Add BERTopic model

README.md

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+
+---
+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

config.json

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

ctfidf.safetensors

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+oid sha256:6e7d0d77830e8786c6a4b877939cd10ec2aa43b6bdde1017124138068482f045
+size 415780

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:850949e2d22caf8a0fb9afb425c72af98c72dc92b4a18441d2da72a0f58deba3
+size 15448