Add BERTopic model

README.md

@@ -0,0 +1,78 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# BERTopic_Legal
+
+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("karinegabsschon/BERTopic_Legal")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 9
+* Number of training documents: 199
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | electric - vehicles - ev - electric vehicles - charging | 6 | -1_electric_vehicles_ev_electric vehicles | 
+| 0 | cars - vehicles - electric - car - parking | 58 | 0_cars_vehicles_electric_car | 
+| 1 | chinese - electric - byd - china - cars | 30 | 1_chinese_electric_byd_china | 
+| 2 | charging - charge - ev - public - electric | 27 | 2_charging_charge_ev_public | 
+| 3 | tesla - musk - dollars - elon - elon musk | 23 | 3_tesla_musk_dollars_elon | 
+| 4 | new - electric - vehicles - car - drivers | 21 | 4_new_electric_vehicles_car | 
+| 5 | porsche - taycan - car - electric - garage | 13 | 5_porsche_taycan_car_electric | 
+| 6 | foxconn - mitsubishi - japanese - nissan - electric | 11 | 6_foxconn_mitsubishi_japanese_nissan | 
+| 7 | nikola - bankruptcy - lucid - northvolt - assets | 10 | 7_nikola_bankruptcy_lucid_northvolt |
+
+</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: True
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 2.0.2
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.8
+* Pandas: 2.2.2
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 4.1.0
+* Transformers: 4.53.0
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.11.13

config.json

@@ -0,0 +1,17 @@
+{
+  "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": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null,
+  "embedding_model": "ibm-granite/granite-embedding-125m-english"
+}

ctfidf.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71de3ad1b706d356e05abe49e4522b6419827910d10656710cc75b8386fb5a0e
+size 794412

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76916c5f603c7dc08ee0c499bb020fa15cc30cb9b0e11e39009466b3d8f68d1c
+size 27736

topics.json

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+{
+  "topic_representations": {
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+      [
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+      [
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+        0.013853699776856142
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+      [
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+    "3": "3_tesla_musk_dollars_elon",
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+    "5": "5_porsche_taycan_car_electric",
+    "6": "6_foxconn_mitsubishi_japanese_nissan",
+    "7": "7_nikola_bankruptcy_lucid_northvolt"
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+  "topic_aspects": {
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