Add BERTopic model

README.md

@@ -0,0 +1,75 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# rag-topic-model
+
+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("aaa961/rag-topic-model")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 6
+* Number of training documents: 168
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | to - my - klarna - for - the | 12 | -1_to_my_klarna_for | 
+| 0 | klarna - my - declined - in - for | 62 | 0_klarna_my_declined_in | 
+| 1 | my - details - klarna - and - call | 34 | 1_my_details_klarna_and | 
+| 2 | the - payment - for - to - pay | 24 | 2_the_payment_for_to | 
+| 3 | the - store - it - for - ago | 19 | 3_the_store_it_for | 
+| 4 | the - ago - sneakers - and - shoes | 17 | 4_the_ago_sneakers_and |
+
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* 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
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 1.26.4
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.7
+* Pandas: 2.3.0+4.g1dfc98e16a
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 3.1.1
+* Transformers: 4.42.2
+* Numba: 0.60.0
+* Plotly: 6.1.2
+* Python: 3.9.22

config.json

@@ -0,0 +1,16 @@
+{
+  "calculate_probabilities": false,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": "auto",
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": false,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null
+}

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:285573612da1923445777782408f8e2a36423c3909759bbd12ba58d70021af3d
+size 9304

topics.json

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+{
+  "topic_representations": {
+    "-1": [
+      [
+        "to",
+        0.062480953781426124
+      ],
+      [
+        "my",
+        0.05976113732434668
+      ],
+      [
+        "klarna",
+        0.05727940719918197
+      ],
+      [
+        "for",
+        0.054670834558747855
+      ],
+      [
+        "the",
+        0.047328587214639194
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+      [
+        "payment",
+        0.04590553230978397
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+      [
+        "card",
+        0.0431318606480702
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+      [
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+        0.04267127708146233
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+      [
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+      [
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+        0.06749832637087703
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+      [
+        "declined",
+        0.06685493723261081
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+      [
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+        0.06019350229402318
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+      [
+        "for",
+        0.05682281768605802
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+      [
+        "ive",
+        0.05278677705000323
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+      [
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+      [
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+      [
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+      [
+        "call",
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+      [
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+      [
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+        "card",
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
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+      [
+        "the",
+        0.08156485155775096
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+      [
+        "ago",
+        0.0725683994234641
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+      [
+        "sneakers",
+        0.06984358149368186
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+      [
+        "and",
+        0.06512511898179059
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+      [
+        "shoes",
+        0.06446649957008656
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+      [
+        "days",
+        0.061865947936947524
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+      [
+        "adidas",
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+      [
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+      [
+        "ordered",
+        0.05820298457806822
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+      [
+        "on",
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+    ]
+  },
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+    "0": "0_klarna_my_declined_in",
+    "1": "1_my_details_klarna_and",
+    "2": "2_the_payment_for_to",
+    "3": "3_the_store_it_for",
+    "4": "4_the_ago_sneakers_and"
+  },
+  "custom_labels": null,
+  "_outliers": 1,
+  "topic_aspects": {}
+}