
# 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("davanstrien/transformers_issues_topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 30
* Number of training documents: 7235

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | encoder - bert - tensorflow - decoder - output | 11 | -1_encoder_bert_tensorflow_decoder | 
| 0 | tokenizer - tokenizers - tokenization - tokenize - berttokenizer | 2265 | 0_tokenizer_tokenizers_tokenization_tokenize | 
| 1 | cuda - runtimeerror - conda - pytorch - tensorflow | 1513 | 1_cuda_runtimeerror_conda_pytorch | 
| 2 | readmemd - readmetxt - readme - docstring - docstrings | 763 | 2_readmemd_readmetxt_readme_docstring | 
| 3 | trainertrain - trainer - trainertfpy - trainers - training | 550 | 3_trainertrain_trainer_trainertfpy_trainers | 
| 4 | rag - roberta - robertatokenizer - robertatokenizerfast - robertabase | 546 | 4_rag_roberta_robertatokenizer_robertatokenizerfast | 
| 5 | modelcard - modelcards - card - model - cards | 473 | 5_modelcard_modelcards_card_model | 
| 6 | importerror - transformerscli - transformers - transformerxl - transformer | 432 | 6_importerror_transformerscli_transformers_transformerxl | 
| 7 | seq2seq - seq2seqtrainer - seq2seqdataset - runseq2seq - examplesseq2seq | 405 | 7_seq2seq_seq2seqtrainer_seq2seqdataset_runseq2seq | 
| 8 | gpt2 - gpt2tokenizer - gpt2xl - gpt2tokenizerfast - gpt | 365 | 8_gpt2_gpt2tokenizer_gpt2xl_gpt2tokenizerfast | 
| 9 | t5 - t5model - t5base - t5large - tf | 289 | 9_t5_t5model_t5base_t5large | 
| 10 | tests - testing - speedup - test - testgeneratefp16 | 230 | 10_tests_testing_speedup_test | 
| 11 | questionansweringpipeline - questionanswering - answering - questionasnwering - distilbertforquestionanswering | 138 | 11_questionansweringpipeline_questionanswering_answering_questionasnwering | 
| 12 | ner - pipeline - pipelinener - pipelines - pipelineframework | 138 | 12_ner_pipeline_pipelinener_pipelines | 
| 13 | deberta - debertav2 - debertav2initpy - debertatokenizer - distilbertmodel | 132 | 13_deberta_debertav2_debertav2initpy_debertatokenizer | 
| 14 | onnxonnxruntime - onnx - onnxexport - 04onnxexport - 04onnxexportipynb | 110 | 14_onnxonnxruntime_onnx_onnxexport_04onnxexport | 
| 15 | benchmark - benchmarks - accuracy - precision - comparison | 85 | 15_benchmark_benchmarks_accuracy_precision | 
| 16 | labelsmoothingfactor - labelsmoothednllloss - labelsmoothing - labels - label | 79 | 16_labelsmoothingfactor_labelsmoothednllloss_labelsmoothing_labels | 
| 17 | longformer - longformers - longform - longformerforqa - longformerlayer | 71 | 17_longformer_longformers_longform_longformerforqa | 
| 18 | generationbeamsearchpy - generatebeamsearch - beamsearch - nonbeamsearch - beam | 60 | 18_generationbeamsearchpy_generatebeamsearch_beamsearch_nonbeamsearch | 
| 19 | cachedir - cache - cachedpath - caching - cached | 58 | 19_cachedir_cache_cachedpath_caching | 
| 20 | wav2vec2 - wav2vec - wav2vec20 - wav2vec2forctc - wav2vec2xlrswav2vec2 | 56 | 20_wav2vec2_wav2vec_wav2vec20_wav2vec2forctc | 
| 21 | flax - flaxelectraformaskedlm - flaxelectraforpretraining - flaxjax - flaxelectramodel | 52 | 21_flax_flaxelectraformaskedlm_flaxelectraforpretraining_flaxjax | 
| 22 | wandbproject - wandb - wandbcallback - wandbdisabled - wandbdisabledtrue | 49 | 22_wandbproject_wandb_wandbcallback_wandbdisabled | 
| 23 | electra - electrapretrainedmodel - electraformaskedlm - electraformultiplechoice - electrafortokenclassification | 38 | 23_electra_electrapretrainedmodel_electraformaskedlm_electraformultiplechoice | 
| 24 | layoutlm - layout - layoutlmtokenizer - layoutlmbaseuncased - tf | 24 | 24_layoutlm_layout_layoutlmtokenizer_layoutlmbaseuncased | 
| 25 | notebook - notebooks - community - text - multilabel | 18 | 25_notebook_notebooks_community_text | 
| 26 | dict - dictstr - returndict - parse - arguments | 18 | 26_dict_dictstr_returndict_parse | 
| 27 | pplm - pr - deprecated - variable - ppl | 17 | 27_pplm_pr_deprecated_variable | 
| 28 | isort - github - repo - version - setupcfg | 15 | 28_isort_github_repo_version |
  
</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

## Framework versions

* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11

{"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"}
bertopic text-classification has_space region:us
TAGS
#bertopic #text-classification #has_space #region-us 

transformers\_issues\_topics
============================


This is a 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:


You can use the model as follows:


Topic overview
--------------


* Number of topics: 30
* Number of training documents: 7235



Click here for an overview of all topics.


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


Framework versions
------------------


* Numpy: 1.22.4
* HDBSCAN: 0.8.29
* UMAP: 0.5.3
* Pandas: 1.5.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.29.2
* Numba: 0.56.4
* Plotly: 5.13.1
* Python: 3.10.11



TAGS
#bertopic #text-classification #has_space #region-us 
