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ahessamb/
bertopic-test_3030

Name: bertopic-test_3030
Availability: InStock

bertopic

text-classification

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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:

python

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