Add BERTopic model
Browse files- .gitattributes +1 -0
- README.md +149 -0
- config.json +17 -0
- ctfidf.safetensors +3 -0
- ctfidf_config.json +3 -0
- topic_embeddings.safetensors +3 -0
- topics.json +0 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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ctfidf_config.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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tags:
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- bertopic
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library_name: bertopic
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pipeline_tag: text-classification
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---
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# BERTopic_6_months
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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## Usage
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To use this model, please install BERTopic:
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```
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pip install -U bertopic
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```
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You can use the model as follows:
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```python
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from bertopic import BERTopic
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topic_model = BERTopic.load("karinegabsschon/BERTopic_6_months")
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topic_model.get_topic_info()
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```
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## Topic overview
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* Number of topics: 80
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* Number of training documents: 5537
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<details>
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<summary>Click here for an overview of all topics.</summary>
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| Topic ID | Topic Keywords | Topic Frequency | Label |
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|----------|----------------|-----------------|-------|
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| -1 | electric - vehicles - car - cars - ev | 21 | electric cars | electric vehicles | electric car |
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| 0 | grant - scheme - car grant - government - 750 | 1297 | electric vehicles | electric cars | electric car |
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| 1 | charging - chargers - public - ev - drivers | 216 | ev charging | charging infrastructure | ev chargers |
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| 2 | tesla - musk - model - company - elon | 215 | tesla shares | tesla sales | tesla nasdaq |
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| 3 | uk - government - zero - mandate - targets | 153 | diesel cars | electric cars | electric vehicles |
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| 4 | tesla - sales - musk - tesla sales - year | 113 | tesla sales | tesla market | year tesla |
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| 5 | kia - range - car - hyundai - electric | 108 | kia ev9 | ev | kia |
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| 6 | xiaomi - yu7 - su7 - china - chinese | 107 | xiaomi yu7 | xiaomi electric | xiaomi su7 |
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| 7 | tax - car tax - car - rates - pay | 106 | car tax | electric vehicles | electric car |
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| 8 | tax - spd - purchase - coalition - funding | 104 | purchase incentives | purchase premium | purchase premiums |
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| 9 | charging - energy - solutions - ev charging - ev | 98 | ev charging | ev charger | charging infrastructure |
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| 10 | charging - romania - abstract - electric - charging points | 98 | ev charging | charging infrastructure | charging points |
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| 11 | toyota - honda - subaru - japanese - nissan | 96 | electric vehicles | electric cars | electric car |
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| 12 | battery - batteries - catl - lithium - solid | 89 | ev battery | ev batteries | battery technology |
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| 13 | byd - charging - minutes - fast - han | 85 | megawatt charging | fast charging | charging times |
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| 14 | charging - stations - charging stations - charging points - points | 84 | charging infrastructure | charging cars | charging stations |
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| 15 | fires - battery - safety - police - car | 83 | ev fires | battery fires | car fires |
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| 16 | byd - tesla - sales - chinese - year | 80 | tesla sales | overtakes tesla | tesla |
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| 17 | ford - farley - company - platform - kentucky | 78 | ford ceo | ford | ford motor |
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| 18 | india - indian - ev - tata - mobility | 75 | tata motors | tesla | tata |
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| 19 | ukraine - units - used - region - kyiv | 72 | vehicles ukraine | electric vehicles | electric cars |
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| 20 | percent - cars - registrations - germany - registered | 66 | electric cars | cars germany | registered cars |
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| 21 | vinfast - vf - vietnam - green - philippines | 63 | vinfast auto | vinfast vf | vehicle ev |
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| 22 | used - used car - used electric - percent - battery | 62 | electric cars | electric vehicles | car market |
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| 23 | new - registrations - new car - sales - year | 59 | ev sales | electric cars | electric car |
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| 24 | id - volkswagen - every1 - vw - id every1 | 58 | volkswagen id | electric car | vw wants |
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| 25 | mercedes - cla - new - range - eqs | 56 | mercedes cla | cla ev | new mercedes |
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| 26 | emissions - electricity - study - cars - combustion | 53 | electric cars | electric car | electric vehicles |
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| 27 | tariffs - eu - china - mexico - trade | 51 | tariffs chinese | car tariffs | additional tariffs |
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| 28 | car - charge - miles - ve - trip | 51 | electric car | electric cars | ev |
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| 29 | byd - chinese - price war - price - war | 49 | chinese car | china electric | chinese electric |
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| 30 | rivian - r2 - nasdaq - rivn - scaringe | 48 | rivian automotive | ev stocks | rivian |
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| 31 | charging - stations - charging stations - terminals - station | 47 | charging stations | electric vehicles | electric car |
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| 32 | percent - registrations - eu - cars - year | 46 | electric cars | electric vehicles | european car |
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| 33 | licensed - cars licensed - czech - czech republic - republic | 44 | electric cars | electric vehicles | cars licensed |
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| 34 | hungary - byd - huf - chinese - serbia | 41 | chinese electric | china xplained | hungary |
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| 35 | moves - aid - moves iii - iii - euros | 40 | electric vehicles | electric vehicle | electric car |
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| 36 | china - shanghai - chinese - market - car | 40 | chinese automotive | shanghai auto | china auto |
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| 37 | stellantis - ferrari - fiat - lamborghini - italian | 40 | electric car | 500 hybrid | electric vehicle |
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| 38 | indonesia - lg - alpine - battery - ev battery | 39 | indonesia battery | indonesia ev | ev battery |
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| 39 | tesla - musk - black - elon - tsla | 39 | tesla | tesla nasdaq | elon musk |
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| 40 | foxconn - mitsubishi - nissan - japanese - mitsubishi motors | 39 | japanese automaker | taiwanese electronics | taiwan foxconn |
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| 41 | credit - tax - trump - tax credit - states | 38 | electric vehicle | electric vehicles | vehicle credit |
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| 42 | storage - electricity - energy - batteries - grid | 38 | energy storage | ev batteries | battery storage |
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| 43 | european - commission - eu - industry - emissions | 37 | european automotive | european car | electric vehicles |
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| 44 | dolphin - surf - dolphin surf - byd - euros | 37 | dolphin surf | byd dolphin | dolphin |
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| 45 | german - vw - place - market - brands | 37 | german car | vw dominates | bmw |
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| 46 | russia - russian - stations - electric - moscow | 37 | russia electric | russian electric | electric cars |
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| 47 | slate - truck - northvolt - bezos - slate auto | 36 | slate truck | electric vehicle | tesla |
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| 48 | audi - porsche - döllner - cellforce - tt | 36 | audi | audi tt | audi ceo |
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| 49 | bonus - euros - ecological - aid - energy | 36 | purchase electric | electric car | electric vehicles |
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| 50 | percent - cars - city - registered - vienna | 35 | electric cars | new cars | electric mobility |
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| 51 | used - value - cent - used car - prices | 34 | ev values | electric cars | electric vehicles |
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| 52 | points - spain - charging points - charging - indicator | 33 | charging infrastructure | charging points | charging network |
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| 53 | gm - general motors - general - motors - equinox | 33 | ev gm | gm electric | chevrolet equinox |
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| 54 | million - sales - china - global - electric | 33 | ev market | electric cars | electric car |
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| 55 | nio - firefly - onvo - chinese - li | 31 | nio tesla | tesla chinese | tesla |
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| 56 | california - trump - federal - states - vehicle | 31 | california electric | electric vehicles | electric vehicle |
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| 57 | lucid - nikola - lcid - uber - nasdaq | 30 | lucid shares | vehicles lucid | nasdaq lcid |
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| 58 | breakdown - barriers - cent - adac - breakdowns | 30 | electric cars | electric vehicles | engine vehicles |
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| 59 | nissan - leaf - micra - generation - new | 29 | nissan leaf | generation nissan | electric car |
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| 60 | uzbekistan - kazakhstan - proton - stations - tashkent | 28 | uzbekistan | tashkent region | electric vehicles |
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| 61 | range - kilometers - test - km - real | 28 | electric car | electric cars | efficient electric |
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| 62 | french - france - electric - centrale - la centrale | 27 | electric cars | electric car | french car |
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| 63 | xpeng - p7 - ai - g6 - chinese | 27 | xpeng g6 | xpeng p7 | xpeng x9 |
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| 64 | belarus - belgee - charging - belorusneft - stations | 26 | belarus electric | vehicles belarus | infrastructure belarus |
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| 65 | volkswagen - group - vw - europe - deliveries | 26 | germany volkswagen | volkswagen | volkswagen group |
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| 66 | tesla - musk - protests - elon - elon musk | 26 | tesla takedown | tesla electric | tesla cybertruck |
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| 67 | pod - pod point - edf - point - owen | 26 | pod point | ev charging | pod drive |
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| 68 | combustion - price - discounts - gap - dudenhöffer | 26 | electric cars | price gap | electric car |
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| 69 | cost - costs - charge - price cap - cap | 25 | charge ev | energy price | electric car |
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| 70 | recycling - batteries - battery - battery recycling - lithium | 25 | battery recycling | ev batteries | ev battery |
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| 71 | record - kilometers - lucid - amg - mercedes | 25 | electric car | longest distance | car travels |
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| 72 | leapmotor - c10 - stellantis - t03 - grant | 25 | leapmotor uk | leapmotor vehicles | leapmotor electric |
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| 73 | percent - survey - respondents - car - cars | 24 | electric car | electric cars | buy electric |
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| 74 | renault - r5 - twingo - r4 - car | 24 | renault r5 | electric car | new renault |
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| 75 | musk - trump - elon - elon musk - president | 23 | tesla | trump musk | elon musk |
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| 76 | mg - s5 - mg4 - cyberster - s5 ev | 22 | mgs5 ev | s5 ev | mg s5 |
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| 77 | spain - units - year - electrified - month | 22 | electric cars | cars spain | sales electric |
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| 78 | london - congestion - congestion charge - tfl - charge | 22 | exemption evs | london congestion | congestion charge |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: None
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* low_memory: False
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* min_topic_size: 10
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* n_gram_range: (1, 1)
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* nr_topics: None
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: True
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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 2.0.2
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* HDBSCAN: 0.8.40
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* UMAP: 0.5.9.post2
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* Pandas: 2.2.2
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* Scikit-Learn: 1.6.1
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* Sentence-transformers: 5.1.0
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* Transformers: 4.55.4
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* Numba: 0.60.0
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* Plotly: 5.24.1
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* Python: 3.12.11
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config.json
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{
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"calculate_probabilities": false,
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"language": null,
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"low_memory": false,
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"min_topic_size": 10,
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"n_gram_range": [
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1,
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1
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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": true,
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"zeroshot_min_similarity": 0.7,
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"zeroshot_topic_list": null,
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"embedding_model": "ibm-granite/granite-embedding-125m-english"
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}
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ctfidf.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:89524b23f41fb41a014b29da4bff19c1b9c53a77fcd41719f6999c2ca6c96181
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size 16170388
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ctfidf_config.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:cb9f3740c3ba8dcf8e59920473990450cc68bfa30181c06a87fe81f6b531bab5
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size 18773339
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:dd0bc860513f52059d03d935ec0eea82e79e17b62f2a81ac916b284f69d67c08
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size 245848
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topics.json
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