Update README.md

README.md

@@ -5,25 +5,32 @@ tags: []
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+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
 
-<!-- Provide a longer summary of what this model is. -->
+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:** [More Information Needed]
+- **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:** [More Information Needed]
+- **Model type:** Classification + Text Generation
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** gpt2 by Hugging Face
 
 ### Model Sources [optional]
 
@@ -39,55 +46,79 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is intended to be used in:
 
-[More Information Needed]
+Embedded EV BMS fault diagnosis
+
+Lab-scale battery monitoring systems
+
+Educational/academic demonstration kits
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Can be used for:
 
-[More Information Needed]
+Real-time IoT fault detection dashboards
+
+Voice-based battery recommendation bots
+
+Integration into EV charging systems
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Not suitable for grid-level battery systems without retraining
 
-[More Information Needed]
+Not a substitute for hardware-based protections
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+May give inaccurate predictions outside of the trained sensor range
 
-[More Information Needed]
+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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Validate GPT-generated text before acting
+
+Add confidence thresholds for alerts
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Complement with hardware-level fault detection
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+tokenizer = GPT2Tokenizer.from_pretrained("your-username/gpt2-cell-diagnosis-system")
+model = GPT2LMHeadModel.from_pretrained("your-username/gpt2-cell-diagnosis-system")
+
+prompt = "Voltage: 3.2V, Temperature: 48C, Current: 0.7A, Status: Overheat\nRecommendation:"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=60)
+print(tokenizer.decode(outputs[0]))
+
+
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Custom Excel dataset (CellDD.xlsx)
 
-[More Information Needed]
+Includes labeled examples of Voltage, Temperature, Current, Status, and textual recommendations
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Procedure
 
 #### Preprocessing [optional]
+Numerical columns normalized
 
-[More Information Needed]
+Text cleaned and used to create prompts for GPT-2
 
 
 #### Training Hyperparameters
@@ -102,27 +133,26 @@ Use the code below to get started with the model.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
+20% split from original dataset (not public)
 
-[More Information Needed]
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Classifier Accuracy: ~96%
+
+Manual evaluation of GPT-2 text output
 
-[More Information Needed]
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
+Classifier Accuracy: ~96%
+
+Manual evaluation of GPT-2 text output
 
 ### Results
 
@@ -144,8 +174,8 @@ Use the code below to get started with the model.
 
 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).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
+- **Hardware Type:** Google Colab (1x T4 GPU)
+- **Hours used:** ~25 minutes
 - **Cloud Provider:** [More Information Needed]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]