bertopic-test_3030 / README.md
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Add BERTopic model
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metadata
tags:
  - bertopic
library_name: bertopic
pipeline_tag: text-classification

bertopic-test_3030

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("ahessamb/bertopic-test_3030")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 30
  • Number of training documents: 1570
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
0 apecoin - neckline - shoulders - fluctuating - chart 2 0_apecoin_neckline_shoulders_fluctuating
1 astra - letter - investors - coindesk - bankruptcy 84 1_astra_letter_investors_coindesk
2 26 - bulls - rsi - ceiling - low 30 2_26_bulls_rsi_ceiling
3 mutant - mayc - bayc - club - nfts 112 3_mutant_mayc_bayc_club
4 shib - doge - shiba - sentiment - dogecoin 115 4_shib_doge_shiba_sentiment
5 xrp - btc - lawsuit - sleuth - bullish 47 5_xrp_btc_lawsuit_sleuth
6 binance - securities - crypto - coinbase - regulatory 147 6_binance_securities_crypto_coinbase
7 ordibots - ordinals - collection - gbrc721 - text 33 7_ordibots_ordinals_collection_gbrc721
8 kitao - sbi - xrp - ripple - holdings 95 8_kitao_sbi_xrp_ripple
9 listings - exponential - coin - ethereum - defi 163 9_listings_exponential_coin_ethereum
10 yuan - event - games - rewards - olympics 68 10_yuan_event_games_rewards
11 emptydoc - richmond - fashion - shiba - community 15 11_emptydoc_richmond_fashion_shiba
12 sygnum - crypto - piggy - btr - huobi 59 12_sygnum_crypto_piggy_btr
13 dln - debridge - chains - liquidity - slippage 3 13_dln_debridge_chains_liquidity
14 longitude - chronometer - bitcoin - ships - rogers 5 14_longitude_chronometer_bitcoin_ships
15 arbitrum - airdrop - recipients - scalability - ethereum 14 15_arbitrum_airdrop_recipients_scalability
16 ethereum - fidelity - blackrock - cryptocurrency - fee 111 16_ethereum_fidelity_blackrock_cryptocurrency
17 swyftx - shibarium - token - shiba - shibaswap 17 17_swyftx_shibarium_token_shiba
18 zachxbt - squid - huang - donation - accused 21 18_zachxbt_squid_huang_donation
19 reading - trend - leaning - ltc - breakdown 2 19_reading_trend_leaning_ltc
20 tether - reserve - gusd - cbdcs - bills 45 20_tether_reserve_gusd_cbdcs
21 lace - brave - mobile - wallet - iog 2 21_lace_brave_mobile_wallet
22 binance - day - coinbase - exchange - bitcoin 82 22_binance_day_coinbase_exchange
23 v3 - bnb - repurchase - peng - pancakeswap 2 23_v3_bnb_repurchase_peng
24 xrp - banks - ripple - institutions - p2p 6 24_xrp_banks_ripple_institutions
25 ada - level - litecoin - cardano - resistance 186 25_ada_level_litecoin_cardano
26 xrp - hoskinson - cardano - securities - analisa 26 26_xrp_hoskinson_cardano_securities
27 peaq - lunc - fetch - cosmos - terra 73 27_peaq_lunc_fetch_cosmos
28 kostin - russia - sanctions - currency - yuan 2 28_kostin_russia_sanctions_currency
29 upgrade - terra - lunc - chrome - jumps 3 29_upgrade_terra_lunc_chrome

Training hyperparameters

  • calculate_probabilities: False
  • language: None
  • low_memory: False
  • min_topic_size: 10
  • n_gram_range: (1, 1)
  • nr_topics: None
  • seed_topic_list: None
  • top_n_words: 10
  • verbose: False

Framework versions

  • Numpy: 1.22.4
  • HDBSCAN: 0.8.29
  • UMAP: 0.5.3
  • Pandas: 1.5.3
  • Scikit-Learn: 1.2.2
  • Sentence-transformers: 2.2.2
  • Transformers: 4.30.2
  • Numba: 0.56.4
  • Plotly: 5.13.1
  • Python: 3.10.12