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Introducing BERTopic Integration with the Hugging Face Hub
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davanstrien

Introducing BERTopic Integration with the Hugging Face Hub

Open in Colab

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.

BERTopic hub filter

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.

Words associated with top 8 topics

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
0 240 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).

Topic distribution comparison

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:

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!