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BERTopic_model_card_bias
librarian-bots/BERTopic_model_card_bias
BERTopic_model_card_bias is a text classification model from librarian-bots. Use it when you need a label for a piece of text. It is set up for bertopic. The card lists the license as mit.
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.
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6
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At a glance
- Task
- Text Classification
- Library
- bertopic
- License
- mit
- Access
- Public
- Created
- May 11, 2023
- Updated
- Jun 27, 2023
- SHA
- c7716d99
Try a prompt
Model card
From the Hugging Face model README
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
| 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
- Task
- Text Classification
- Library
- bertopic
- License
- mit
- Languages
- en
- Created
- May 11, 2023
- Updated
- Jun 27, 2023