Add BERTopic model

README.md

@@ -0,0 +1,73 @@
+
+---
+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("przadka/rag-topic-model")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 4
+* Number of training documents: 203
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | on - card - my - charge - account | 54 | -1_on_card_my_charge | 
+| 0 | refund - my - nike - for - store | 16 | 0_refund_my_nike_for | 
+| 1 | to - my - klarna - email - and | 77 | 1_to_my_klarna_email | 
+| 2 | my - the - payment - klarna - for | 56 | 2_my_the_payment_klarna |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* 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
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 2.1.3
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.7
+* Pandas: 2.2.3
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 3.1.1
+* Transformers: 4.45.2
+* Numba: 0.61.0
+* Plotly: 6.0.0
+* Python: 3.10.12

config.json

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+{
+  "calculate_probabilities": false,
+  "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,
+  "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:bf46216928afa925d87b82e1f156bffc22e3440931a9e2373ea5a1abfdfdad99
+size 6232

topics.json

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+{
+  "topic_representations": {
+    "-1": [
+      [
+        "on",
+        0.09163203304952545
+      ],
+      [
+        "card",
+        0.08064970389745767
+      ],
+      [
+        "my",
+        0.0767006922807348
+      ],
+      [
+        "charge",
+        0.06628677938756693
+      ],
+      [
+        "account",
+        0.06147155751770172
+      ],
+      [
+        "onetime",
+        0.06048261659107499
+      ],
+      [
+        "textbooks",
+        0.06048261659107499
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+      [
+        "for",
+        0.056879211846449326
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+      [
+        "it",
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+      [
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+        0.05263266076836004
+      ]
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+    "0": [
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+        "refund",
+        0.10357181740806075
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+      [
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+        0.07945375060858025
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+      [
+        "nike",
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+      [
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+        0.078402687188764
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+      [
+        "store",
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+      [
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+      [
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+      [
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+      [
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+      [
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+    "0": "0_refund_my_nike_for",
+    "1": "1_to_my_klarna_email",
+    "2": "2_my_the_payment_klarna"
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+  "_outliers": 1,
+  "topic_aspects": {}
+}