Update README.md

README.md

@@ -1,6 +1,12 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- inclusion
+license: mit
+datasets:
+- gikebe/inclusion-dataset
+base_model:
+- google-bert/bert-base-uncased
 ---
 
 # Model Card for Model ID
@@ -17,92 +23,83 @@ tags: []
 
 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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **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]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://huggingface.co/gikebe/inclusion-model/
 
 ## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+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."
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical 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.
 
-[More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
 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.
 
-[More Information Needed]
+```
+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
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+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.
 
-[More Information Needed]
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The training procedure involved fine-tuning the BERT-based model (bert-base-uncased) for text classification.
 
-#### Preprocessing [optional]
+#### Preprocessing
 
-[More Information Needed]
+Text was tokenized using the BertTokenizer with maximum sequence length truncation.
 
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+-Epochs: 3
+-Batch size: 8
+-Optimizer: AdamW
+-Learning rate: 5e-5
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+The model was evaluated on the same dataset split into training and testing sets.
+
 
 ### Testing Data, Factors & Metrics
 
@@ -114,86 +111,44 @@ Use the code below to get started with the model.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Relevant factors include the context and specificity of the inclusion-related texts.
 
-[More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Evaluation is yet to be done
 
-[More Information Needed]
 
 ### Results
 
-[More Information Needed]
+Evaluation is yet to be done
 
 #### Summary
 
 
+## Model Examination 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+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.
 
-#### Hardware
 
-[More Information Needed]
+## Technical Specifications 
+The model was trained using cloud-based GPU resources.
 
-#### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```
+@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:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Gikebe. (2024). Inclusion Model. Hugging Face. Retrieved from https://huggingface.co/gikebe/inclusion-dataset