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librarian-bots/BERTopic_model_card_bias

sourceHugging Facemitupdated 3y agoView on Hugging Face
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Model Card

BERTopic model card bias topic model

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:

pip install -U bertopic

You can use the model as follows:

python
from bertopic import BERTopic
topic_model = BERTopic.load("davanstrien/BERTopic_model_card_bias")

topic_model.get_topic_info()

Topic overview

  • —Number of topics: 11
  • —Number of training documents: 1271

<details> <summary>Click here for an overview of all topics.</summary>

Topic IDTopic KeywordsTopic FrequencyLabel
-1evaluation - claim - reasoning - parameters - university13-1evaluationclaimreasoningparameters
0checkpoint - fairly - characterized - even - sectionhttpshuggingfacecobertbaseuncased130checkpointfairlycharacterizedeven
1generative - research - uses - processes - artistic1371generativeresearchusesprocesses
2checkpoint - try - snippet - sectionhttpshuggingfacecobertbaseuncased - limitation482checkpointtrysnippetsectionhttpshuggingfacecobertbaseuncased
3meant - technical - sociotechnical - convey - needed323meanttechnicalsociotechnicalconvey
4gpt2 - team - their - cardhttpsgithubcomopenaigpt2blobmastermodelcardmd - worked324gpt2teamtheircardhttpsgithubcomopenaigpt2blobmastermodelcardmd
5datasets - internet - unfiltered - therefore - lot275datasetsinternetunfilteredtherefore
6dacy - danish - pipelines - transformer - bert256dacydanishpipelinestransformer
7your - pythia - branch - checkpoints - provide207yourpythiabranchcheckpoints
8opt - trained - large - software - code158opttrainedlargesoftware
9al - et - identity - occupational - groups159aletidentityoccupational

</details>

Training hyperparameters

  • —calculate_probabilities: False
  • —language: english
  • —low_memory: False
  • —mintopicsize: 10
  • —ngramrange: (1, 1)
  • —nr_topics: None
  • —seedtopiclist: None
  • —topnwords: 10
  • —verbose: False

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: 4.29.0
  • —Numba: 0.56.4
  • —Plotly: 5.13.1
  • —Python: 3.10.11