Add BERTopic model

README.md

@@ -0,0 +1,79 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# bertopic-turkish-political
+
+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("yeniguno/bertopic-turkish-political")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 10
+* Number of training documents: 45249
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | türkiye - halk - iktidar - muhalefet - seçmen | 281 | -1_türkiye_halk_iktidar_muhalefet | 
+| 0 | türkiye - atatürk - seçmen - siyaset - halk | 20262 | 0_türkiye_atatürk_seçmen_siyaset | 
+| 1 | olmak - iktidar - ekonomik - gerekmek - görüş | 12760 | 1_olmak_iktidar_ekonomik_gerekmek | 
+| 2 | seçim - seçmen - koalisyon - meclis - ittifak | 3395 | 2_seçim_seçmen_koalisyon_meclis | 
+| 3 | medya - milliyet - muhalefet - politik - faşist | 3065 | 3_medya_milliyet_muhalefet_politik | 
+| 4 | terör - şiddet - saldırı - terörist - toplum | 2437 | 4_terör_şiddet_saldırı_terörist | 
+| 5 | mahkeme - yargı - toplum - kavram - hukuk | 1641 | 5_mahkeme_yargı_toplum_kavram | 
+| 6 | seçim - meclis - endişe - sonuç - zor | 648 | 6_seçim_meclis_endişe_sonuç | 
+| 7 | köpek - sokak - park - protesto - taksi | 473 | 7_köpek_sokak_park_protesto | 
+| 8 | cinsiyet - lgbt - siyaset - cinsel - tecavüz | 287 | 8_cinsiyet_lgbt_siyaset_cinsel |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: None
+* low_memory: False
+* min_topic_size: 100
+* n_gram_range: (1, 1)
+* nr_topics: 10
+* 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: 2.2.2
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 3.3.1
+* Transformers: 4.47.1
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.11.11

config.json

@@ -0,0 +1,16 @@
+{
+  "calculate_probabilities": false,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 100,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": 10,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": false,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null
+}

ctfidf.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ba936671c051bff43e2563b78d199ab977145ddcc444e868c2d495bd56e72f2
+size 518488

topic_embeddings.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a8859f196dc4f722949ddaaf17347afd0ec13c722946cd7fd257c672f0e88169
+size 30808