Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# transformers_issues_topics
+
+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("asoria/transformers_issues_topics")
+
+topic_model.get_topic_info()
+```
+
+## Topic overview
+
+* Number of topics: 30
+* Number of training documents: 9000
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | Topic ID | Topic Keywords | Topic Frequency | Label | 
+|----------|----------------|-----------------|-------| 
+| -1 | pytorch - tensorflow - bert - tf - pretrained | 15 | -1_pytorch_tensorflow_bert_tf | 
+| 0 | bert - bertforsequenceclassification - berttokenizer - bart - batchencodeplus | 2321 | 0_bert_bertforsequenceclassification_berttokenizer_bart | 
+| 1 | cuda - memory - trainertrain - tensorflow - trainer | 1554 | 1_cuda_memory_trainertrain_tensorflow | 
+| 2 | transformerscli - transformers - transformer - importerror - transformerxl | 882 | 2_transformerscli_transformers_transformer_importerror | 
+| 3 | modelcard - modelcards - card - model - models | 490 | 3_modelcard_modelcards_card_model | 
+| 4 | gpt2 - gpt2tokenizer - gpt2xl - gpt2tokenizerfast - gpt2model | 462 | 4_gpt2_gpt2tokenizer_gpt2xl_gpt2tokenizerfast | 
+| 5 | attributeerror - typeerror - valueerror - runtimeerror - indexerror | 437 | 5_attributeerror_typeerror_valueerror_runtimeerror | 
+| 6 | typos - typo - doc - docstring - fix | 336 | 6_typos_typo_doc_docstring | 
+| 7 | t5 - t5model - t5base - tf - t5large | 298 | 7_t5_t5model_t5base_tf | 
+| 8 | readmemd - readmetxt - readme - modelcard - file | 270 | 8_readmemd_readmetxt_readme_modelcard | 
+| 9 | ci - testing - tests - test - speedup | 254 | 9_ci_testing_tests_test | 
+| 10 | s2s - s2sdistill - s2t - s2strainer - exampless2s | 245 | 10_s2s_s2sdistill_s2t_s2strainer | 
+| 11 | glue - gluepy - glueconvertexamplestofeatures - roberta - huggingfacetransformers | 214 | 11_glue_gluepy_glueconvertexamplestofeatures_roberta | 
+| 12 | ner - pipeline - pipelines - nerpipeline - fillmaskpipeline | 158 | 12_ner_pipeline_pipelines_nerpipeline | 
+| 13 | rag - ragtokenforgeneration - ragsequenceforgeneration - clean - tests | 153 | 13_rag_ragtokenforgeneration_ragsequenceforgeneration_clean | 
+| 14 | questionansweringpipeline - questionanswering - answering - tfalbertforquestionanswering - questionasnwering | 143 | 14_questionansweringpipeline_questionanswering_answering_tfalbertforquestionanswering | 
+| 15 | onnx - 04onnxexport - 04onnxexportipynb - aionnx - sphynx | 131 | 15_onnx_04onnxexport_04onnxexportipynb_aionnx | 
+| 16 | longformer - longformers - longform - longformerlayer - longformermodel | 104 | 16_longformer_longformers_longform_longformerlayer | 
+| 17 | labelsmoothednllloss - label - labelsmoothingfactor - labels - labelsmoothing | 76 | 17_labelsmoothednllloss_label_labelsmoothingfactor_labels | 
+| 18 | benchmark - benchmarking - benchmarks - accuracy - evaluation | 73 | 18_benchmark_benchmarking_benchmarks_accuracy | 
+| 19 | wav2vec2 - wav2vec - wav2vec20 - wav2vec2forctc - wav2vec2xlrswav2vec2 | 67 | 19_wav2vec2_wav2vec_wav2vec20_wav2vec2forctc | 
+| 20 | flax - flaxelectraformaskedlm - flaxelectraforpretraining - flaxjax - flaxelectramodel | 51 | 20_flax_flaxelectraformaskedlm_flaxelectraforpretraining_flaxjax | 
+| 21 | configpath - configs - config - configuration - modelconfigs | 49 | 21_configpath_configs_config_configuration | 
+| 22 | logging - logs - log - logger - loghistory | 40 | 22_logging_logs_log_logger | 
+| 23 | cachedir - cache - cachedpath - caching - cached | 38 | 23_cachedir_cache_cachedpath_caching | 
+| 24 | wandbproject - wandb - sagemaker - sagemakertrainer - wandbcallback | 36 | 24_wandbproject_wandb_sagemaker_sagemakertrainer | 
+| 25 | notebook - notebooks - community - colab - t5 | 33 | 25_notebook_notebooks_community_colab | 
+| 26 | electra - electrapretrainedmodel - electraformaskedlm - electraformultiplechoice - electrafortokenclassification | 30 | 26_electra_electrapretrainedmodel_electraformaskedlm_electraformultiplechoice | 
+| 27 | layoutlm - layout - layoutlmtokenizer - layoutlmbaseuncased - tf | 25 | 27_layoutlm_layout_layoutlmtokenizer_layoutlmbaseuncased | 
+| 28 | pplm - pr - deprecated - variable - ppl | 15 | 28_pplm_pr_deprecated_variable |
+  
+</details>
+
+## Training hyperparameters
+
+* calculate_probabilities: False
+* language: english
+* low_memory: False
+* min_topic_size: 10
+* n_gram_range: (1, 1)
+* nr_topics: 30
+* seed_topic_list: None
+* top_n_words: 10
+* verbose: True
+* zeroshot_min_similarity: 0.7
+* zeroshot_topic_list: None
+
+## Framework versions
+
+* Numpy: 1.26.4
+* HDBSCAN: 0.8.38.post1
+* UMAP: 0.5.6
+* Pandas: 2.1.4
+* Scikit-Learn: 1.5.2
+* Sentence-transformers: 3.1.1
+* Transformers: 4.44.2
+* Numba: 0.60.0
+* Plotly: 5.24.1
+* Python: 3.10.12

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": 30,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null,
+  "embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
+}

topic_embeddings.safetensors

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+size 46168