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:

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

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	evaluation - claim - reasoning - parameters - university	13	-1_evaluation_claim_reasoning_parameters
0	checkpoint - fairly - characterized - even - sectionhttpshuggingfacecobertbaseuncased	13	0_checkpoint_fairly_characterized_even
1	generative - research - uses - processes - artistic	137	1_generative_research_uses_processes
2	checkpoint - try - snippet - sectionhttpshuggingfacecobertbaseuncased - limitation	48	2_checkpoint_try_snippet_sectionhttpshuggingfacecobertbaseuncased
3	meant - technical - sociotechnical - convey - needed	32	3_meant_technical_sociotechnical_convey
4	gpt2 - team - their - cardhttpsgithubcomopenaigpt2blobmastermodelcardmd - worked	32	4_gpt2_team_their_cardhttpsgithubcomopenaigpt2blobmastermodelcardmd
5	datasets - internet - unfiltered - therefore - lot	27	5_datasets_internet_unfiltered_therefore
6	dacy - danish - pipelines - transformer - bert	25	6_dacy_danish_pipelines_transformer
7	your - pythia - branch - checkpoints - provide	20	7_your_pythia_branch_checkpoints
8	opt - trained - large - software - code	15	8_opt_trained_large_software
9	al - et - identity - occupational - groups	15	9_al_et_identity_occupational

Training hyperparameters

• calculate_probabilities: False
• 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

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