
# bertopic_dcd_auto_final

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("vidric/bertopic_dcd_auto_final")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 40
* Number of training documents: 22195

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | wangi - tidak - banget - parfum - hmns | 12 | -1_wangi_tidak_banget_parfum | 
| 0 | wangi - mantap - banget - enak - suka | 7512 | 0_wangi_mantap_banget_enak | 
| 1 | alpha - farhampton - beli - saya - sama | 12436 | 1_alpha_farhampton_beli_saya | 
| 2 | hari - sampai - kirim - minggu - pesan | 903 | 2_hari_sampai_kirim_minggu | 
| 3 | order - repeat - kali - kesekian - bakal | 177 | 3_order_repeat_kali_kesekian | 
| 4 | kalem - nyengat - wangi - enak - banget | 118 | 4_kalem_nyengat_wangi_enak | 
| 5 | indonesia - bangga - produk - terus - harumkan | 70 | 5_indonesia_bangga_produk_terus | 
| 6 | kualitas - bagus - produk - barang - baik | 63 | 6_kualitas_bagus_produk_barang | 
| 7 | selamat - mendarat - dengan - barang - sampai | 61 | 7_selamat_mendarat_dengan_barang | 
| 8 | respon - cepat - quick - good - jawab | 59 | 8_respon_cepat_quick_good | 
| 9 | produk - bagus - sangat - penawaran - oke | 54 | 9_produk_bagus_sangat_penawaran | 
| 10 | pokok - mantap - puas - jon - epic | 49 | 10_pokok_mantap_puas_jon | 
| 11 | voucher - 50rb - 50k - dapat - 50 | 44 | 11_voucher_50rb_50k_dapat | 
| 12 | hadiah - buat - semoga - dia - cocok | 39 | 12_hadiah_buat_semoga_dia | 
| 13 | test - tester - mari - kita - coba | 35 | 13_test_tester_mari_kita | 
| 14 | deskripsi - sesuai - barang - dengan - bagus | 34 | 14_deskripsi_sesuai_barang_dengan | 
| 15 | worth - it - harga - sepadan - serius | 33 | 15_worth_it_harga_sepadan | 
| 16 | chat - admin - barang - balas - respon | 33 | 16_chat_admin_barang_balas | 
| 17 | 10ml - 10 - ml - 50ml - beli | 32 | 17_10ml_10_ml_50ml | 
| 18 | layanan - service - baik - barang - oke | 31 | 18_layanan_service_baik_barang | 
| 19 | 10 - silage - kedepanya - mayanlah - kemasin | 30 | 19_10_silage_kedepanya_mayanlah | 
| 20 | kartu - card - tulis - greting - ucap | 30 | 20_kartu_card_tulis_greting | 
| 21 | beli - 4x - bintang - kedua - pemesanan | 28 | 21_beli_4x_bintang_kedua | 
| 22 | bicara - bintang - biar - nyang - limo | 28 | 22_bicara_bintang_biar_nyang | 
| 23 | unik - istimewa - exceptional - addicted - cerita | 25 | 23_unik_istimewa_exceptional_addicted | 
| 24 | buka - kotak - box - belum - unboxing | 25 | 24_buka_kotak_box_belum | 
| 25 | akhirnya - bagi - dapat - juara - finaly | 21 | 25_akhirnya_bagi_dapat_juara | 
| 26 | starterpacking - semakin - gara - bantu - merosot | 20 | 26_starterpacking_semakin_gara_bantu | 
| 27 | review - orang - saja - zodiak - mag | 19 | 27_review_orang_saja_zodiak | 
| 28 | ketiga - kali - memuaskan - beli - selalu | 18 | 28_ketiga_kali_memuaskan_beli | 
| 29 | layanan - langsung - baru - cepat - service | 17 | 29_layanan_langsung_baru_cepat | 
| 30 | admin - ramah - tersampaikan - mantap - layanan | 17 | 30_admin_ramah_tersampaikan_mantap | 
| 31 | layanan - produk - bagus - service - terbaik | 17 | 31_layanan_produk_bagus_service | 
| 32 | notes - note - midlle - base - mbak | 16 | 32_notes_note_midlle_base | 
| 33 | bangga - lokal - pride - maszeh - kualitas | 16 | 33_bangga_lokal_pride_maszeh | 
| 34 | bintang - harum - kasih - lima - hmns | 16 | 34_bintang_harum_kasih_lima | 
| 35 | botol - kedua - tiga - sihir - ketiga | 15 | 35_botol_kedua_tiga_sihir | 
| 36 | 10 - voucher - hari - november - tanggal | 15 | 36_10_voucher_hari_november | 
| 37 | delta - team - deltanya - theta - senjata | 14 | 37_delta_team_deltanya_theta | 
| 38 | travel - praktis - kecil - ukuran - tepa | 13 | 38_travel_praktis_kecil_ukuran |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: auto
* seed_topic_list: None
* top_n_words: 10
* verbose: False

## Framework versions

* Numpy: 1.24.3
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 2.0.1
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.57.0
* Plotly: 5.14.1
* Python: 3.10.10

{"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"}
bertopic text-classification region:us
TAGS
#bertopic #text-classification #region-us 

bertopic\_dcd\_auto\_final
==========================


This is a 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:


You can use the model as follows:


Topic overview
--------------


* Number of topics: 40
* Number of training documents: 22195



Click here for an overview of all topics.


Training hyperparameters
------------------------


* calculate\_probabilities: True
* language: None
* low\_memory: False
* min\_topic\_size: 10
* n\_gram\_range: (1, 1)
* nr\_topics: auto
* seed\_topic\_list: None
* top\_n\_words: 10
* verbose: False


Framework versions
------------------


* Numpy: 1.24.3
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 2.0.1
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.57.0
* Plotly: 5.14.1
* Python: 3.10.10



TAGS
#bertopic #text-classification #region-us 
