Abhinayathir/memory-triggered-emotion-classifier

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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: Abhinayathir
• Funded by [optional]: Personal project
• Shared by [optional]: Abhinayathir
• Model type: Text Classification, Emotion Recognition
• Language(s) (NLP): English
• License: MIT
• Finetuned from model [optional]: DistilBERT

Model Sources [optional]

• Repository: https://huggingface.co/Abhinayathir/memory-triggered-emotion-classifier
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

The model can be used in therapeutic applications to assist users in recalling personal memories based on their current emotional state. It can be used in apps or websites offering emotional support and well-being services.

Downstream Use [optional]

The model can be integrated into mental health apps, chatbots, or virtual assistants that aim to offer emotional support by recalling memories.

Out-of-Scope Use

This model should not be used in any context that involves medical diagnosis or as a substitute for professional mental health counseling.

Bias, Risks, and Limitations

• The model is trained on a limited dataset and might not capture all emotional nuances from users of different cultural backgrounds.
• It may not always provide the most accurate or helpful memory recall, as it relies on the emotional input from the user, which might be misinterpreted in some cases.
• The model is not a replacement for professional therapy or mental health support and should only be used for general emotional support.

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

Load the model

memory_recaller = pipeline("text-classification", model="Abhinayathir/memory-triggered-emotion-classifier")

Input user's emotional state

user_input = "I am feeling sad" recalled_memory = memory_recaller(user_input) print(recalled_memory)

Training Details

Training Data

Dataset Source: Custom dataset created for training emotional classification models. The dataset contains labeled emotional states such as “sad”, “happy”, “anxious”, “excited”, etc., along with associated memories. Data Processing: Text data was preprocessed using tokenization and padding to make it suitable for transformer-based models. Emotions were labeled based on the dataset descriptions.

Training Procedure

The model was trained on labeled emotional state data for emotion classification. It used fine-tuning techniques on a pre-trained DistilBERT model.

Preprocessing [optional]

Text Tokenization: Text was tokenized using the Hugging Face DistilBERT tokenizer, converting input text into token IDs. Padding and Truncation: Inputs were padded to a fixed length of 128 tokens, and truncation was applied to longer texts. Normalization: Input text was normalized to lowercase for consistency.

Training Hyperparameters

• Training regime: fp16 mixed precision

Speeds, Sizes, Times [optional]

Epochs: 3 epochs Batch Size: 32 Learning Rate: 5e-5 Optimizer: AdamW

Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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