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RACLETTE-fp16

@@ -107,158 +107,31 @@ emotion_dict = {e: 0 for e in ["surprised","excited","angry","proud","sad","anno
 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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-## Uses
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-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).
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@@ -272,31 +145,3 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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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.
+- **Developed by:** DeGRa
+- **Model type:** Fine-Tuned Mistral-7B
+- **Language(s) (NLP):** English
 ### Direct Use
+This model is primarily designed for use as an **empathetic chatbot** that understands and responds to users' emotional states in a conversational setting. It progressively builds an emotional profile of the user over multiple turns, enabling more personalized and compassionate interactions. The model leverages generative next-token prediction to infer emotions and produce empathetic responses in real-time, creating a natural and emotionally aware dialogue experience.
+### Downstream Use
+Beyond chatbot applications, this model can be utilized for **emotion classification** tasks. By extracting emotion embeddings from text inputs, it can serve as a foundational tool for emotion detection and analysis in various downstream applications such as sentiment analysis, mental health screening, social media monitoring, and other affective computing domains. Its interpretable emotional profiles enable explainable insights for psychological and clinical tasks.
 ### Out-of-Scope Use
+This model is **not intended** for direct clinical diagnosis or as a standalone mental health intervention tool. It should not be used in applications requiring professional medical judgment. The model may not perform well in domains with language or emotional expressions significantly different from those encountered during fine-tuning. Additionally, it should not be deployed where accurate identification of serious mental health crises without human oversight is required.
 ## Bias, Risks, and Limitations
+The model inherits biases from the **finetuning data**, which consists of publicly available Reddit posts and empathetic dialogue datasets that may contain demographic, cultural, or social biases. These biases can affect emotion recognition accuracy and response generation, potentially amplifying stereotypes or missing subtle emotional nuances in under-represented populations. Careful evaluation and continuous monitoring are necessary when deploying the model, especially in sensitive mental health contexts. Users should be informed of these limitations and encouraged to complement model outputs with expert human evaluation.
+## Citation
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