Add BERTopic model

README.md

@@ -0,0 +1,73 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# recipecomments-bertopic
+
+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("daveripper0020/recipecomments-bertopic")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 6
+* Number of training documents: 386
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | 블로그 - 이형 - 많이 - 까지 - 항상 | 12 | -1_블로그_이형_많이_까지 | 
+| 0 | 잼민 - 춘장 - 인가요 - 뚝딱 - 너무 | 54 | 0_잼민_춘장_인가요_뚝딱 | 
+| 1 | 뚝딱 - 잼민 - 춘장 - 요리 - 수익 | 151 | 1_뚝딱_잼민_춘장_요리 | 
+| 2 | 찜닭 - 마늘 - 레시피 - 넣고 - 간장 | 77 | 2_찜닭_마늘_레시피_넣고 | 
+| 3 | 맛있어요 - 진짜 - 먹었는데 - 간단하고 - 너무 | 76 | 3_맛있어요_진짜_먹었는데_간단하고 | 
+| 4 | 감사합니다 - 합니다 - 자는 - 저리 - 믿는 | 16 | 4_감사합니다_합니다_자는_저리 |
+  
+</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: False
+
+## Framework versions
+
+* Numpy: 1.23.5
+* HDBSCAN: 0.8.33
+* UMAP: 0.5.4
+* Pandas: 1.5.3
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.2.2
+* Transformers: 4.35.1
+* Numba: 0.58.1
+* Plotly: 5.15.0
+* Python: 3.10.12

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": false,
+  "embedding_model": "sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens"
+}

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aac29d9764b0e971ce9a79b40a9c2e19ff76faae4d275edd46c44479747377ff
+size 18520

topics.json

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+}