
# BERTopic model card bias topic model

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/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 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 |
  
</details>

## 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
{"language": ["en"], "license": "mit", "library_name": "bertopic", "tags": ["bertopic", "metadata", "model cards", "bias"], "datasets": ["davanstrien/model_cards_with_readmes"], "pipeline_tag": "text-classification", "inference": false}
bertopic metadata model cards bias text-classification en dataset:davanstrien/model_cards_with_readmes license:mit region:us
TAGS
#bertopic #metadata #model cards #bias #text-classification #en #dataset-davanstrien/model_cards_with_readmes #license-mit #region-us 

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:


You can use the model as follows:


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


* Number of topics: 11
* Number of training documents: 1271



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: 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



TAGS
#bertopic #metadata #model cards #bias #text-classification #en #dataset-davanstrien/model_cards_with_readmes #license-mit #region-us 
