%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
TW
Chat Topics
模型概述
該模型使用BERTopic框架構建，專門用於分析聊天文本並提取有意義的話題分類。適用於社交媒體、客服對話等場景的話題挖掘和分析。
模型特點
模塊化設計
採用BERTopic的模塊化框架，可靈活調整各個處理環節
多話題識別
能夠自動識別75個不同的話題類別
關鍵詞提取
為每個話題提供最具代表性的關鍵詞
大規模訓練
基於63,530篇文檔訓練，覆蓋廣泛話題
模型能力
文本分類
話題識別
關鍵詞提取
話題可視化
使用案例
社交媒體分析
聊天話題監控
分析社交媒體上的熱門討論話題
識別75個不同的話題類別
客戶服務
客服對話分析
分類客戶諮詢的主要話題
提高客服響應效率
🚀 chat_topics
這是一個 BERTopic 模型。BERTopic 是一個靈活且模塊化的主題建模框架，可從大型數據集中生成易於解釋的主題。
🚀 快速開始
要使用此模型，你需要先安裝 BERTopic：
pip install -U bertopic
你可以按以下方式使用該模型：
from bertopic import BERTopic
topic_model = BERTopic.load("davanstrien/chat_topics")
topic_model.get_topic_info()
✨ 主要特性
主題概述
- 主題數量：75
- 訓練文檔數量：63530
點擊此處查看所有主題概述。
| 主題 ID | 主題關鍵詞 | 主題頻率 | 標籤 |
|---|---|---|---|
| -1 | provide - using - information - sure - help | 26 | -1_provide_using_information_sure |
| 0 | openai - ai - chatgpt - assistant - language | 7837 | 生成式人工智能 |
| 1 | anytime - welcome - assistance - helpful - thank | 1342 | 1_anytime_welcome_assistance_helpful |
| 2 | quantum - particle - physics - particles - relativity | 778 | 物理學 |
| 3 | story - lived - life - novel - felt | 569 | 3_story_lived_life_novel |
| 4 | letter - sincerely - regards - email - dear | 516 | 4_letter_sincerely_regards_email |
| 5 | rust - haskell - programming - java - languages | 504 | 編程 |
| 6 | css - html - style - div - js | 494 | 網頁編程 |
| 7 | linux - ubuntu - debian - fedora - install | 440 | 7_linux_ubuntu_debian_fedora |
| 8 | recipe - bake - ingredients - baking - dough | 425 | 8_recipe_bake_ingredients_baking |
| 9 | websocket - json - socket - api - discord | 425 | 9_websocket_json_socket_api |
| 10 | communism - capitalism - marx - economic - economy | 424 | 10_communism_capitalism_marx_economic |
| 11 | dog - pet - breed - breeds - pets | 408 | 11_dog_pet_breed_breeds |
| 12 | philosophy - theological - philosophical - beliefs - consciousness | 394 | 12_philosophy_theological_philosophical_beliefs |
| 13 | git - github - repository - software - commit | 381 | 13_git_github_repository_software |
| 14 | music - songs - musical - lyrics - song | 370 | 14_music_songs_musical_lyrics |
| 15 | devops - development - developers - industry - develop | 323 | 15_devops_development_developers_industry |
| 16 | pythagorean - hypotenuse - triangle - math - sqrt | 302 | 16_pythagorean_hypotenuse_triangle_math |
| 17 | eu - europe - economy - economic - war | 291 | 17_eu_europe_economy_economic |
| 18 | sleep - asleep - bedtime - procrastination - depression | 280 | 18_sleep_asleep_bedtime_procrastination |
| 19 | kramer - seinfeld - jerry - cafe - elaine | 279 | 19_kramer_seinfeld_jerry_cafe |
| 20 | printing - prints - printer - print - printers | 276 | 20_printing_prints_printer_print |
| 21 | influenza - flu - panic - symptoms - medical | 251 | 21_influenza_flu_panic_symptoms |
| 22 | chess - chessboard - practice - strategy - learn | 242 | 22_chess_chessboard_practice_strategy |
| 23 | algorithm - primes - array - integers - python | 240 | 23_algorithm_primes_array_integers |
| 24 | youtube - viewers - media - google - streaming | 240 | 24_youtube_viewers_media_google |
| 25 | poison - chemicals - powder - turpentine - smoke | 226 | 25_poison_chemicals_powder_turpentine |
| 26 | monday - sunday - count_weekend_days - calendar - dates | 216 | 26_monday_sunday_count_weekend_days_calendar |
| 27 | colors - colour - color - pigments - blue | 208 | 27_colors_colour_color_pigments |
| 28 | roman - attila - rome - empire - warfare | 205 | 28_roman_attila_rome_empire |
| 29 | investing - investments - investment - stocks - financial | 204 | 29_investing_investments_investment_stocks |
| 30 | vocabulary - wordle - words - scrabble - word | 201 | 30_vocabulary_wordle_words_scrabble |
| 31 | planets - sun - earth - planet - pluto | 198 | 31_planets_sun_earth_planet |
| 32 | renewable - solar - electricity - energy - electrical | 190 | 32_renewable_solar_electricity_energy |
| 33 | pygame - ball_radius - draw - circle - canvas | 181 | 33_pygame_ball_radius_draw_circle |
| 34 | fishing - fish - boat - hiking - camping | 176 | 34_fishing_fish_boat_hiking |
| 35 | gpus - gpu - motherboard - cpu - hardware | 162 | 35_gpus_gpu_motherboard_cpu |
| 36 | hvac - remodeling - energy - kwh - housing | 159 | 36_hvac_remodeling_energy_kwh |
| 37 | database - graphql - databases - postgresql - sql | 159 | 37_database_graphql_databases_postgresql |
| 38 | información - significado - cómo - como - sistemas | 158 | 38_información_significado_cómo_como |
| 39 | motherboard - pcie - gpu - bios - computer | 153 | 39_motherboard_pcie_gpu_bios |
| 40 | crops - produce - planting - peppers - plants | 148 | 40_crops_produce_planting_peppers |
| 41 | paintings - art - modernist - artists - modern | 148 | 41_paintings_art_modernist_artists |
| 42 | workout - exercises - dumbbells - dumbbell - exercise | 147 | 42_workout_exercises_dumbbells_dumbbell |
| 43 | climate - warming - pollution - environmental - emissions | 142 | 43_climate_warming_pollution_environmental |
| 44 | coffee - espresso - brewing - tea - beans | 137 | 44_coffee_espresso_brewing_tea |
| 45 | velocity - drag - acceleration - density - formula | 132 | 45_velocity_drag_acceleration_density |
| 46 | woodchuck - woodchucks - units - kilogram - kilograms | 130 | 46_woodchuck_woodchucks_units_kilogram |
| 47 | ascii - glyphs - hiragana - art - font | 129 | 47_ascii_glyphs_hiragana_art |
| 48 | guitars - guitar - strings - guitarists - instrument | 127 | 48_guitars_guitar_strings_guitarists |
| 49 | tallest - buildings - building - burj - khalifa | 114 | 49_tallest_buildings_building_burj |
| 50 | flat - earth - curvature - spherical - tectonic | 111 | 50_flat_earth_curvature_spherical |
| 51 | essay - awareness - understanding - being - be | 102 | 51_essay_awareness_understanding_being |
| 52 | portals - ender - portal - obsidian - netherite | 102 | 52_portals_ender_portal_obsidian |
| 53 | android - apple - phones - devices - vehicles | 101 | 53_android_apple_phones_devices |
| 54 | fasting - dietary - diet - eating - metabolic | 101 | 54_fasting_dietary_diet_eating |
| 55 | meditation - relief - pain - health - nociception | 99 | 55_meditation_relief_pain_health |
| 56 | weather - forecast - forecasts - raining - precipitation | 95 | 56_weather_forecast_forecasts_raining |
| 57 | president - presidents - presidency - constitution - biden | 94 | 57_president_presidents_presidency_constitution |
| 58 | no - nope - yes - not - maybe | 94 | 58_no_nope_yes_not |
| 59 | peregrine - airspeed - falcon - speed - bird | 90 | 59_peregrine_airspeed_falcon_speed |
| 60 | crontab - cron - myscript - script - bash | 83 | 60_crontab_cron_myscript_script |
| 61 | youtuber - streamer - ceo - musk - founder | 83 | 61_youtuber_streamer_ceo_musk |
| 62 | layovers - flights - circumnavigate - layover - travel | 83 | 62_layovers_flights_circumnavigate_layover |
| 63 | keyboards - keyboard - switches - qwerty - types | 83 | 63_keyboards_keyboard_switches_qwerty |
| 64 | file_path_in_dir1 - file_path1 - csv_file - file_path_in_dir2 - file_path2 | 80 | 64_file_path_in_dir1_file_path1_csv_file_file_path_in_dir2 |
| 65 | pele - maradona - lebron - ronaldo - nba | 76 | 65_pele_maradona_lebron_ronaldo |
| 66 | alopecia - hairstyles - hairstyle - hair - scalp | 66 | 66_alopecia_hairstyles_hairstyle_hair |
| 67 | nginx - docker - kubernetes - proxy_pass - nodeport | 65 | 67_nginx_docker_kubernetes_proxy_pass |
| 68 | directories - directory - sudo - filesystem - folders | 62 | 68_directories_directory_sudo_filesystem |
| 69 | gps - map - geocaching - maps - armenia | 52 | 69_gps_map_geocaching_maps |
| 70 | meiosis - mitosis - fertilization - reproduction - ovulation | 51 | 70_meiosis_mitosis_fertilization_reproduction |
| 71 | colleges - admissions - universities - campus - university | 43 | 71_colleges_admissions_universities_campus |
| 72 | unicorns - unicorn - pony - ponies - mythical | 32 | 72_unicorns_unicorn_pony_ponies |
| 73 | superpowers - abilities - superhero - superhuman - powers | 28 | 73_superpowers_abilities_superhero_superhuman |
訓練超參數
- calculate_probabilities: False
- language: None
- low_memory: False
- min_topic_size: 20
- n_gram_range: (1, 1)
- nr_topics: 75
- seed_topic_list: None
- top_n_words: 10
- verbose: True
框架版本
| 屬性 | 詳情 |
|---|---|
| 模型類型 | BERTopic 主題建模模型 |
| 訓練數據 | OpenAssistant/oasst1 |
| Numpy 版本 | 1.22.4 |
| HDBSCAN 版本 | 0.8.29 |
| UMAP 版本 | 0.5.3 |
| Pandas 版本 | 1.5.3 |
| Scikit-Learn 版本 | 1.2.2 |
| Sentence-transformers 版本 | 2.2.2 |
| Transformers 版本 | 4.29.2 |
| Numba 版本 | 0.56.4 |
| Plotly 版本 | 5.13.1 |
| Python 版本 | 3.10.11 |
📄 許可證
本項目採用 MIT 許可證。
Distilbert Base Uncased Finetuned Sst 2 English
Apache-2.0
基於DistilBERT-base-uncased在SST-2情感分析數據集上微調的文本分類模型，準確率91.3%
文本分類 英語
D
distilbert
5.2M
746
Xlm Roberta Base Language Detection
MIT
基於XLM-RoBERTa的多語言檢測模型，支持20種語言的文本分類
文本分類
Transformers 支持多種語言
Transformers 支持多種語言X
papluca
2.7M
333
Roberta Hate Speech Dynabench R4 Target
該模型通過動態生成數據集來改進在線仇恨檢測，專注於從最差案例中學習以提高檢測效果。
文本分類 Transformers 英語
Transformers 英語R
facebook
2.0M
80
Bert Base Multilingual Uncased Sentiment
MIT
基於bert-base-multilingual-uncased微調的多語言情感分析模型，支持6種語言的商品評論情感分析
文本分類 支持多種語言
B
nlptown
1.8M
371
Emotion English Distilroberta Base
基於DistilRoBERTa-base微調的英文文本情感分類模型，可預測埃克曼六種基本情緒及中性類別。
文本分類 Transformers 英語
Transformers 英語E
j-hartmann
1.1M
402
Robertuito Sentiment Analysis
基於RoBERTuito的西班牙語推文情感分析模型，支持POS(積極)/NEG(消極)/NEU(中性)三類情感分類
文本分類 西班牙語
R
pysentimiento
1.0M
88
Finbert Tone
FinBERT是一款基於金融通訊文本預訓練的BERT模型，專注於金融自然語言處理領域。finbert-tone是其微調版本，用於金融情感分析任務。
文本分類 Transformers 英語
Transformers 英語F
yiyanghkust
998.46k
178
Roberta Base Go Emotions
MIT
基於RoBERTa-base的多標籤情感分類模型，在go_emotions數據集上訓練，支持28種情感標籤識別。
文本分類 Transformers 英語
Transformers 英語R
SamLowe
848.12k
565
Xlm Emo T
XLM-EMO是一個基於XLM-T模型微調的多語言情感分析模型，支持19種語言，專門針對社交媒體文本的情感預測。
文本分類 Transformers 其他
Transformers 其他X
MilaNLProc
692.30k
7
Deberta V3 Base Mnli Fever Anli
MIT
基於MultiNLI、Fever-NLI和ANLI數據集訓練的DeBERTa-v3模型，擅長零樣本分類和自然語言推理任務
文本分類 Transformers 英語
Transformers 英語D
MoritzLaurer
613.93k
204
精選推薦AI模型
Llama 3 Typhoon V1.5x 8b Instruct
專為泰語設計的80億參數指令模型，性能媲美GPT-3.5-turbo，優化了應用場景、檢索增強生成、受限生成和推理任務
大型語言模型 Transformers 支持多種語言
Transformers 支持多種語言L
scb10x
3,269
16
Cadet Tiny
Openrail
Cadet-Tiny是一個基於SODA數據集訓練的超小型對話模型，專為邊緣設備推理設計，體積僅為Cosmo-3B模型的2%左右。
對話系統 Transformers 英語
Transformers 英語C
ToddGoldfarb
2,691
6
Roberta Base Chinese Extractive Qa
基於RoBERTa架構的中文抽取式問答模型，適用於從給定文本中提取答案的任務。
問答系統 中文
R
uer
2,694
98