Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# testmodel1
+
+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("baseball46245/testmodel1")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 1
+* Number of training documents: 17
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | the - of - and - ai - to | 17 | -1_the_of_and_ai |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: english
+* 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: 1.24.3
+* HDBSCAN: 0.8.29
+* UMAP: 0.5.3
+* Pandas: 2.0.2
+* Scikit-Learn: 1.2.2
+* Sentence-transformers: 2.2.2
+* Transformers: 4.30.1
+* Numba: 0.57.0
+* Plotly: 5.15.0
+* Python: 3.10.6

config.json

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+{
+  "calculate_probabilities": false,
+  "language": "english",
+  "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/all-MiniLM-L6-v2"
+}

topic_embeddings.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a233babb3c7237bf6a63110f243f2644100ab4389cbec4fabe2be64c8d1d17f8
+size 1624

topics.json

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+{
+  "topic_representations": {
+    "-1": [
+      [
+        "the",
+        0.12954182226104918
+      ],
+      [
+        "of",
+        0.11953036950713802
+      ],
+      [
+        "and",
+        0.10904205253037412
+      ],
+      [
+        "ai",
+        0.105428448776221
+      ],
+      [
+        "to",
+        0.09030216703646363
+      ],
+      [
+        "in",
+        0.0822788017602565
+      ],
+      [
+        "data",
+        0.07389214731356038
+      ],
+      [
+        "not",
+        0.050894520347419245
+      ],
+      [
+        "is",
+        0.050894520347419245
+      ],
+      [
+        "its",
+        0.045842774645202373
+      ]
+    ]
+  },
+  "topics": [
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1,
+    -1
+  ],
+  "topic_sizes": {
+    "-1": 17
+  },
+  "topic_mapper": [
+    [
+      -1,
+      -1,
+      -1
+    ]
+  ],
+  "topic_labels": {
+    "-1": "-1_the_of_and_ai"
+  },
+  "custom_labels": null,
+  "_outliers": 1,
+  "topic_aspects": {}
+}