Add BERTopic model
Browse files- README.md +107 -0
- config.json +15 -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_dcd_auto_final
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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("vidric/bertopic_dcd_auto_final")
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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: 40
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* Number of training documents: 22195
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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 | wangi - tidak - banget - parfum - hmns | 12 | -1_wangi_tidak_banget_parfum |
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| 0 | wangi - mantap - banget - enak - suka | 7512 | 0_wangi_mantap_banget_enak |
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| 1 | alpha - farhampton - beli - saya - sama | 12436 | 1_alpha_farhampton_beli_saya |
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| 2 | hari - sampai - kirim - minggu - pesan | 903 | 2_hari_sampai_kirim_minggu |
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| 3 | order - repeat - kali - kesekian - bakal | 177 | 3_order_repeat_kali_kesekian |
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| 4 | kalem - nyengat - wangi - enak - banget | 118 | 4_kalem_nyengat_wangi_enak |
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| 5 | indonesia - bangga - produk - terus - harumkan | 70 | 5_indonesia_bangga_produk_terus |
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| 6 | kualitas - bagus - produk - barang - baik | 63 | 6_kualitas_bagus_produk_barang |
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| 7 | selamat - mendarat - dengan - barang - sampai | 61 | 7_selamat_mendarat_dengan_barang |
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| 8 | respon - cepat - quick - good - jawab | 59 | 8_respon_cepat_quick_good |
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| 9 | produk - bagus - sangat - penawaran - oke | 54 | 9_produk_bagus_sangat_penawaran |
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| 10 | pokok - mantap - puas - jon - epic | 49 | 10_pokok_mantap_puas_jon |
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| 11 | voucher - 50rb - 50k - dapat - 50 | 44 | 11_voucher_50rb_50k_dapat |
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| 12 | hadiah - buat - semoga - dia - cocok | 39 | 12_hadiah_buat_semoga_dia |
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| 13 | test - tester - mari - kita - coba | 35 | 13_test_tester_mari_kita |
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| 14 | deskripsi - sesuai - barang - dengan - bagus | 34 | 14_deskripsi_sesuai_barang_dengan |
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| 15 | worth - it - harga - sepadan - serius | 33 | 15_worth_it_harga_sepadan |
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| 16 | chat - admin - barang - balas - respon | 33 | 16_chat_admin_barang_balas |
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| 17 | 10ml - 10 - ml - 50ml - beli | 32 | 17_10ml_10_ml_50ml |
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| 18 | layanan - service - baik - barang - oke | 31 | 18_layanan_service_baik_barang |
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| 19 | 10 - silage - kedepanya - mayanlah - kemasin | 30 | 19_10_silage_kedepanya_mayanlah |
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| 20 | kartu - card - tulis - greting - ucap | 30 | 20_kartu_card_tulis_greting |
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| 21 | beli - 4x - bintang - kedua - pemesanan | 28 | 21_beli_4x_bintang_kedua |
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| 22 | bicara - bintang - biar - nyang - limo | 28 | 22_bicara_bintang_biar_nyang |
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| 23 | unik - istimewa - exceptional - addicted - cerita | 25 | 23_unik_istimewa_exceptional_addicted |
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| 24 | buka - kotak - box - belum - unboxing | 25 | 24_buka_kotak_box_belum |
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| 25 | akhirnya - bagi - dapat - juara - finaly | 21 | 25_akhirnya_bagi_dapat_juara |
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| 26 | starterpacking - semakin - gara - bantu - merosot | 20 | 26_starterpacking_semakin_gara_bantu |
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| 27 | review - orang - saja - zodiak - mag | 19 | 27_review_orang_saja_zodiak |
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| 28 | ketiga - kali - memuaskan - beli - selalu | 18 | 28_ketiga_kali_memuaskan_beli |
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| 29 | layanan - langsung - baru - cepat - service | 17 | 29_layanan_langsung_baru_cepat |
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| 30 | admin - ramah - tersampaikan - mantap - layanan | 17 | 30_admin_ramah_tersampaikan_mantap |
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| 31 | layanan - produk - bagus - service - terbaik | 17 | 31_layanan_produk_bagus_service |
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| 32 | notes - note - midlle - base - mbak | 16 | 32_notes_note_midlle_base |
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| 33 | bangga - lokal - pride - maszeh - kualitas | 16 | 33_bangga_lokal_pride_maszeh |
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| 34 | bintang - harum - kasih - lima - hmns | 16 | 34_bintang_harum_kasih_lima |
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| 35 | botol - kedua - tiga - sihir - ketiga | 15 | 35_botol_kedua_tiga_sihir |
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| 36 | 10 - voucher - hari - november - tanggal | 15 | 36_10_voucher_hari_november |
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| 37 | delta - team - deltanya - theta - senjata | 14 | 37_delta_team_deltanya_theta |
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| 38 | travel - praktis - kecil - ukuran - tepa | 13 | 38_travel_praktis_kecil_ukuran |
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</details>
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## Training hyperparameters
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* calculate_probabilities: True
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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: auto
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: False
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## Framework versions
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* Numpy: 1.24.3
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* HDBSCAN: 0.8.29
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* UMAP: 0.5.3
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* Pandas: 2.0.1
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* Scikit-Learn: 1.2.2
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* Sentence-transformers: 2.2.2
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* Transformers: 4.29.2
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* Numba: 0.57.0
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* Plotly: 5.14.1
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* Python: 3.10.10
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config.json
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{
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"calculate_probabilities": true,
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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": "auto",
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": false,
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"embedding_model": "distiluse-base-multilingual-cased"
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}
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:384e303e83f39738a026b16e499a02f6c7152531b0d89f2a6dc06cc737886ec2
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size 82008
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topics.json
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