Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# bertopic_kmean-20topics
+
+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("hts98/bertopic_kmean-20topics")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 20
+* Number of training documents: 529579
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| 0 | hanoi - quarter - old - bay - lake | 70447 | 0_hanoi_quarter_old_bay | 
+| 1 | vietnam - vietnamese - best - stayed - mekong | 61694 | 1_vietnam_vietnamese_best_stayed | 
+| 2 | location - hotel - good - old - breakfast | 50809 | 2_location_hotel_good_old | 
+| 3 | good - clean - location - helpful - friendly | 44027 | 3_good_clean_location_helpful | 
+| 4 | pool - beach - view - massage - spa | 43959 | 4_pool_beach_view_massage | 
+| 5 | room - told - said - asked - shower | 40332 | 5_room_told_said_asked | 
+| 6 | thank - service - staff - ms - helpful | 36010 | 6_thank_service_staff_ms | 
+| 7 | hoi - homestay - town - bikes - free | 28816 | 7_hoi_homestay_town_bikes | 
+| 8 | saigon - minh - chi - ho - city | 28655 | 8_saigon_minh_chi_ho | 
+| 9 | resort - villa - beach - villas - island | 20536 | 9_resort_villa_beach_villas | 
+| 10 | bikes - beach - town - bike - free | 19495 | 10_bikes_beach_town_bike | 
+| 11 | hostel - dorm - dalat - dorms - beds | 17662 | 11_hostel_dorm_dalat_dorms | 
+| 12 | bay - halong - ha - cruise - kiem | 12629 | 12_bay_halong_ha_cruise | 
+| 13 | nang - da - danang - naman - dragon | 12005 | 13_nang_da_danang_naman | 
+| 14 | phu - quoc - resort - mui - ne | 9228 | 14_phu_quoc_resort_mui | 
+| 15 | hcmc - hcm - tau - vung - silverland | 8368 | 15_hcmc_hcm_tau_vung | 
+| 16 | phong - ninh - binh - nha - coc | 8121 | 16_phong_ninh_binh_nha | 
+| 17 | hue - citadel - imperial - jade - serene | 8072 | 17_hue_citadel_imperial_jade | 
+| 18 | nha - trang - sheraton - beach - russian | 6163 | 18_nha_trang_sheraton_beach | 
+| 19 | la - siesta - residencia - trendy - selva | 2551 | 19_la_siesta_residencia_trendy |
+  
+</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: 15
+* verbose: True
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 1.24.3
+* HDBSCAN: 0.8.33
+* UMAP: 0.5.5
+* Pandas: 2.0.3
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.2.2
+* Transformers: 4.35.2
+* Numba: 0.57.1
+* Plotly: 5.16.1
+* Python: 3.10.12

config.json

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+{
+  "calculate_probabilities": false,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": null,
+  "seed_topic_list": null,
+  "top_n_words": 15,
+  "verbose": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null
+}

ctfidf.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08d0081be03d6dd97c6afbc94e002fb6e4ef65170bf0b566bb7fc57fc958f77e
+size 6891752

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31f27b816e565a38b1a9e1c66b9936819be4bf1cad72f284087c35f7193c700c
+size 30808