README.md

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

# BERTopic_topics_agriculture

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("hebashakeel/BERTopic_topics_agriculture")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 86
* Number of training documents: 58144

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 |  -  -  -  -  | 162 | -1____ | 
| 0 | sauce - you - recipe - add - with | 1 | 0_sauce_you_recipe_add | 
| 1 | crops - crop - ha - wheat - spring | 3467 | 1_crops_crop_ha_wheat | 
| 2 | tractor - deere - the - with - it | 3097 | 2_tractor_deere_the_with | 
| 3 | cows - milk - is - cattle - grazing | 2373 | 3_cows_milk_is_cattle | 
| 4 | prices - wheat - corn - year - million | 2297 | 4_prices_wheat_corn_year | 
| 5 | soil - plants - plant - fruit - is | 2325 | 5_soil_plants_plant_fruit | 
| 6 | canada - ontario - agriculture - canadian - and | 2274 | 6_canada_ontario_agriculture_canadian | 
| 7 | we - that - to - the - of | 2362 | 7_we_that_to_the | 
| 8 | market - analysis - report - global - forecast | 4450 | 8_market_analysis_report_global | 
| 9 | usda - program - and - agriculture - programs | 1103 | 9_usda_program_and_agriculture | 
| 10 | uk - trade - workers - government - the | 1697 | 10_uk_trade_workers_government | 
| 11 | corn - percent - moisture - crop - planting | 1406 | 11_corn_percent_moisture_crop | 
| 12 | poultry - birds - chickens - feed - eggs | 909 | 12_poultry_birds_chickens_feed | 
| 13 | pig - pigs - npa - producers - said | 719 | 13_pig_pigs_npa_producers | 
| 14 | mental - health - people - farmers - charity | 559 | 14_mental_health_people_farmers | 
| 15 | police - crime - dog - rural - said | 595 | 15_police_crime_dog_rural | 
| 16 | organic - farming - crops - soil - certification | 654 | 16_organic_farming_crops_soil | 
| 17 | candle - you - your - amazon - candles | 649 | 17_candle_you_your_amazon | 
| 18 | fish - pond - water - catfish - ponds | 732 | 18_fish_pond_water_catfish | 
| 19 | kg - lamb - lambs - cattle - beef | 514 | 19_kg_lamb_lambs_cattle | 
| 20 | scheme - defra - farmers - sfi - land | 601 | 20_scheme_defra_farmers_sfi | 
| 21 | based - plant - foods - meat - company | 792 | 21_based_plant_foods_meat | 
| 22 | gene - plant - plants - genetic - research | 731 | 22_gene_plant_plants_genetic | 
| 23 | we - our - quarter - that - think | 742 | 23_we_our_quarter_that | 
| 24 | carbon - emissions - farmers - to - and | 538 | 24_carbon_emissions_farmers_to | 
| 25 | deforestation - forest - forests - eu - brazil | 1191 | 25_deforestation_forest_forests_eu | 
| 26 | laws - farmers - fence - government - minister | 560 | 26_laws_farmers_fence_government | 
| 27 | pig - pigs - sows - sow - farrowing | 417 | 27_pig_pigs_sows_sow | 
| 28 | safety - was - fire - farm - the | 481 | 28_safety_was_fire_farm | 
| 29 | fish - ocean - marine - the - seafood | 537 | 29_fish_ocean_marine_the | 
| 30 | land - agricultural - property - title - registration | 436 | 30_land_agricultural_property_title | 
| 31 | land - farmland - acre - acres - values | 452 | 31_land_farmland_acre_acres | 
| 32 | snail - snails - read - also - you | 511 | 32_snail_snails_read_also | 
| 33 | de - que - la - en - el | 302 | 33_de_que_la_en | 
| 34 | antibiotics - antibiotic - pigs - animal - animals | 375 | 34_antibiotics_antibiotic_pigs_animal | 
| 35 | blood - vitamin - health - cancer - broccoli | 402 | 35_blood_vitamin_health_cancer | 
| 36 | disney - rss - websites - turning - url | 449 | 36_disney_rss_websites_turning | 
| 37 | exports - milk - dairy - beef - year | 330 | 37_exports_milk_dairy_beef | 
| 38 | we - sheep - ewes - lambs - have | 552 | 38_we_sheep_ewes_lambs | 
| 39 | your - you - diet - soy - body | 500 | 39_your_you_diet_soy | 
| 40 | milking - mastitis - teat - cows - cow | 630 | 40_milking_mastitis_teat_cows | 
| 41 | farming - nfu - scotland - the - will | 290 | 41_farming_nfu_scotland_the | 
| 42 | my - was - his - he - it | 747 | 42_my_was_his_he | 
| 43 | ethanol - rail - fuel - e15 - biofuels | 956 | 43_ethanol_rail_fuel_e15 | 
| 44 | bees - species - bee - study - of | 348 | 44_bees_species_bee_study | 
| 45 | you - your - to - that - it | 366 | 45_you_your_to_that | 
| 46 | birds - avian - poultry - influenza - flu | 476 | 46_birds_avian_poultry_influenza | 
| 47 | swine - asf - disease - virus - fever | 274 | 47_swine_asf_disease_virus | 
| 48 | scheme - payments - welsh - bps - payment | 282 | 48_scheme_payments_welsh_bps | 
| 49 | tb - bovine - test - cattle - badger | 459 | 49_tb_bovine_test_cattle | 
| 50 | tax - business - be - insurance - or | 264 | 50_tax_business_be_insurance | 
| 51 | agriculture - agricultural - state - of - the | 541 | 51_agriculture_agricultural_state_of | 
| 52 | tenant - tenants - landlords - tenancy - scheme | 595 | 52_tenant_tenants_landlords_tenancy | 
| 53 | soil - carbon - water - crop - and | 302 | 53_soil_carbon_water_crop | 
| 54 | court - epa - rule - law - plaintiffs | 853 | 54_court_epa_rule_law | 
| 55 | litre - milk - price - dairy - arla | 408 | 55_litre_milk_price_dairy | 
| 56 | feeder - pounds - steers - cattle - week | 259 | 56_feeder_pounds_steers_cattle | 
| 57 | you - they - chicken - that - them | 202 | 57_you_they_chicken_that | 
| 58 | autonomous - robots - robot - technology - the | 417 | 58_autonomous_robots_robot_technology | 
| 59 | wool - micron - merino - cargill - strike | 300 | 59_wool_micron_merino_cargill | 
| 60 | agree - cookies - website - privacy - analytics | 201 | 60_agree_cookies_website_privacy | 
| 61 | farm - you - her - your - to | 231 | 61_farm_you_her_your | 
| 62 | statements - forward - looking - company - uncertainties | 534 | 62_statements_forward_looking_company | 
| 63 | woodland - trees - forestry - carbon - planting | 221 | 63_woodland_trees_forestry_carbon | 
| 64 | protein - oz - powder - ends - powders | 285 | 64_protein_oz_powder_ends | 
| 65 | closed - at - hogs - down - cents | 203 | 65_closed_at_hogs_down | 
| 66 | link - place - related - services - agric4profits | 226 | 66_link_place_related_services | 
| 67 | levy - ahdb - payers - vote - growers | 344 | 67_levy_ahdb_payers_vote | 
| 68 | of - the - in - were - was | 186 | 68_of_the_in_were | 
| 69 | sugar - beet - growers - yellows - british | 182 | 69_sugar_beet_growers_yellows | 
| 70 | ukraine - food - fertiliser - prices - cf | 172 | 70_ukraine_food_fertiliser_prices | 
| 71 | head - slaughter - average - cattle - volumes | 363 | 71_head_slaughter_average_cattle | 
| 72 | urban - agriculture - food - gardens - community | 176 | 72_urban_agriculture_food_gardens | 
| 73 | insects - insect - larvae - fly - honey | 249 | 73_insects_insect_larvae_fly | 
| 74 | pork - the - covid - survey - of | 176 | 74_pork_the_covid_survey | 
| 75 | meat - protein - based - plant - food | 399 | 75_meat_protein_based_plant | 
| 76 | campaign - wild - boar - pigs - dairy | 287 | 76_campaign_wild_boar_pigs | 
| 77 | party - bill - minister - election - liberal | 183 | 77_party_bill_minister_election | 
| 78 | silage - hay - grass - forage - feed | 194 | 78_silage_hay_grass_forage | 
| 79 | school - food - schools - meals - snap | 285 | 79_school_food_schools_meals | 
| 80 | meal - creditor - collateral - debtor - creditors | 231 | 80_meal_creditor_collateral_debtor | 
| 81 | organic - black - farmers - veterans - program | 142 | 81_organic_black_farmers_veterans | 
| 82 | rabbit - rabbits - deere - uaw - they | 191 | 82_rabbit_rabbits_deere_uaw | 
| 83 | avian - influenza - birds - poultry - flocks | 108 | 83_avian_influenza_birds_poultry | 
| 84 | egg - eggs - free - range - cage | 162 | 84_egg_eggs_free_range |

</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: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 3.4.1
* Transformers: 4.51.1
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.11.11