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02fe22bee008/gpt2-cell-recommendation-system

sourceHugging Faceupdated 1y agoView on Hugging Face
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This model performs intelligent battery cell fault diagnosis using real-time voltage, temperature, and current inputs. It consists of a hybrid architecture:

A RandomForestClassifier predicts the cell fault status.

A GPT-2 model, fine-tuned on labeled diagnosis data, generates recommendations based on sensor readings.

The model is deployed with an TTS modules can be added to explain results via speech or video.

Model Details

Model Description

A RandomForestClassifier for predicting battery cell status based on voltage, temperature, and current.

A fine-tuned GPT-2 language model to generate recommendation text tailored to the predicted fault.

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: Bhoomika Patil (IEEE Member, AESS Vice Chair 2024, PES Chair 2025)
  • —Funded by [optional]: [More Information Needed]
  • —Shared by [optional]: [More Information Needed]
  • —Model type: Classification + Text Generation
  • —Language(s) (NLP): [More Information Needed]
  • —License: [More Information Needed]
  • —Finetuned from model [optional]: gpt2 by Hugging Face

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Uses

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Direct Use

This model is intended to be used in:

Embedded EV BMS fault diagnosis

Lab-scale battery monitoring systems

Educational/academic demonstration kits

Downstream Use [optional]

Can be used for:

Real-time IoT fault detection dashboards

Voice-based battery recommendation bots

Integration into EV charging systems

Out-of-Scope Use

Not suitable for grid-level battery systems without retraining

Not a substitute for hardware-based protections

Bias, Risks, and Limitations

May give inaccurate predictions outside of the trained sensor range

GPT-2 recommendations may not always be technically feasible or safe

Does not consider time-series or historical trends

Limited robustness for deployment in production-grade EVs

Recommendations

Validate GPT-generated text before acting

Add confidence thresholds for alerts

Complement with hardware-level fault detection

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.frompretrained("your-username/gpt2-cell-diagnosis-system") model = GPT2LMHeadModel.frompretrained("your-username/gpt2-cell-diagnosis-system")

prompt = "Voltage: 3.2V, Temperature: 48C, Current: 0.7A, Status: Overheat\nRecommendation:" inputs = tokenizer(prompt, returntensors="pt") outputs = model.generate(**inputs, maxlength=60) print(tokenizer.decode(outputs[0]))

Training Details

Training Data

Custom Excel dataset (CellDD.xlsx)

Includes labeled examples of Voltage, Temperature, Current, Status, and textual recommendations

Training Procedure

Preprocessing [optional]

Numerical columns normalized

Text cleaned and used to create prompts for GPT-2

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 -->
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Evaluation

Testing Data, Factors & Metrics

Testing Data

20% split from original dataset (not public)

Factors

Classifier Accuracy: ~96%

Manual evaluation of GPT-2 text output

Metrics

Classifier Accuracy: ~96%

Manual evaluation of GPT-2 text output

Results

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Summary

Model Examination [optional]

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Environmental Impact

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

  • —Hardware Type: Google Colab (1x T4 GPU)
  • —Hours used: ~25 minutes
  • —Cloud Provider: [More Information Needed]
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  • —Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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