Danroy
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MamaFrontida

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+# Model Card for Frontida's T5-Small Model
+## Introduction
+This model card presents an overview of the `t5-small` model as adapted and fine-tuned for Frontida, a project dedicated to supporting new mothers through the challenges of postpartum depression. Frontida leverages the `t5-small` model to understand and respond to user queries with empathy and accuracy.
 ## Model Details
 ### Model Description
+The `t5-small` model, developed by Google and fine-tuned by the Frontida team, serves as the backbone of our chatbot's natural language processing capabilities. This version of the T5 model is optimized for efficiency, enabling quick and reliable responses within our application. It has been adapted to specifically address the nuances and complexities of conversations surrounding postpartum depression.
+- **Developed by:** Google, with fine-tuning by the Frontida team
+- **Model type:** Text-to-Text Transfer Transformer (T5) Small
+- **Language(s) (NLP):** Primarily English, with plans to support additional languages
+- **License:** Apache 2.0
+- **Finetuned from:** Google’s original `t5-small` model
+### Model Sources
+- **Repository:** Available on Hugging Face (link to Frontida’s repository)
+- **Paper:** "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (Raffel et al., 2019)
+- **Demo:** Frontida Chatbot Interface (link to demo if available)
 ## Uses
 ### Direct Use
+Frontida's `t5-small` model is directly used within our chatbot interface to provide immediate, contextually relevant support and video recommendations for mothers experiencing postpartum depression.
+### Downstream Use
+While primarily designed for direct interaction within Frontida, the model's applications can extend to other mental health support systems, offering a foundation for empathetic, AI-driven conversation.
 ### Out-of-Scope Use
+The model is not intended for clinical diagnosis or as a substitute for professional healthcare advice.
 ## Bias, Risks, and Limitations
+We acknowledge the potential for biases in AI models and have taken steps to mitigate such risks in `t5-small`. However, users should be aware of the model's limitations, particularly in understanding the full scope of an individual's emotional state.
 ### Recommendations
+Users are encouraged to use Frontida as a supplementary support tool alongside traditional mental health resources. Ongoing model training and refinement are priorities to ensure the most empathetic and accurate responses.
 ## How to Get Started with the Model
+To interact with Frontida's `t5-small` model, users can access our chatbot via the Frontida web application. Developers interested in exploring the model's architecture and training can visit our repository on Hugging Face.
 ## Training Details
 ### Training Data
+The model was fine-tuned on a curated dataset comprising diverse conversations and texts related to mental health, specifically postpartum depression, ensuring a wide range of scenarios and emotions are covered.
+### Training Procedure
+#### Preprocessing
+Text data was normalized and tokenized using standard NLP preprocessing techniques to ensure consistency and improve model understanding.
 #### Training Hyperparameters
+- Training regime details are provided in the repository, focusing on optimizing performance while maintaining the model's efficiency.
 ## Evaluation
 ### Testing Data, Factors & Metrics
+Evaluation was conducted using a separate test set, focusing on accuracy, empathy in responses, and relevance of video recommendations.
 ## Environmental Impact
+Efforts were made to minimize the carbon footprint during training, with details on compute usage and emissions available upon request.
+## Technical Specifications
+Further details on the model's architecture, objective, and compute infrastructure are available in the Frontida repository.
+## More Information
+For additional details, including how to contribute to the model's development or integrate it into other applications, please visit the Frontida project page on Hugging Face.