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

-1_language_models_model_data

dialogue - dialog - response - responses - intent

14247

0_dialogue_dialog_response_responses

speech - asr - speech recognition - recognition - end

1833

1_speech_asr_speech recognition_recognition

tuning - tasks - prompt - models - language

1369

2_tuning_tasks_prompt_models

summarization - summaries - summary - abstractive - document

1109

3_summarization_summaries_summary_abstractive

question - answer - qa - answering - question answering

893

4_question_answer_qa_answering

sentiment - sentiment analysis - aspect - analysis - opinion

837

5_sentiment_sentiment analysis_aspect_analysis

clinical - medical - biomedical - notes - patient

691

6_clinical_medical_biomedical_notes

translation - nmt - machine translation - neural machine - neural machine translation

586

7_translation_nmt_machine translation_neural machine

generation - text generation - text - language generation - nlg

558

8_generation_text generation_text_language generation

hate - hate speech - offensive - speech - detection

484

9_hate_hate speech_offensive_speech

news - fake - fake news - stance - fact

455

10_news_fake_fake news_stance

relation - relation extraction - extraction - relations - entity

450

11_relation_relation extraction_extraction_relations

ner - named - named entity - entity - named entity recognition

376

12_ner_named_named entity_entity

parsing - parser - dependency - treebank - parsers

370

13_parsing_parser_dependency_treebank

event - temporal - events - event extraction - extraction

314

14_event_temporal_events_event extraction

emotion - emotions - multimodal - emotion recognition - emotional

300

15_emotion_emotions_multimodal_emotion recognition

word - embeddings - word embeddings - embedding - words

292

16_word_embeddings_word embeddings_embedding

explanations - explanation - rationales - rationale - interpretability

212

17_explanations_explanation_rationales_rationale

morphological - arabic - morphology - languages - inflection

204

18_morphological_arabic_morphology_languages

topic - topics - topic models - lda - topic modeling

200

19_topic_topics_topic models_lda

bias - gender - biases - gender bias - debiasing

195

20_bias_gender_biases_gender bias

law - frequency - zipf - words - length

185

21_law_frequency_zipf_words

legal - court - law - legal domain - case

182

22_legal_court_law_legal domain

adversarial - attacks - attack - adversarial examples - robustness

181

23_adversarial_attacks_attack_adversarial examples

commonsense - commonsense knowledge - reasoning - knowledge - commonsense reasoning

180

24_commonsense_commonsense knowledge_reasoning_knowledge

quantum - semantics - calculus - compositional - meaning

171

25_quantum_semantics_calculus_compositional

correction - error - error correction - grammatical - grammatical error

161

26_correction_error_error correction_grammatical

argument - arguments - argumentation - argumentative - mining

160

27_argument_arguments_argumentation_argumentative

sarcasm - humor - sarcastic - detection - humorous

157

28_sarcasm_humor_sarcastic_detection

coreference - resolution - coreference resolution - mentions - mention

156

29_coreference_resolution_coreference resolution_mentions

sense - word sense - wsd - word - disambiguation

153

30_sense_word sense_wsd_word

knowledge - knowledge graph - graph - link prediction - entities

149

31_knowledge_knowledge graph_graph_link prediction

parsing - semantic parsing - amr - semantic - parser

146

32_parsing_semantic parsing_amr_semantic

cross lingual - lingual - cross - transfer - languages

146

33_cross lingual_lingual_cross_transfer

mt - translation - qe - quality - machine translation

139

34_mt_translation_qe_quality

sql - text sql - queries - spider - schema

138

35_sql_text sql_queries_spider

classification - text classification - label - text - labels

136

36_classification_text classification_label_text

style - style transfer - transfer - text style - text style transfer

136

37_style_style transfer_transfer_text style

question - question generation - questions - answer - generation

129

38_question_question generation_questions_answer

authorship - authorship attribution - attribution - author - authors

127

39_authorship_authorship attribution_attribution_author

sentence - sentence embeddings - similarity - sts - sentence embedding

123

40_sentence_sentence embeddings_similarity_sts

code - identification - switching - cs - code switching

121

41_code_identification_switching_cs

story - stories - story generation - generation - storytelling

118

42_story_stories_story generation_generation

discourse - discourse relation - discourse relations - rst - discourse parsing

117

43_discourse_discourse relation_discourse relations_rst

code - programming - source code - code generation - programming languages

117

44_code_programming_source code_code generation

paraphrase - paraphrases - paraphrase generation - paraphrasing - generation

114

45_paraphrase_paraphrases_paraphrase generation_paraphrasing

agent - games - environment - instructions - agents

111

46_agent_games_environment_instructions

covid - covid 19 - 19 - tweets - pandemic

108

47_covid_covid 19_19_tweets

linking - entity linking - entity - el - entities

107

48_linking_entity linking_entity_el

poetry - poems - lyrics - poem - music

103

49_poetry_poems_lyrics_poem

image - captioning - captions - visual - caption

100

50_image_captioning_captions_visual

nli - entailment - inference - natural language inference - language inference

51_nli_entailment_inference_natural language inference

keyphrase - keyphrases - extraction - document - phrases

52_keyphrase_keyphrases_extraction_document

simplification - text simplification - ts - sentence - simplified

53_simplification_text simplification_ts_sentence

empathetic - emotion - emotional - empathy - emotions

54_empathetic_emotion_emotional_empathy

depression - mental - health - mental health - social media

55_depression_mental_health_mental health

segmentation - word segmentation - chinese - chinese word segmentation - chinese word

56_segmentation_word segmentation_chinese_chinese word segmentation

citation - scientific - papers - citations - scholarly

57_citation_scientific_papers_citations

agreement - syntactic - verb - grammatical - subject verb

58_agreement_syntactic_verb_grammatical

metaphor - literal - figurative - metaphors - idiomatic

59_metaphor_literal_figurative_metaphors

srl - semantic role - role labeling - semantic role labeling - role

60_srl_semantic role_role labeling_semantic role labeling

privacy - private - federated - privacy preserving - federated learning

61_privacy_private_federated_privacy preserving

change - semantic change - time - semantic - lexical semantic

62_change_semantic change_time_semantic

bilingual - lingual - cross lingual - cross - embeddings

63_bilingual_lingual_cross lingual_cross

political - media - news - bias - articles

64_political_media_news_bias

medical - qa - question - questions - clinical

65_medical_qa_question_questions

math - mathematical - math word - word problems - problems

66_math_mathematical_math word_word problems

financial - stock - market - price - news

67_financial_stock_market_price

table - tables - tabular - reasoning - qa

68_table_tables_tabular_reasoning

readability - complexity - assessment - features - reading

69_readability_complexity_assessment_features

layout - document - documents - document understanding - extraction

70_layout_document_documents_document understanding

brain - cognitive - reading - syntactic - language

71_brain_cognitive_reading_syntactic

sign - gloss - language - signed - language translation

72_sign_gloss_language_signed

vqa - visual - visual question - visual question answering - question

73_vqa_visual_visual question_visual question answering

biased - biases - spurious - nlp - debiasing

74_biased_biases_spurious_nlp

visual - dialogue - multimodal - image - dialog

75_visual_dialogue_multimodal_image

translation - machine translation - machine - smt - statistical

76_translation_machine translation_machine_smt

multimodal - visual - image - translation - machine translation

77_multimodal_visual_image_translation

geographic - location - geolocation - geo - locations

78_geographic_location_geolocation_geo

reasoning - prompting - llms - chain thought - chain

79_reasoning_prompting_llms_chain thought

essay - scoring - aes - essay scoring - essays

80_essay_scoring_aes_essay scoring

crisis - disaster - traffic - tweets - disasters

81_crisis_disaster_traffic_tweets

graph - text classification - text - gcn - classification

82_graph_text classification_text_gcn

annotation - tools - linguistic - resources - xml

83_annotation_tools_linguistic_resources

entity alignment - alignment - kgs - entity - ea

84_entity alignment_alignment_kgs_entity

personality - traits - personality traits - evaluative - text

85_personality_traits_personality traits_evaluative

ad - alzheimer - alzheimer disease - disease - speech

86_ad_alzheimer_alzheimer disease_disease

taxonomy - hypernymy - taxonomies - hypernym - hypernyms

87_taxonomy_hypernymy_taxonomies_hypernym

active learning - active - al - learning - uncertainty

88_active learning_active_al_learning

reviews - summaries - summarization - review - opinion

89_reviews_summaries_summarization_review

emoji - emojis - sentiment - message - anonymous

90_emoji_emojis_sentiment_message

table - table text - tables - table text generation - text generation

91_table_table text_tables_table text generation

domain - domain adaptation - adaptation - domains - source

92_domain_domain adaptation_adaptation_domains

alignment - word alignment - parallel - pairs - alignments

93_alignment_word alignment_parallel_pairs

indo - languages - indo european - names - family

94_indo_languages_indo european_names

patent - claim - claim generation - chemical - technical

95_patent_claim_claim generation_chemical

agents - emergent - communication - referential - games

96_agents_emergent_communication_referential

graph - amr - graph text - graphs - text generation

97_graph_amr_graph text_graphs

moral - ethical - norms - values - social

98_moral_ethical_norms_values

acronym - acronyms - abbreviations - abbreviation - disambiguation

99_acronym_acronyms_abbreviations_abbreviation

100

typing - entity typing - entity - type - types

100_typing_entity typing_entity_type

101

coherence - discourse - discourse coherence - coherence modeling - text

101_coherence_discourse_discourse coherence_coherence modeling

102

pos - taggers - tagging - tagger - pos tagging

102_pos_taggers_tagging_tagger

103

drug - social - social media - media - health

103_drug_social_social media_media

104

gender - translation - bias - gender bias - mt

104_gender_translation_bias_gender bias

105

job - resume - skills - skill - soft

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)

Count

Name

Representation

240

22_chess_chessboard_practice_strategy

['chess', 'chessboard', 'practice', 'strategy', 'learn', 'pawn', 'board', 'pawns', 'play', 'decks']

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!