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Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Gikebe
Model type: BERT-based sequence classification model for inclusion-related text classification
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: bert-base-uncased

Model Sources [optional]

Repository: https://huggingface.co/gikebe/inclusion-model/

Uses

Direct Use

This model can be used to classify text related to inclusion, diversity, and social justice topics into different categories such as "Inclusion Mindset," "Intersectionality," "Empowerment," "Privilege," and "Perfectionism."

Out-of-Scope Use

The model is not suitable for:

-Classification tasks outside of the diversity and inclusion domain. -Use cases where highly nuanced or sensitive topics require additional layers of ethical consideration.

Bias, Risks, and Limitations

-The model may reflect biases present in the underlying training data. -As it is trained on a specific set of texts, it may not generalize well to all contexts related to inclusion and diversity. -The model could misinterpret or misclassify content in languages other than English or in cultural contexts it wasn’t trained on.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

classifier = pipeline("text-classification", model="gikebe/inclusion-dataset")
result = classifier("Women of color face unique challenges that are often overlooked in diversity discussions.")
print(result)

Training Details

Training Data

The model was trained on a custom dataset of inclusion-related texts, derived from books such as Inclusion on Purpose by Ruchika Tulshyan, The Memo by Minda Harts, and others.

Training Procedure

The training procedure involved fine-tuning the BERT-based model (bert-base-uncased) for text classification.

Preprocessing

Text was tokenized using the BertTokenizer with maximum sequence length truncation.

Training Hyperparameters

-Epochs: 3 -Batch size: 8 -Optimizer: AdamW -Learning rate: 5e-5

Evaluation

The model was evaluated on the same dataset split into training and testing sets.

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

Relevant factors include the context and specificity of the inclusion-related texts.

Metrics

Evaluation is yet to be done

Results

Evaluation is yet to be done

Summary

Model Examination

This model is based on the BERT architecture and fine-tuned for sequence classification with the objective of predicting categories related to inclusion and diversity.

Technical Specifications

The model was trained using cloud-based GPU resources.

Citation

BibTeX:

@misc{gikebe_inclusion_2024,
  author = {Gikebe, [Your Name]},
  title = {Inclusion Model},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/gikebe/inclusion-dataset}},
}

APA:

Gikebe. (2024). Inclusion Model. Hugging Face. Retrieved from https://huggingface.co/gikebe/inclusion-dataset

gikebe
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inclusion-model