Hugging Face's logo

shantanudave
/
TopicModel_StoreReviews

Text Classification
BERTopic
Model card Files Community
TopicModel_StoreReviews
File size: 2,284 Bytes 
c7ed6cc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
 

---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# TopicModel_StoreReviews

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("shantanudave/TopicModel_StoreReviews")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 10
* Number of training documents: 14747

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | clothing - clothes - fashion - clothe - clothing store | 2672 | Fashionable Clothing Selection | 
| 1 | shopping - shop - price - cheap - store | 1864 | Diverse Shopping Experiences | 
| 2 | tidy - clean - branch - range - renovation | 1807 | Clean Retail Space | 
| 3 | quality - offer - use - stop - good | 1793 | Quality Offer Search | 
| 4 | selection - choice - large - large selection - size | 1459 | Large Size Selection | 
| 5 | advice - saleswoman - service - friendly - competent | 1447 | Friendly Saleswoman Service | 
| 6 | staff - friendly staff - staff staff - staff friendly - friendly | 1177 | Friendly Staff Selection | 
| 7 | wow - waw - oh - yeah -  | 1108 | Expressive Words Discovery | 
| 8 | voucher - money - return - exchange - cash | 933 | Customer Return Experience | 
| 9 | super - friendly super - super friendly - pleasure - super service | 487 | super friendly service |
  
</details>

## Training hyperparameters

* calculate_probabilities: True
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.23.5
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 1.3.5
* Scikit-Learn: 1.4.1.post1
* Sentence-transformers: 2.6.1
* Transformers: 4.39.3
* Numba: 0.59.1
* Plotly: 5.21.0
* Python: 3.10.13