---
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("Ruslan10/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 | bert - tensorflow - pytorch - pretrained - models | 11 | -1_bert_tensorflow_pytorch_pretrained | 
| 0 | bertforsequenceclassification - encoderdecoder - berttokenizer - tokenizer - bert | 2042 | 0_bertforsequenceclassification_encoderdecoder_berttokenizer_tokenizer | 
| 1 | pytorch - tensorflow - modelingutilspy - tensors - tensor | 1903 | 1_pytorch_tensorflow_modelingutilspy_tensors | 
| 2 | seq2seqtrainer - seq2seq - testing - tests - prepareseq2seqbatch | 735 | 2_seq2seqtrainer_seq2seq_testing_tests | 
| 3 | docstring - docstrings - readmetxt - doc - readmemd | 603 | 3_docstring_docstrings_readmetxt_doc | 
| 4 | gpt2 - gpt2tokenizer - gpt2tokenizerfast - gpt - gpt2model | 513 | 4_gpt2_gpt2tokenizer_gpt2tokenizerfast_gpt | 
| 5 | trainertrain - trainer - trainers - training - evaluateduringtraining | 442 | 5_trainertrain_trainer_trainers_training | 
| 6 | modelcard - modelcards - card - model - models | 440 | 6_modelcard_modelcards_card_model | 
| 7 | albertforpretraining - xlnet - xlnetlmheadmodel - albertbasev2 - albertformaskedlm | 436 | 7_albertforpretraining_xlnet_xlnetlmheadmodel_albertbasev2 | 
| 8 | t5 - t5model - tf - t5base - tf2 | 359 | 8_t5_t5model_tf_t5base | 
| 9 | transformerscli - transformers - transformer - transformerxl - importerror | 259 | 9_transformerscli_transformers_transformer_transformerxl | 
| 10 | ner - pipeline - pipelines - nerpipeline - fillmaskpipeline | 197 | 10_ner_pipeline_pipelines_nerpipeline | 
| 11 | questionansweringpipeline - questionanswering - answering - tfalbertforquestionanswering - questionasnwering | 159 | 11_questionansweringpipeline_questionanswering_answering_tfalbertforquestionanswering | 
| 12 | longformer - longform - longformers - longformerlayer - longformerformultiplechoice | 135 | 12_longformer_longform_longformers_longformerlayer | 
| 13 | onnx - onnxonnxruntime - onnxexport - 04onnxexport - 04onnxexportipynb | 117 | 13_onnx_onnxonnxruntime_onnxexport_04onnxexport | 
| 14 | generationbeamsearchpy - generatebeamsearch - generatebeamsearchoutputs - beamsearch - nonbeamsearch | 95 | 14_generationbeamsearchpy_generatebeamsearch_generatebeamsearchoutputs_beamsearch | 
| 15 | benchmark - benchmarks - accuracy - evaluation - metrics | 90 | 15_benchmark_benchmarks_accuracy_evaluation | 
| 16 | huggingfacemaster - huggingfacetokenizers297 - huggingface - huggingfacetransformers - huggingfacetransformer | 83 | 16_huggingfacemaster_huggingfacetokenizers297_huggingface_huggingfacetransformers | 
| 17 | datacollatorforlanguagemodelingfile - datacollatorforlanguagemodeling - datacollatorforlanguagemodelling - datacollatorforpermutationlanguagemodeling - datacollatorfornextsentenceprediction | 77 | 17_datacollatorforlanguagemodelingfile_datacollatorforlanguagemodeling_datacollatorforlanguagemodelling_datacollatorforpermutationlanguagemodeling | 
| 18 | flax - flaxelectraformaskedlm - flaxelectraforpretraining - flaxjax - flaxelectramodel | 52 | 18_flax_flaxelectraformaskedlm_flaxelectraforpretraining_flaxjax | 
| 19 | notebook - notebooks - community - colab - t5 | 48 | 19_notebook_notebooks_community_colab | 
| 20 | wandbproject - wandb - sagemaker - sagemakertrainer - wandbcallback | 39 | 20_wandbproject_wandb_sagemaker_sagemakertrainer | 
| 21 | cachedir - cache - cachedpath - caching - cached | 34 | 21_cachedir_cache_cachedpath_caching | 
| 22 | electra - electrapretrainedmodel - electraformaskedlm - electraformultiplechoice - electrafortokenclassification | 32 | 22_electra_electrapretrainedmodel_electraformaskedlm_electraformultiplechoice | 
| 23 | layoutlm - layout - layoutlmtokenizer - layoutlmbaseuncased - tf | 23 | 23_layoutlm_layout_layoutlmtokenizer_layoutlmbaseuncased | 
| 24 | dict - dictstr - returndict - parse - arguments | 18 | 24_dict_dictstr_returndict_parse | 
| 25 | pplm - pr - deprecated - variable - ppl | 18 | 25_pplm_pr_deprecated_variable | 
| 26 | isort - blackisortflake8 - github - repo - version | 15 | 26_isort_blackisortflake8_github_repo | 
| 27 | blenderbot - blenderbot3b - blenderbotforcausallm - bot - boto3 | 14 | 27_blenderbot_blenderbot3b_blenderbotforcausallm_bot | 
| 28 | indexerror - index - missingindex - indices - runtimeerror | 11 | 28_indexerror_index_missingindex_indices |

</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: 2.0.2
* HDBSCAN: 0.8.41
* UMAP: 0.5.11
* Pandas: 2.2.2
* Scikit-Learn: 1.6.1
* Sentence-transformers: 5.2.0
* Transformers: 4.57.6
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.12.12