Update README file

README.md

@@ -1,70 +1,242 @@
 ---
-library_name: transformers
+language:
+- en
 license: apache-2.0
-base_model: distilbert/distilbert-base-uncased
 tags:
-- generated_from_trainer
+- text-classification
+- blood-donation
+- emergency-detection
+- medical
+- healthcare
+- sri-lanka
+- distilbert
+- fine-tuned
+datasets:
+- AshenFdo/synthetic_blood_request_urgency_dataset
+base_model: distilbert/distilbert-base-uncased
+pipeline_tag: text-classification
 metrics:
 - accuracy
 model-index:
 - name: emergency_blood_request_classifier
-  results: []
+  results:
+  - task:
+      type: text-classification
+    dataset:
+      name: synthetic_blood_request_urgency_dataset
+      type: AshenFdo/synthetic_blood_request_urgency_dataset
+    metrics:
+    - type: accuracy
+      value: 1.0
+---
+
+# 🩸 Emergency Blood Request Classifier
+
+A fine-tuned **DistilBERT** model for **binary text classification** that automatically determines whether a blood donation request is an **emergency** or **not an emergency**.
+
+This model is part of my personal project to build an AI-powered blood donation mobile application for Sri Lanka — designed to prioritize life-critical requests and alert nearby donors faster.
+
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+## 📌 Model Summary
+
+| Property | Details |
+|---|---|
+| **Base Model** | [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) |
+| **Task** | Binary Text Classification |
+| **Labels** | `emergency`, `not_emergency` |
+| **Training Dataset** | [AshenFdo/synthetic_blood_request_urgency_dataset](https://huggingface.co/datasets/AshenFdo/synthetic_blood_request_urgency_dataset) |
+| **Evaluation Accuracy** | **100%** (eval set) |
+| **Training Epochs** | 10 |
+| **Language** | English |
+| **Domain** | Healthcare / Blood Donation (Sri Lanka) |
+| **License** | Apache 2.0 |
+
+---
+
+## 🎯 Intended Use
+
+### Primary Use
+This model is designed to be integrated into a **Sri Lanka blood donation mobile application**. When a user posts a blood request, the model reads the description and classifies it as:
+
+- 🚨 `emergency` → triggers immediate alerts to nearby donors
+- 📋 `not_emergency` → listed normally in the donor feed
+
+### How It Fits the Bigger Picture
+
+```
+Blood Donation App (Sri Lanka)
+        │
+        ▼
+  User posts a blood request
+        │
+        ▼
+  🤖 This Model (Emergency Classifier)
+  ─────────────────────────────────────
+  Reads the request description
+        │
+        ├──▶ emergency     → 🚨 Immediately alert nearby donors
+        │
+        └──▶ not_emergency → 📋 List normally in the donor feed
+```
+
+### Other Potential Uses
+- Urgency triage in healthcare communication platforms
+- Benchmarking lightweight NLP models on medical emergency detection
+- Research on urgency language patterns in South Asian healthcare contexts
+
+---
+
+## ⚠️ Limitations
+
+- **Synthetic training data** — The model was trained on AI-generated data. Real-world requests may use different phrasing, slang, abbreviations, or informal language not well-represented in training.
+- **English only** — Sri Lankan blood requests often appear in Sinhala or Tamil. This model does not support those languages.
+- **Sri Lanka context** — The dataset references Sri Lankan hospitals and cities. Performance on requests from other regions may vary.
+- **Not for clinical use** — This model must not be used as a substitute for medical triage or clinical decision-making. It is an assistive tool for a donor coordination platform only.
+
+---
+
+## 🚀 Quick Start
+
+### Using the Pipeline (Recommended)
 
-# emergency_blood_request_classifier
+```python
+from transformers import pipeline
 
-This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.0000
-- Accuracy: 1.0
+classifier = pipeline("text-classification", model="AshenFdo/emergency_blood_request_classifier")
 
-## Model description
+result = classifier("O negative blood required urgently at Karapitiya Hospital. We are out of time.")
+print(result)
+# [{'label': 'emergency', 'score': 0.999...}]
 
-More information needed
+result = classifier("Organizing AB+ blood for an upcoming planned operation at Kandy National Hospital.")
+print(result)
+# [{'label': 'not_emergency', 'score': 0.999...}]
+```
 
-## Intended uses & limitations
+### Loading Model Directly
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
 
-## Training and evaluation data
+tokenizer = AutoTokenizer.from_pretrained("AshenFdo/emergency_blood_request_classifier")
+model = AutoModelForSequenceClassification.from_pretrained("AshenFdo/emergency_blood_request_classifier")
 
-More information needed
+text = "EMERGENCY: Kandy National Hospital ICU urgently needs A- blood. Patient is critical."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
 
-## Training procedure
+with torch.no_grad():
+    logits = model(**inputs).logits
 
-### Training hyperparameters
+predicted_class_id = logits.argmax().item()
+label = model.config.id2label[predicted_class_id]
+print(f"Prediction: {label}")
+# Prediction: emergency
+```
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 10
+### Batch Inference
 
-### Training results
+```python
+from transformers import pipeline
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.0954        | 1.0   | 63   | 0.0107          | 0.998    |
-| 0.0041        | 2.0   | 126  | 0.0010          | 1.0      |
-| 0.0109        | 3.0   | 189  | 0.0017          | 0.998    |
-| 0.0001        | 4.0   | 252  | 0.0001          | 1.0      |
-| 0.0001        | 5.0   | 315  | 0.0000          | 1.0      |
-| 0.0000        | 6.0   | 378  | 0.0000          | 1.0      |
-| 0.0000        | 7.0   | 441  | 0.0000          | 1.0      |
-| 0.0000        | 8.0   | 504  | 0.0000          | 1.0      |
-| 0.0000        | 9.0   | 567  | 0.0000          | 1.0      |
-| 0.0000        | 10.0  | 630  | 0.0000          | 1.0      |
+classifier = pipeline("text-classification", model="AshenFdo/emergency_blood_request_classifier")
 
+requests = [
+    "Need O- blood for a routine hospital visit at Negombo Hospital.",
+    "My sister is in Kandy Hospital ICU with organ failure. We desperately need B+ blood. Pls help!",
+    "Community blood donation drive at Colombo town hall this Saturday.",
+    "URGENT: Karapitiya hospital needs AB- blood. Bus accident, multiple casualties.",
+]
 
-### Framework versions
+results = classifier(requests)
+for req, res in zip(requests, results):
+    print(f"[{res['label']}] {req[:60]}...")
+```
+
+---
+
+## 📊 Training Details
+
+### Dataset
+- **2,500** synthetic blood request descriptions
+- **Balanced** — 1,250 `emergency` and 1,250 `not_emergency`
+- Sri Lanka-specific hospital names and geographic references
+- See the full dataset: [AshenFdo/synthetic_blood_request_urgency_dataset](https://huggingface.co/datasets/AshenFdo/synthetic_blood_request_urgency_dataset)
+
+### Label Definitions
+
+| Label | Description |
+|---|---|
+| `emergency` | Patient is in a critical or life-threatening condition requiring blood immediately. Typically includes keywords like "urgent", "critical", "out of time", "severe hemorrhage", ICU references, or accident/trauma scenarios. |
+| `not_emergency` | Routine, planned, or replacement donation requests. Includes scheduled surgeries, chronic condition support, community blood drives, and replacement donor programs. |
+
+### Hyperparameters
+
+| Parameter | Value |
+|---|---|
+| Learning Rate | `1e-4` |
+| Train Batch Size | `32` |
+| Eval Batch Size | `32` |
+| Epochs | `10` |
+| Optimizer | AdamW (fused) |
+| LR Scheduler | Linear |
+| Seed | `42` |
+
+### Training Results
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+|---|---|---|---|
+| 1 | 0.0954 | 0.0107 | 0.998 |
+| 2 | 0.0041 | 0.0010 | 1.000 |
+| 3 | 0.0109 | 0.0017 | 0.998 |
+| 4 | 0.0001 | 0.0001 | 1.000 |
+| 5–10 | ~0.0000 | ~0.0000 | 1.000 |
+
+### Framework Versions
+- Transformers: 5.10.1
+- PyTorch: 2.11.0+cu128
+- Datasets: 5.0.0
+- Tokenizers: 0.22.2
+- Training Environment: Google Colab (GPU)
+
+---
+
+## 🗂️ Example Predictions
+
+| Description | Expected Label |
+|---|---|
+| *"HOSPITAL EMERGENCY: Ratnapura Hospital urgently requires AB- blood for a critical patient."* | `emergency` |
+| *"O negative blood required urgently at Karapitiya Hospital. We are out of time."* | `emergency` |
+| *"Need O+ blood ASAP. Friend is in Ragama hospital surgical ICU with a burst spleen."* | `emergency` |
+| *"Organizing AB+ blood for an upcoming planned operation at Kandy National Hospital."* | `not_emergency` |
+| *"Blood donation campaign at the Ratnapura clock tower organized by the local association."* | `not_emergency` |
+| *"Advance donor support for an elective surgery at Ratnapura Hospital. A- needed."* | `not_emergency` |
+
+---
+
+## 🔗 Related Resources
+
+- 📁 **Full Project on GitHub:** [AshenFdo/Blood-Request-Emergency-Classification-Model](https://github.com/AshenFdo/Blood-Request-Emergency-Classification-Model)
+- 🗃️ **Training Dataset:** [AshenFdo/synthetic_blood_request_urgency_dataset](https://huggingface.co/datasets/AshenFdo/synthetic_blood_request_urgency_dataset)
+- 🤗 **Base Model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased)
+
+---
+
+## ���� Citation
+
+If you use this model in your research or project, please cite it as:
+
+```bibtex
+@model{AshenFdo_emergency_blood_request_classifier_2025,
+  author    = {AshenFdo},
+  title     = {Emergency Blood Request Classifier},
+  year      = {2025},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/AshenFdo/emergency_blood_request_classifier}
+}
+```
+
+---
 
-- Transformers 5.10.1
-- Pytorch 2.11.0+cu128
-- Datasets 5.0.0
-- Tokenizers 0.22.2
+*Built with the goal of saving lives — one donation at a time. 🩸*