Model Card for Model ID
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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
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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Glossary [optional]
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