 volunteers. Whilst this dataset already has some scores for toxicity, quality, humour etc., we may want to get a better understanding of what types of conversations are represented in this dataset.

BERTopic offers one way of getting a better understanding of the topics in this dataset. In this case, we train a model on the English assistant responses part of the datasets. Resulting in a topic model with 75 topics.

BERTopic gives us various ways of visualizing a dataset. We can see the top 8 topics and their associated words below. We can see that the second most frequent topic consists mainly of ‘response words’, which we often see frequently from chat models, i.e. responses which aim to be ‘polite’ and ‘helpful’. We can also see a large number of topics related to programming or computing topics as well as physics, recipes and pets.

databricks/databricks-dolly-15k is another dataset that can be used to train an RLHF model. The approach taken to creating this dataset was quite different from the OpenAssistant Conversations dataset since it was created by employees of Databricks instead of being crowd sourced via volunteers. Perhaps we can use our trained BERTopic model to compare the topics across these two datasets?

The new BERTopic Hub integrations mean we can load this trained model and apply it to new examples.

topic_model = BERTopic.load("davanstrien/chat_topics")

We can predict on a single example text:

example = "Stalemate is a drawn position. It doesn't matter who has captured more pieces or is in a winning position"
topic, prob = topic_model.transform(example)

We can get more information about the predicted topic

topic_model.get_topic_info(topic)

We can see here the topics predicted seem to make sense. We may want to extend this to compare the topics predicted for the whole dataset.

from datasets import load_dataset

dataset = load_dataset("databricks/databricks-dolly-15k")
dolly_docs = dataset['train']['response']
dolly_topics, dolly_probs = topic_model.transform(dolly_docs)

We can then compare the distribution of topics across both datasets. We can see here that there seems to be a broader distribution across topics in the dolly dataset according to our BERTopic model. This might be a result of the different approaches to creating both datasets (we likely want to retrain a BERTopic across both datasets to ensure we are not missing topics to confirm this).

Comparison of the distribution of topics between the two datasets

We can potentially use topic models in a production setting to monitor whether topics drift to far from an expected distribution. This can serve as a signal that there has been drift between your original training data and the types of conversations you are seeing in production. You may also decide to use a topic modelling as you are collecting training data to ensure you are getting examples for topics you may particularly care about.

Get Started with BERTopic and Hugging Face Hub

You can visit the official documentation for a quick start guide to get help using BERTopic.

You can find a starter Colab notebook here that shows how you can train a BERTopic model and push it to the Hub.

Some examples of BERTopic models already on the hub:

• MaartenGr/BERTopic_ArXiv: a model trained on ~30000 ArXiv Computation and Language articles (cs.CL) after 1991.
• MaartenGr/BERTopic_Wikipedia: a model trained on 1000000 English Wikipedia pages.
• davanstrien/imdb_bertopic: a model trained on the unsupervised split of the IMDB dataset

You can find a full overview of BERTopic models on the hub using the libraries filter

We invite you to explore the possibilities of this new integration and share your trained models on the hub!