bertopic-test / README.md

Add BERTopic model

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	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# bertopic-test

	This is a [BERTopic](https://github.com/MaartenGr/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")

	topic_model.get_topic_info()
	```

	## Topic overview

	* Number of topics: 50
	* Number of training documents: 1570

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| Topic ID \| Topic Keywords \| Topic Frequency \| Label \|
	\|----------\|----------------\|-----------------\|-------\|
	\| 0 \| liquidations - forcefully - betting - liquidation - contracts \| 8 \| 0_liquidations_forcefully_betting_liquidation \|
	\| 1 \| litecoin - wsm - presale - 77 - near \| 94 \| 1_litecoin_wsm_presale_77 \|
	\| 2 \| sec - court - terraform - dismiss - lawyers \| 49 \| 2_sec_court_terraform_dismiss \|
	\| 3 \| huobi - hkvac - bsl - web3 - code \| 12 \| 3_huobi_hkvac_bsl_web3 \|
	\| 4 \| lucie - shiba - susbarium - puppynet - portals \| 3 \| 4_lucie_shiba_susbarium_puppynet \|
	\| 5 \| 000006819 - shiba - accuracy - finbold - estimates \| 27 \| 5_000006819_shiba_accuracy_finbold \|
	\| 6 \| tokens - sec - binance - securities - coinbase \| 45 \| 6_tokens_sec_binance_securities \|
	\| 7 \| mckinsey - ai - nanjing - productivity - diffusion \| 43 \| 7_mckinsey_ai_nanjing_productivity \|
	\| 8 \| resistance - swing - fib - zone - ltc \| 32 \| 8_resistance_swing_fib_zone \|
	\| 9 \| brinkman - tategpt - bitcoin - artists - wealth \| 26 \| 9_brinkman_tategpt_bitcoin_artists \|
	\| 10 \| stablecoin - stablecoins - decline - redemptions - tusd \| 2 \| 10_stablecoin_stablecoins_decline_redemptions \|
	\| 11 \| mutant - mayc - bayc - club - mcmullen \| 64 \| 11_mutant_mayc_bayc_club \|
	\| 12 \| xrp - ema - ripple - bullish - cryptocurrencies \| 43 \| 12_xrp_ema_ripple_bullish \|
	\| 13 \| tether - cbdcs - loans - federal - nafcu \| 27 \| 13_tether_cbdcs_loans_federal \|
	\| 14 \| rate - tradingview - bnb - breakout - coinmarketcap \| 85 \| 14_rate_tradingview_bnb_breakout \|
	\| 15 \| 26 - bulls - rsi - ceiling - 300 \| 2 \| 15_26_bulls_rsi_ceiling \|
	\| 16 \| lowest - jump - week - wallet - staggering \| 3 \| 16_lowest_jump_week_wallet \|
	\| 17 \| xrp - ripple - mekras - sbi - institutions \| 56 \| 17_xrp_ripple_mekras_sbi \|
	\| 18 \| debt - mortgages - trillion - government - suspends \| 3 \| 18_debt_mortgages_trillion_government \|
	\| 19 \| longitude - chronometer - bitcoin - ships - graffiti \| 2 \| 19_longitude_chronometer_bitcoin_ships \|
	\| 20 \| volumes - piggy - aud - xrp - usdt \| 15 \| 20_volumes_piggy_aud_xrp \|
	\| 21 \| root - ledger - stakers - sidechains - compatibility \| 4 \| 21_root_ledger_stakers_sidechains \|
	\| 22 \| astra - letter - concerns - investors - bitwise \| 4 \| 22_astra_letter_concerns_investors \|
	\| 23 \| gold - governments - manipulated - stocks - mined \| 10 \| 23_gold_governments_manipulated_stocks \|
	\| 24 \| tether - sygnum - documents - bank - coindesk \| 9 \| 24_tether_sygnum_documents_bank \|
	\| 25 \| rewards - governance - lido - proposal - june \| 45 \| 25_rewards_governance_lido_proposal \|
	\| 26 \| listings - coin - fairerc20 - bittrex - withdrawals \| 68 \| 26_listings_coin_fairerc20_bittrex \|
	\| 27 \| peaq - ordibots - cosmos - fetch - machine \| 81 \| 27_peaq_ordibots_cosmos_fetch \|
	\| 28 \| uniswap - v4 - orders - hooks - differing \| 23 \| 28_uniswap_v4_orders_hooks \|
	\| 29 \| price - neo - matic - rise - altcoin \| 92 \| 29_price_neo_matic_rise \|
	\| 30 \| emptydoc - staff - policy - binance - workspaces \| 2 \| 30_emptydoc_staff_policy_binance \|
	\| 31 \| lunc - synthetix - terra - perps - staking \| 33 \| 31_lunc_synthetix_terra_perps \|
	\| 32 \| tweet - dogecoin - chart - meme - negative \| 3 \| 32_tweet_dogecoin_chart_meme \|
	\| 33 \| binance - securities - exchange - cz - regulators \| 63 \| 33_binance_securities_exchange_cz \|
	\| 34 \| bitmart - sale - xrp - discount - event \| 4 \| 34_bitmart_sale_xrp_discount \|
	\| 35 \| yuan - event - olympics - canadians - organizers \| 49 \| 35_yuan_event_olympics_canadians \|
	\| 36 \| gusd - fidelity - bitcoin - proposal - blackrock \| 52 \| 36_gusd_fidelity_bitcoin_proposal \|
	\| 37 \| bills - mcglone - markets - stablecoins - liquidity \| 56 \| 37_bills_mcglone_markets_stablecoins \|
	\| 38 \| asset - gain - drop - trading - hours \| 2 \| 38_asset_gain_drop_trading \|
	\| 39 \| epstein - hamsterwheel - vulnerability - bounty - certick \| 28 \| 39_epstein_hamsterwheel_vulnerability_bounty \|
	\| 40 \| pyth - transparency - data - terra - oracle \| 19 \| 40_pyth_transparency_data_terra \|
	\| 41 \| shiba - inu - weighted - collapse - recovery \| 2 \| 41_shiba_inu_weighted_collapse \|
	\| 42 \| neo - opensea - carey - security - impersonators \| 24 \| 42_neo_opensea_carey_security \|
	\| 43 \| balancer - zkevm - liquidity - defi - 8020 \| 3 \| 43_balancer_zkevm_liquidity_defi \|
	\| 44 \| reed - battle - platform - argument - trading \| 22 \| 44_reed_battle_platform_argument \|
	\| 45 \| ada - cardano - whale - sell - investors \| 4 \| 45_ada_cardano_whale_sell \|
	\| 46 \| uk - coinbase - hong - crypto - regulatory \| 65 \| 46_uk_coinbase_hong_crypto \|
	\| 47 \| ethereum - tvl - defi - arbitrum - airdrop \| 54 \| 47_ethereum_tvl_defi_arbitrum \|
	\| 48 \| swyftx - shibarium - token - shibaswap - shiba \| 54 \| 48_swyftx_shibarium_token_shibaswap \|
	\| 49 \| bitcoin - mining - gain - miners - difficulty \| 54 \| 49_bitcoin_mining_gain_miners \|

	</details>

	## 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


	---
	tags:
	- bertopic
	library_name: bertopic
	pipeline_tag: text-classification
	---

	# bertopic-test

	This is a [BERTopic](https://github.com/MaartenGr/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")

	topic_model.get_topic_info()
	```

	## Topic overview

	* Number of topics: 50
	* Number of training documents: 1570

	<details>
	<summary>Click here for an overview of all topics.</summary>

	\| Topic ID \| Topic Keywords \| Topic Frequency \| Label \|
	\|----------\|----------------\|-----------------\|-------\|
	\| 0 \| liquidations - forcefully - betting - liquidation - contracts \| 8 \| 0_liquidations_forcefully_betting_liquidation \|
	\| 1 \| litecoin - wsm - presale - 77 - near \| 94 \| 1_litecoin_wsm_presale_77 \|
	\| 2 \| sec - court - terraform - dismiss - lawyers \| 49 \| 2_sec_court_terraform_dismiss \|
	\| 3 \| huobi - hkvac - bsl - web3 - code \| 12 \| 3_huobi_hkvac_bsl_web3 \|
	\| 4 \| lucie - shiba - susbarium - puppynet - portals \| 3 \| 4_lucie_shiba_susbarium_puppynet \|
	\| 5 \| 000006819 - shiba - accuracy - finbold - estimates \| 27 \| 5_000006819_shiba_accuracy_finbold \|
	\| 6 \| tokens - sec - binance - securities - coinbase \| 45 \| 6_tokens_sec_binance_securities \|
	\| 7 \| mckinsey - ai - nanjing - productivity - diffusion \| 43 \| 7_mckinsey_ai_nanjing_productivity \|
	\| 8 \| resistance - swing - fib - zone - ltc \| 32 \| 8_resistance_swing_fib_zone \|
	\| 9 \| brinkman - tategpt - bitcoin - artists - wealth \| 26 \| 9_brinkman_tategpt_bitcoin_artists \|
	\| 10 \| stablecoin - stablecoins - decline - redemptions - tusd \| 2 \| 10_stablecoin_stablecoins_decline_redemptions \|
	\| 11 \| mutant - mayc - bayc - club - mcmullen \| 64 \| 11_mutant_mayc_bayc_club \|
	\| 12 \| xrp - ema - ripple - bullish - cryptocurrencies \| 43 \| 12_xrp_ema_ripple_bullish \|
	\| 13 \| tether - cbdcs - loans - federal - nafcu \| 27 \| 13_tether_cbdcs_loans_federal \|
	\| 14 \| rate - tradingview - bnb - breakout - coinmarketcap \| 85 \| 14_rate_tradingview_bnb_breakout \|
	\| 15 \| 26 - bulls - rsi - ceiling - 300 \| 2 \| 15_26_bulls_rsi_ceiling \|
	\| 16 \| lowest - jump - week - wallet - staggering \| 3 \| 16_lowest_jump_week_wallet \|
	\| 17 \| xrp - ripple - mekras - sbi - institutions \| 56 \| 17_xrp_ripple_mekras_sbi \|
	\| 18 \| debt - mortgages - trillion - government - suspends \| 3 \| 18_debt_mortgages_trillion_government \|
	\| 19 \| longitude - chronometer - bitcoin - ships - graffiti \| 2 \| 19_longitude_chronometer_bitcoin_ships \|
	\| 20 \| volumes - piggy - aud - xrp - usdt \| 15 \| 20_volumes_piggy_aud_xrp \|
	\| 21 \| root - ledger - stakers - sidechains - compatibility \| 4 \| 21_root_ledger_stakers_sidechains \|
	\| 22 \| astra - letter - concerns - investors - bitwise \| 4 \| 22_astra_letter_concerns_investors \|
	\| 23 \| gold - governments - manipulated - stocks - mined \| 10 \| 23_gold_governments_manipulated_stocks \|
	\| 24 \| tether - sygnum - documents - bank - coindesk \| 9 \| 24_tether_sygnum_documents_bank \|
	\| 25 \| rewards - governance - lido - proposal - june \| 45 \| 25_rewards_governance_lido_proposal \|
	\| 26 \| listings - coin - fairerc20 - bittrex - withdrawals \| 68 \| 26_listings_coin_fairerc20_bittrex \|
	\| 27 \| peaq - ordibots - cosmos - fetch - machine \| 81 \| 27_peaq_ordibots_cosmos_fetch \|
	\| 28 \| uniswap - v4 - orders - hooks - differing \| 23 \| 28_uniswap_v4_orders_hooks \|
	\| 29 \| price - neo - matic - rise - altcoin \| 92 \| 29_price_neo_matic_rise \|
	\| 30 \| emptydoc - staff - policy - binance - workspaces \| 2 \| 30_emptydoc_staff_policy_binance \|
	\| 31 \| lunc - synthetix - terra - perps - staking \| 33 \| 31_lunc_synthetix_terra_perps \|
	\| 32 \| tweet - dogecoin - chart - meme - negative \| 3 \| 32_tweet_dogecoin_chart_meme \|
	\| 33 \| binance - securities - exchange - cz - regulators \| 63 \| 33_binance_securities_exchange_cz \|
	\| 34 \| bitmart - sale - xrp - discount - event \| 4 \| 34_bitmart_sale_xrp_discount \|
	\| 35 \| yuan - event - olympics - canadians - organizers \| 49 \| 35_yuan_event_olympics_canadians \|
	\| 36 \| gusd - fidelity - bitcoin - proposal - blackrock \| 52 \| 36_gusd_fidelity_bitcoin_proposal \|
	\| 37 \| bills - mcglone - markets - stablecoins - liquidity \| 56 \| 37_bills_mcglone_markets_stablecoins \|
	\| 38 \| asset - gain - drop - trading - hours \| 2 \| 38_asset_gain_drop_trading \|
	\| 39 \| epstein - hamsterwheel - vulnerability - bounty - certick \| 28 \| 39_epstein_hamsterwheel_vulnerability_bounty \|
	\| 40 \| pyth - transparency - data - terra - oracle \| 19 \| 40_pyth_transparency_data_terra \|
	\| 41 \| shiba - inu - weighted - collapse - recovery \| 2 \| 41_shiba_inu_weighted_collapse \|
	\| 42 \| neo - opensea - carey - security - impersonators \| 24 \| 42_neo_opensea_carey_security \|
	\| 43 \| balancer - zkevm - liquidity - defi - 8020 \| 3 \| 43_balancer_zkevm_liquidity_defi \|
	\| 44 \| reed - battle - platform - argument - trading \| 22 \| 44_reed_battle_platform_argument \|
	\| 45 \| ada - cardano - whale - sell - investors \| 4 \| 45_ada_cardano_whale_sell \|
	\| 46 \| uk - coinbase - hong - crypto - regulatory \| 65 \| 46_uk_coinbase_hong_crypto \|
	\| 47 \| ethereum - tvl - defi - arbitrum - airdrop \| 54 \| 47_ethereum_tvl_defi_arbitrum \|
	\| 48 \| swyftx - shibarium - token - shibaswap - shiba \| 54 \| 48_swyftx_shibarium_token_shibaswap \|
	\| 49 \| bitcoin - mining - gain - miners - difficulty \| 54 \| 49_bitcoin_mining_gain_miners \|

	</details>

	## 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