| --- |
| library_name: transformers |
| tags: |
| - inclusion |
| license: mit |
| datasets: |
| - gikebe/inclusion-dataset |
| base_model: |
| - google-bert/bert-base-uncased |
| --- |
| |
| # Model Card for Model ID |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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| - **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 |
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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://huggingface.co/gikebe/inclusion-model/ |
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| ## Uses |
| ### Direct Use |
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| 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." |
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| ### Out-of-Scope Use |
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| The model is not suitable for: |
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| -Classification tasks outside of the diversity and inclusion domain. |
| -Use cases where highly nuanced or sensitive topics require additional layers of ethical consideration. |
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| ## Bias, Risks, and Limitations |
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| -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. |
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| ### Recommendations |
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| Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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| ## How to Get Started with the Model |
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| Use the code below to get started with the model. |
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| ``` |
| from transformers import pipeline |
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| 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) |
| ``` |
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| ## Training Details |
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| ### Training Data |
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| 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. |
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| ### Training Procedure |
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| The training procedure involved fine-tuning the BERT-based model (bert-base-uncased) for text classification. |
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| #### Preprocessing |
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| Text was tokenized using the BertTokenizer with maximum sequence length truncation. |
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| #### Training Hyperparameters |
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| -Epochs: 3 |
| -Batch size: 8 |
| -Optimizer: AdamW |
| -Learning rate: 5e-5 |
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| ## Evaluation |
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| The model was evaluated on the same dataset split into training and testing sets. |
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| ### Testing Data, Factors & Metrics |
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| #### Testing Data |
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| <!-- This should link to a Dataset Card if possible. --> |
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| [More Information Needed] |
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| #### Factors |
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| Relevant factors include the context and specificity of the inclusion-related texts. |
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| #### Metrics |
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| Evaluation is yet to be done |
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| ### Results |
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| Evaluation is yet to be done |
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| #### Summary |
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| ## Model Examination |
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| 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. |
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| ## Technical Specifications |
| The model was trained using cloud-based GPU resources. |
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| ## Citation |
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| **BibTeX:** |
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| ``` |
| @misc{gikebe_inclusion_2024, |
| author = {Gikebe, [Your Name]}, |
| title = {Inclusion Model}, |
| year = {2024}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/gikebe/inclusion-dataset}}, |
| } |
| ``` |
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| **APA:** |
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| Gikebe. (2024). Inclusion Model. Hugging Face. Retrieved from https://huggingface.co/gikebe/inclusion-dataset |
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