created Bug Priority model and hugging face deployment read project

README.md

@@ -1,133 +1,11 @@
 ---
 title: Bug Priority Multiclass
-emoji: 📚
-colorFrom: green
-colorTo: purple
+emoji: 💻
+colorFrom: red
+colorTo: gray
 sdk: docker
 pinned: false
-short_description: This model fine-tunes `roberta-base` using a labeled dataset
-
-tags:
-- text-classification
-- accessibility
-- bug-triage
-- transformers
-- roberta
-- pytorch-lightning
-license: apache-2.0
-datasets:
-- custom
-language:
-- en
-
-# RoBERTa Base Model for Accessibility Bug Priority Classification
-
-This model fine-tunes `roberta-base` using a labeled dataset of accessibility-related bug descriptions to automatically classify their **priority level**. It helps automate the triage of bugs affecting users of screen readers and other assistive technologies.
-
-
-## 🧠 Problem Statement
-
-Modern applications often suffer from accessibility issues that impact users with disabilities, such as content not being read properly by screen readers like **VoiceOver**, **NVDA**, or **JAWS**. These bugs are often reported via issue trackers or user forums in the form of short text summaries.
-
-Due to the unstructured and domain-specific nature of these reports, manual triage is:
-- Time-consuming
-- Inconsistent
-- Often delayed in resolution
-
-There is a critical need to **prioritize accessibility bugs quickly and accurately** to ensure inclusive user experiences.
-
-
-## 🎯 Research Objective
-
-This research project builds a machine learning model that can **automatically assign a priority level** to an accessibility bug report. The goal is to:
-
-- Streamline accessibility QA workflows
-- Accelerate high-impact fixes
-- Empower developers and testers with ML-assisted tooling
-
-## 📊 Dataset Statistics
-
-The dataset used for training consists of real-world accessibility bug reports, each labeled with one of four priority levels. The distribution of labels is imbalanced, and label-aware preprocessing steps were taken to improve model performance.
-
-| Label | Priority Level | Count |
-|-------|----------------|-------|
-| 1     | Medium         | 2035  |
-| 2     | High           | 1465  |
-| 0     | Low            | 804   |
-| 3     | Critical       | 756   |
-
-**Total Samples**: 5,060
-
-### 🧹 Preprocessing
-
-- Text normalization and cleanup  
-- Length filtering based on token count  
-- Label frequency normalization for class-weighted loss  
-
-To address class imbalance, class weights were computed as inverse label frequency and used in the cross-entropy loss during training.
-
-## 🧪 Dataset Description
-
-The dataset consists of short bug report texts labeled with one of four priority levels:
-
-| Label | Meaning     |
-|-------|-------------|
-| 0     | Low         |
-| 1     | Medium      |
-| 2     | High        |
-| 3     | Critical    |
-
-### ✏️ Sample Entries:
-
-```csv
-Text,Label
-"mac voiceover screen reader",3
-"Firefox crashes when interacting with some MathML content using Voiceover on Mac",0
-"VoiceOver skips over text in paragraphs which contain <strong> or <em> tags",2
-```
-
-
-## 📊 Model Comparison
-
-We fine-tuned and evaluated three transformer models under identical training conditions using PyTorch Lightning (multi-GPU, mixed precision, and weighted loss). The validation accuracy and F1 scores are as follows:
-
-| Model           | Base Architecture          | Validation Accuracy | Weighted F1 Score |
-|-----------------|----------------------------|---------------------|-------------------|
-| DeBERTa-v3 Base | microsoft/deberta-v3-base  | **69%**             | **0.69**          |
-| ALBERT Base     | albert-base-v2             | 68%                 | 0.68              |
-| RoBERTa Base    | roberta-base               | 66%                 | 0.67              |
-
-### 📝 Observations
-
-- **DeBERTa** delivered the best performance, likely due to its *disentangled attention* and *enhanced positional encoding*.
-- **ALBERT** performed surprisingly well despite having fewer parameters, showcasing its efficiency.
-- **RoBERTa** provided stable and reliable results but slightly underperformed compared to the others.
-
-
-# RoBERTa Base Model for Accessibility Priority Classification
-
-This model fine-tunes `roberta-base` using a 4-class custom dataset to classify accessibility issues by priority. It was trained using PyTorch Lightning and optimized with mixed precision on multiple GPUs.
-
-## Details
-
-- **Model**: roberta-base
-- **Framework**: PyTorch Lightning
-- **Labels**: 0 (Low), 1 (Medium), 2 (High), 3 (Critical)
-- **Validation F1**: 0.71 (weighted)
-
-## Usage
-
-```python
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-import torch
-
-model = RobertaForSequenceClassification.from_pretrained("your-username/roberta-priority-multiclass")
-tokenizer = RobertaTokenizer.from_pretrained("your-username/roberta-priority-multiclass")
-
-inputs = tokenizer("VoiceOver skips over text with <strong> tags", return_tensors="pt")
-outputs = model(**inputs)
-prediction = torch.argmax(outputs.logits, dim=1).item()
-
-print("Predicted Priority:", prediction)
-```
+short_description: This is a Multiclass Bug Priority Model
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference