Add BERTopic model

README.md

@@ -0,0 +1,83 @@
+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# bal_arxiv_scientific_paps_berttopic_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("Rchamba/bal_arxiv_scientific_paps_berttopic_model")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 14
+* Number of training documents: 360
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | data - steganography - secret - probability - method | 13 | -1_data_steganography_secret_probability | 
+| 0 | sp - intelligence - processing - human - image | 35 | 0_sp_intelligence_processing_human | 
+| 1 | quantum - automata - finite - classical - measurement | 64 | 1_quantum_automata_finite_classical | 
+| 2 | problems - complexity - constraints - symmetry - csps | 37 | 2_problems_complexity_constraints_symmetry | 
+| 3 | logic - computability - cl - edu - www | 25 | 3_logic_computability_cl_edu | 
+| 4 | science - citation - journals - social - communication | 24 | 4_science_citation_journals_social | 
+| 5 | tetraquark - vector - bar - rm - qcd | 23 | 5_tetraquark_vector_bar_rm | 
+| 6 | combinatorial - problems - design - problem - clustering | 22 | 6_combinatorial_problems_design_problem | 
+| 7 | prediction - entropy - model - universal - cc | 22 | 7_prediction_entropy_model_universal | 
+| 8 | notes - informal - spaces - analysis - metric | 21 | 8_notes_informal_spaces_analysis | 
+| 9 | orbital - earth - postnewtonian - effects - artificial | 21 | 9_orbital_earth_postnewtonian_effects | 
+| 10 | keyphrases - word - algorithm - semantic - similarity | 20 | 10_keyphrases_word_algorithm_semantic | 
+| 11 | kernel - gmm - kernels - datasets - classification | 17 | 11_kernel_gmm_kernels_datasets | 
+| 12 | data - ultrametric - ultrametricity - analysis - application | 16 | 12_data_ultrametric_ultrametricity_analysis |
+
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: True
+* 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
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 2.0.2
+* HDBSCAN: 0.8.40
+* UMAP: 0.5.7
+* Pandas: 2.2.2
+* Scikit-Learn: 1.6.1
+* Sentence-transformers: 4.1.0
+* Transformers: 4.52.4
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.11.13

config.json

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+{
+  "calculate_probabilities": true,
+  "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,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null,
+  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
+}

ctfidf.safetensors

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+oid sha256:4d9ff73a8200cb61d07e1cde175f7f524864b9ec86379c39cbada15c92c47fb7
+size 163624

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:97b3539dbc450f882fbe24e019319ede33da266408e799d473892717e6679084
+size 21592

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