Add BERTopic model
Browse files- README.md +89 -0
- config.json +16 -0
- ctfidf.safetensors +3 -0
- ctfidf_config.json +0 -0
- topic_embeddings.safetensors +3 -0
- topics.json +0 -0
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_kmean-20topics
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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("hts98/bertopic_kmean-20topics")
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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: 20
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* Number of training documents: 529579
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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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| 0 | hanoi - quarter - old - bay - lake | 70447 | 0_hanoi_quarter_old_bay |
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| 1 | vietnam - vietnamese - best - stayed - mekong | 61694 | 1_vietnam_vietnamese_best_stayed |
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| 2 | location - hotel - good - old - breakfast | 50809 | 2_location_hotel_good_old |
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| 3 | good - clean - location - helpful - friendly | 44027 | 3_good_clean_location_helpful |
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| 4 | pool - beach - view - massage - spa | 43959 | 4_pool_beach_view_massage |
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| 5 | room - told - said - asked - shower | 40332 | 5_room_told_said_asked |
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| 6 | thank - service - staff - ms - helpful | 36010 | 6_thank_service_staff_ms |
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| 7 | hoi - homestay - town - bikes - free | 28816 | 7_hoi_homestay_town_bikes |
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| 8 | saigon - minh - chi - ho - city | 28655 | 8_saigon_minh_chi_ho |
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| 9 | resort - villa - beach - villas - island | 20536 | 9_resort_villa_beach_villas |
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| 10 | bikes - beach - town - bike - free | 19495 | 10_bikes_beach_town_bike |
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| 11 | hostel - dorm - dalat - dorms - beds | 17662 | 11_hostel_dorm_dalat_dorms |
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| 12 | bay - halong - ha - cruise - kiem | 12629 | 12_bay_halong_ha_cruise |
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| 13 | nang - da - danang - naman - dragon | 12005 | 13_nang_da_danang_naman |
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| 14 | phu - quoc - resort - mui - ne | 9228 | 14_phu_quoc_resort_mui |
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| 15 | hcmc - hcm - tau - vung - silverland | 8368 | 15_hcmc_hcm_tau_vung |
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| 16 | phong - ninh - binh - nha - coc | 8121 | 16_phong_ninh_binh_nha |
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| 17 | hue - citadel - imperial - jade - serene | 8072 | 17_hue_citadel_imperial_jade |
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| 18 | nha - trang - sheraton - beach - russian | 6163 | 18_nha_trang_sheraton_beach |
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| 19 | la - siesta - residencia - trendy - selva | 2551 | 19_la_siesta_residencia_trendy |
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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: 15
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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: 1.24.3
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* HDBSCAN: 0.8.33
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* UMAP: 0.5.5
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* Pandas: 2.0.3
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* Scikit-Learn: 1.2.2
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* Sentence-transformers: 2.2.2
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* Transformers: 4.35.2
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* Numba: 0.57.1
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* Plotly: 5.16.1
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* Python: 3.10.12
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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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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 15,
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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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}
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ctfidf.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:08d0081be03d6dd97c6afbc94e002fb6e4ef65170bf0b566bb7fc57fc958f77e
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size 6891752
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ctfidf_config.json
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:31f27b816e565a38b1a9e1c66b9936819be4bf1cad72f284087c35f7193c700c
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size 30808
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topics.json
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