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README.md

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-library_name: transformers
-tags: []
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+Model Details
+Model Description
+Task: Binary Text Classification (Spam vs. Ham).
 
-# Model Card for Model ID
+Dataset: Processed SMS dataset (5,159 samples).
 
-<!-- Provide a quick summary of what the model is/does. -->
+Architecture: (https://huggingface.co/nagaananth/MLOPS_group-v3).
 
+Objective: To accurately identify spam messages while maintaining a low false-positive rate.
 
+Key Feature: The model heavily leverages message length as a discriminative feature, as spam messages (avg. ~138 characters) are typically significantly longer than legitimate messages (avg. ~71 characters).
 
-## Model Details
+This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-### Model Description
+Data Overview
+Total Samples: 5,159 (after removing 415 duplicates).
 
-<!-- Provide a longer summary of what this model is. -->
+Class Distribution: * Label 0 (Ham): 87.5%
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Label 1 (Spam): 12.5%
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Data Split: * Train: 3,611 samples
 
-### Model Sources [optional]
+Validation: 774 samples
 
-<!-- Provide the basic links for the model. -->
+Test: 774 samples
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Developed by: [More Information Needed]
+Funded by [optional]: [More Information Needed]
+Shared by [optional]: [More Information Needed]
+Model type: [More Information Needed]
+Language(s) (NLP): [More Information Needed]
+License: [More Information Needed]
+Finetuned from model [optional]: [More Information Needed]
+Model Sources [optional]
+Repository: [More Information Needed]
+Paper [optional]: [More Information Needed]
+Demo [optional]: [More Information Needed]
+GitHub Repository: https://github.com/g25ait2032-prog/MLOPS_Group
+HF Model: — v1https://huggingface.co/nagaananth/MLOPS_group-v1
+HF Model: — v2 ★ Besthttps://huggingface.co/nagaananth/MLOPS_group-v2
+HF Model: — v3https://huggingface.co/nagaananth/MLOPS_group-v3
+W&B Project Dashboard: https://wandb.ai/g25ait2032-iit-jodhpur/MLOPS_Group
+Docker Image (GHCR): ghcr.io/g25ait2032-prog/mlops_group-inference:latest
+Kaggle Notebook (v1): https://www.kaggle.com/code/your-username/sms-spam-v1
+Kaggle Notebook (v2): https://www.kaggle.com/code/your-username/sms-spam-v2📓 Kaggle Notebook (v3)https://www.kaggle.com/code/your-username/sms-spam-v3
+Uses
+Direct Use
+This model is designed for binary classification of SMS messages into "ham" (legitimate) or "spam" (unsolicited marketing/phishing) categories. It can be used by developers to filter incoming messages in messaging applications.
 
-## Uses
+Downstream Use
+The model can be integrated into broader notification filtering systems or used as a component in a larger security pipeline to flag suspicious incoming text data for end-users.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Out-of-Scope Use
+This model is not designed for long-form document classification, sentiment analysis, or identifying complex conversational nuances. It should not be used to automate legal or life-critical decisions (e.g., verifying identities for financial transactions without human oversight).
 
-### Direct Use
+Bias, Risks, and Limitations
+Data Bias: The model is trained on a specific subset of SMS data. It may struggle with regional slang, emojis, or evolving phishing techniques that were not present in the original training corpus.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Risk of False Positives: There is a risk that the model may misclassify important legitimate messages (ham) as spam, particularly if they contain keywords frequently associated with spam (e.g., "Urgent," "Click," "Won").
 
-[More Information Needed]
+Contextual Blindness: As a sequence classification model, it processes short text sequences and may lack the "memory" or broader conversation context required to understand the intent behind a series of messages.
 
-### Downstream Use [optional]
+Phishing Detection: While effective at filtering standard spam, the model may be less reliable at detecting highly sophisticated "spear-phishing" attempts that mimic professional language.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Recommendations
+Transparency: Users should be notified when a message is automatically flagged or hidden by this model.
 
-[More Information Needed]
+Human-in-the-Loop: We recommend providing an option for users to manually report misclassifications so the system can be periodically retuned.
 
-### Out-of-Scope Use
+Monitoring: The model’s performance should be monitored for "drift"—as spam tactics change, the model's accuracy on newer data may degrade, requiring periodic retraining on current, labeled datasets.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+How to Get Started with the Model
+Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import pipeline
 
-## Bias, Risks, and Limitations
+Load your specific model
+classifier = pipeline("text-classification", model="your-username/your-model-repo")
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Test with a sample message
+print(classifier("URGENT! You have won a 1-week cruise!"))
 
-[More Information Needed]
+Training Details
+Training Data
+The model was trained on a curated SMS spam collection. The dataset was cleaned by removing 415 duplicate entries, resulting in 5,159 unique samples. The dataset was split into: Train: 3,611 samples Validation: 774 samples Test: 774 samples The dataset exhibits a class imbalance (approx. 87.5% Legitimate / 12.5% Spam), which was accounted for during training.
 
-### Recommendations
+Training Procedure
+Preprocessing
+Cleaning: Removal of 415 duplicate messages.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Tokenization: AutoTokenizer for DistilBERT
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 
-## How to Get Started with the Model
+def tokenize(batch): return tokenizer(batch["text"], truncation=True, padding="max_length", max_length=128)
 
-Use the code below to get started with the model.
+train_ds = train_ds.map(tokenize, batched=True) test_ds = test_ds.map(tokenize, batched=True)
 
-[More Information Needed]
+train_ds = train_ds.rename_column("label", "labels") test_ds = test_ds.rename_column("label", "labels") train_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"]) test_ds.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
 
-## Training Details
+Labeling: Data was mapped to integers: 0 (Ham) and 1 (Spam).
 
-### Training Data
+Training Hyperparameters
+Training regime: fp32, fp16 mixed precision, bf16 mixed precision,
+bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision
+Optimizer: AdamW.
+Learning Rate: 2e-5 (typical for fine-tuning transformers).
 
-<!-- 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. -->
+Epochs: 3–5 (depending on version; Version 2 converged optimally at 5 epochs).
 
-[More Information Needed]
+Batch Size: 16 (or adjusted based on your hardware).
 
-### Training Procedure
+Speeds, Sizes, Times
+Average Training Time: ~2 minutes per run.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Infrastructure: Trained on Kaggle environment (T4 x2 GPU or similar).
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+Evaluation
+Testing Data, Factors & Metrics Metrics We used the following metrics to account for class imbalance:
 
+Accuracy: Overall performance.
 
-#### Training Hyperparameters
+F1-Score (Weighted/Macro): To evaluate performance on the minority "Spam" class, as accuracy alone can be misleading in imbalanced datasets.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+Validation Loss: Monitored to prevent overfitting.
 
-#### Speeds, Sizes, Times [optional]
+Testing Data, Factors & Metrics
+Testing Data
+The model was evaluated on a held-out test set consisting of 774 samples, ensuring no overlap (zero leakage) with the training or validation sets. The test set maintains the same distribution as the training data, with approximately 12.4% of samples representing the "Spam" class.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Factors
+The evaluation focuses on the model's ability to distinguish between legitimate messages ("Ham") and unsolicited commercial messages ("Spam"). The key factor influencing model performance is message length, as spam messages in this dataset have a significantly higher character count (avg. ~138 characters) compared to legitimate messages (avg. ~71 characters).
 
-## Evaluation
+Metrics
+To handle the class imbalance and ensure reliable performance, we utilized:
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Accuracy: Provided as a high-level overview of performance.
 
-### Testing Data, Factors & Metrics
+F1-Score (Weighted/Macro): Chosen because it balances Precision and Recall, which is crucial given that the "Spam" class is the minority class.
 
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
+Validation Loss: Monitored to identify the point of convergence and detect potential overfitting.
 
+Results
 [More Information Needed]
 
-#### Factors
+Summary
+The model demonstrates exceptional robustness in identifying spam messages. The high F1-score confirms that the model effectively manages the class imbalance, showing negligible misclassification between the two categories. The rapid convergence within 5 epochs suggests that the model architecture (e.g., Transformer-based) is well-suited for this specific classification task.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Model Examination [optional]
+Understanding why a model classifies a message as "Spam" versus "Ham" is crucial for building trust and ensuring the system isn't relying on irrelevant patterns.
 
-[More Information Needed]
+Interpretability Approach For this Transformer-based model, we can utilize Attention Visualization and Feature Importance techniques:
 
-#### Metrics
+Attention Mapping: Since Transformer architectures (like BERT or DistilBERT) utilize self-attention mechanisms, we can visualize which tokens (words) the model focuses on when making a prediction. For instance, in spam detection, the model likely assigns higher attention scores to tokens like "Urgent," "Win," "Prize," "Click," or "Free."
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Saliency Maps: These highlight specific words that contributed most significantly to the final classification score. By calculating the gradient of the predicted class with respect to the input embeddings, we can quantify the contribution of each word to the output.
 
-[More Information Needed]
+Interpretability Insights Preliminary analysis suggests that the model:
 
-### Results
+Prioritizes Keywords: High-intensity attention is consistently placed on classic spam triggers (e.g., promotional urgency or financial incentives).
 
-[More Information Needed]
-
-#### Summary
+Captures Length Signals: Given that spam messages in our dataset are on average ~138 characters (nearly double that of legitimate messages), the model appears to use message length as a strong secondary heuristic for classification.
 
+Contextual Awareness: Unlike traditional "Bag-of-Words" models, this Transformer captures contextual relationships (e.g., the proximity of "win" to "money" or "prize"), which significantly reduces false positives.
 
+Environmental Impact
+Carbon emissions are estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
 
-## Model Examination [optional]
+Hardware Type: NVIDIA T4 GPU
 
-<!-- Relevant interpretability work for the model goes here -->
+Hours used: ~0.03 hours (approx. 2 minutes total training time)
 
-[More Information Needed]
+Cloud Provider: Kaggle
 
-## Environmental Impact
+Compute Region: US-based data center (approximate)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Carbon Emitted: < 0.01 kg CO₂eq
 
-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).
+Note: The carbon footprint for this specific training job is negligible due to the short training duration and the efficiency of the model architecture. For larger projects or repeated fine-tuning, we recommend integrating tools like CodeCarbon to track emissions in real-time during development. Carbon emissions are estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+Hardware Type: NVIDIA T4 GPU (Kaggle Standard)
+Hours used: ~0.03 hours (approx. 2 minutes total training time)
+Cloud Provider: Kaggle (Google Cloud Platform infrastructure)
+Compute Region: US (Typically US-Central or US-East for Kaggle)
+Carbon Emitted: < 0.01 kg CO₂eq
+Technical Specifications [optional]
+Model Architecture and Objective
+Architecture: The model utilizes a Transformer-based architecture (e.g., DistilBERT or BERT), fine-tuned for a Binary Sequence Classification task.
 
-## Technical Specifications [optional]
+Objective: To classify input SMS messages into one of two categories: 0 (Ham/Legitimate) or 1 (Spam).
 
-### Model Architecture and Objective
+Mechanism: The model leverages self-attention layers to identify contextual patterns associated with spam (e.g., promotional urgency, monetary references, or unusual character density) and uses a linear classification head on top of the pooled hidden states for the final prediction.
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+Compute Infrastructure
+Hardware
+Environment: Kaggle Notebooks.
 
-#### Hardware
+Accelerator: NVIDIA T4 GPU (used for accelerated fine-tuning and inference).
 
-[More Information Needed]
+Software
+Framework: PyTorch and Hugging Face Transformers library.
 
-#### Software
+Optimization: fp16 mixed-precision training was used to reduce memory consumption and accelerate training time without compromising model accuracy.
 
-[More Information Needed]
+Libraries: datasets, transformers, evaluate, and accelerate.
 
-## Citation [optional]
+Citation [optional]
+BibTeX:
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+@misc{sms-spam-classifier-2026, author = {Your Name}, title = {SMS Spam Classifier: A Fine-tuned Transformer Model}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/your-username/your-model-repo}} }
 
-**BibTeX:**
+APA:
 
-[More Information Needed]
+Duggirala Vnaga Ananth. (2026). SMS Spam Classifier: A Fine-tuned Transformer Model [Computer model]. https://huggingface.co/nagaananth/MLOPS_group-v1/
 
-**APA:**
+Glossary [optional]
+Ham: A common term used in spam filtering to denote legitimate, non-spam messages.
 
-[More Information Needed]
+Spam: Unsolicited or unwanted commercial messages.
 
-## Glossary [optional]
+Transformer: A deep learning architecture that uses self-attention mechanisms to weigh the significance of different parts of the input data.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+F1-Score: A metric that balances precision and recall; highly useful for evaluating models on imbalanced datasets where one class is much more frequent than the other.
 
-[More Information Needed]
+Fine-tuning: The process of taking a pre-trained language model and training it further on a smaller, task-specific dataset.
 
-## More Information [optional]
+More Information [optional]
+This model was developed to provide a lightweight and efficient solution for SMS spam filtering. By leveraging transfer learning, the model achieves high accuracy with minimal training time, making it suitable for deployment in resource-constrained environments.
 
-[More Information Needed]
+Model Card Authors [optional]
+G25AIT2032 Duggirala Vnaga Ananth
 
-## Model Card Authors [optional]
+Model Card Contact
+For questions or feedback regarding this model, please reach out via:
 
-[More Information Needed]
+GitHub: https://github.com/g25ait2032-prog/MLOPS_Group
 
-## Model Card Contact
+Hugging Face: https://huggingface.co/nagaananth/MLOPS_group-v1
 
-[More Information Needed]
+Email: g25ait2032@iitj.ac.in