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@@ -1,199 +1,517 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+  - en
+tags:
+  - medical
+  - clinical-nlp
+  - biobert
+  - bio-clinicalbert
+  - cancer
+  - survival-analysis
+  - oncology
+  - pathology
+  - tcga
+  - lora
+  - peft
+  - cox-regression
+  - risk-prediction
+  - text-classification
+  - feature-extraction
+  - pytorch
+datasets:
+  - custom
+base_model: emilyalsentzer/Bio_ClinicalBERT
+pipeline_tag: feature-extraction
+model-index:
+  - name: finetunePathologicalTextUsingBioBERT
+    results:
+      - task:
+          type: feature-extraction
+          name: Survival Risk Prediction
+        metrics:
+          - type: loss
+            name: Cox PH Validation Loss
+            value: 0.5290
+          - type: loss
+            name: Cox PH Training Loss
+            value: 0.4003
 ---
 
-# Model Card for Model ID
+# 🧬 Fine-Tuned Bio_ClinicalBERT for Cancer Survival Prediction from Pathological Text
 
-<!-- Provide a quick summary of what the model is/does. -->
+> **A domain-adapted biomedical language model fine-tuned on 19,637 TCGA pathological text reports for cancer survival risk prediction using Cox Proportional Hazards loss with LoRA adapters — trained on NVIDIA RTX 3090 (24 GB VRAM).**
 
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+[![PyTorch](https://img.shields.io/badge/PyTorch-2.6.0+cu124-red.svg)](https://pytorch.org/)
+[![Transformers](https://img.shields.io/badge/Transformers-5.7.0-orange.svg)](https://huggingface.co/docs/transformers)
+[![PEFT](https://img.shields.io/badge/PEFT-0.19.1-green.svg)](https://huggingface.co/docs/peft)
+[![GPU](https://img.shields.io/badge/GPU-RTX_3090_24GB-76B900.svg)](https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090/)
 
+---
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a **fine-tuned version of [Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT)** (Alsentzer et al., 2019) adapted for **cancer survival risk prediction** directly from unstructured pathological text reports. The model was trained on data from **The Cancer Genome Atlas (TCGA)** spanning **24 cancer types** across **32 cohorts**.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Instead of traditional hand-crafted features (stage, grade, tumor size), this model **learns survival-relevant patterns directly from raw pathological text** — capturing subtle linguistic cues such as pathologist phrasing correlating with tumor aggressiveness, specific morphological descriptions, and diagnostic uncertainty language.
 
-- **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]
+The model outputs:
+1. **A continuous risk score** — higher values indicate higher mortality risk (used with Cox Proportional Hazards framework)
+2. **768-dimensional embeddings** — from the `[CLS]` token, suitable for downstream multimodal survival pipelines
 
-### Model Sources [optional]
+- **Developed by:** [Dr. Kareem Kamal](https://github.com/drkareemkamal)
+- **Model type:** BERT-based encoder with LoRA adapters + linear survival risk head
+- **Language(s):** English (clinical/biomedical)
+- **License:** MIT
+- **Fine-tuned from:** [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT)
+- **Base architecture:** BERT-Base (cased, 12-layer, 768-hidden, 12-attention-heads, ~110M parameters)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [github.com/drkareemkamal/cancer-survival-analysis](https://github.com/drkareemkamal/cancer-survival-analysis)
+- **Base model paper:** [Publicly Available Clinical BERT Embeddings (Alsentzer et al., NAACL 2019)](https://arxiv.org/abs/1904.03323)
+- **BioBERT paper:** [BioBERT: a pre-trained biomedical language representation model (Lee et al., 2020)](https://arxiv.org/abs/1901.08746)
 
-## Uses
+---
+
+## About Bio_ClinicalBERT (Base Model)
+
+Bio_ClinicalBERT has a unique **three-stage pre-training lineage** that makes it ideal for clinical text understanding:
+
+| Stage | Training Data | Details |
+|-------|--------------|---------|
+| **1. BERT-Base** | Wikipedia + BookCorpus | General English language understanding |
+| **2. BioBERT v1.0** | PubMed abstracts (200K) + PMC full-text (270K) | Biomedical scientific literature |
+| **3. Bio_ClinicalBERT** | MIMIC-III clinical notes (~880M words) | Real electronic health records (EHR) |
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+**Key specifications of the base model:**
+- **Architecture:** `cased_L-12_H-768_A-12` (12 layers, 768 hidden dim, 12 attention heads)
+- **Parameters:** ~110 million
+- **Vocabulary:** 28,996 WordPiece tokens (domain-adapted)
+- **Max sequence length:** 128 tokens (original); extended to **512 tokens** in our fine-tuning
+- **Original training:** 150,000 steps on GeForce GTX TITAN X (12 GB), batch size 32, LR 5e-5
+
+This lineage means the model understands:
+- ✅ General English grammar and semantics (BERT)
+- ✅ Biomedical terminology and relationships (BioBERT)
+- ✅ Clinical shorthand, abbreviations, and report structure (MIMIC-III)
+
+---
+
+## Uses
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Load the fine-tuned model to extract survival-relevant embeddings or risk scores from pathological text:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
 
-### Downstream Use [optional]
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("drkareemkamal/finetunePathologicalTextUsingBioBERT")
+model = AutoModel.from_pretrained("drkareemkamal/finetunePathologicalTextUsingBioBERT")
+model.eval()
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Example pathological report text
+text = """Invasive ductal carcinoma, Nottingham grade 3/3. 
+Tumor size: 2.8 cm. ER negative, PR negative, HER2 positive (3+). 
+Lymphovascular invasion present. 2 of 14 sentinel lymph nodes positive 
+for metastatic carcinoma. Margins: negative, closest margin 0.3 cm."""
 
-[More Information Needed]
+# Tokenize
+inputs = tokenizer(
+    text,
+    return_tensors="pt",
+    max_length=512,
+    truncation=True,
+    padding=True
+)
 
-### Out-of-Scope Use
+# Extract [CLS] embedding (768-dim)
+with torch.no_grad():
+    outputs = model(**inputs)
+    cls_embedding = outputs.last_hidden_state[:, 0, :]  # Shape: (1, 768)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+print(f"Embedding shape: {cls_embedding.shape}")  # torch.Size([1, 768])
+```
 
-[More Information Needed]
+### Downstream Use
 
-## Bias, Risks, and Limitations
+**Survival Risk Scoring** — Use with the custom risk head for direct risk prediction:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```python
+import torch.nn as nn
 
-[More Information Needed]
+# Reconstruct the risk head (trained alongside the model)
+risk_head = nn.Linear(768, 1)
+# Load risk head weights from checkpoint if available
 
-### Recommendations
+risk_score = risk_head(cls_embedding)
+print(f"Risk score: {risk_score.item():.4f}")
+# Higher score → higher predicted mortality risk
+```
+
+**Multimodal Fusion** — Combine text embeddings with clinical, genomic, and mutation data:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+```python
+# Text embedding: 768-dim from this model
+# Gene expression: 50-dim from PCA of RNA-Seq FPKM values
+# Mutation features: binary mutation matrix
+# Clinical features: age, stage, grade, etc.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+combined = torch.cat([text_emb, gene_emb, mutation_emb, clinical_emb], dim=-1)
+# Feed into downstream survival model (e.g., DeepSurv, Cox-nnet)
+```
 
-## How to Get Started with the Model
+### Out-of-Scope Use
 
-Use the code below to get started with the model.
+- ❌ **Not a diagnostic tool** — This model predicts survival risk, not diagnosis
+- ❌ **Not for non-cancer text** — Trained exclusively on oncological pathology reports
+- ❌ **Not for clinical deployment without regulatory approval** — Research use only
+- ❌ **Not for non-English text** — Trained on English pathology reports only
+- ❌ **Not for individual patient decisions** — Requires human clinical oversight
 
-[More Information Needed]
+---
 
 ## 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. -->
-
-[More Information Needed]
+| Property | Value |
+|----------|-------|
+| **Source** | [The Cancer Genome Atlas (TCGA)](https://portal.gdc.cancer.gov/) via [cBioPortal](https://www.cbioportal.org/) |
+| **Dataset file** | `merged_tcga_data_final.csv` |
+| **Total samples** | **19,637** pathological text reports with survival outcomes |
+| **Train split** | 16,691 samples (85%) |
+| **Validation split** | 2,946 samples (15%) |
+| **Cancer types** | 24 disease types across 32 TCGA cohorts |
+| **Text column** | `text` — raw pathological report content |
+| **Survival endpoint** | Overall Survival: `OS_MONTHS` (time) + `OS_STATUS` (event: LIVING/DECEASED) |
+| **Event distribution** | ~70.7% Living / ~29.3% Deceased |
+
+**Cancer type distribution in training data:**
+
+| Disease Type | Samples | Deaths | Event Rate |
+|-------------|---------|--------|------------|
+| Adenomas and Adenocarcinomas | 8,977 | 1,944 | 21.7% |
+| Squamous Cell Neoplasms | 2,764 | 1,166 | 42.2% |
+| Ductal and Lobular Neoplasms | 2,362 | 498 | 21.1% |
+| Gliomas | 1,654 | 794 | 48.0% |
+| Cystic, Mucinous and Serous | 1,078 | 382 | 35.4% |
+| Transitional Cell Papillomas | 816 | 386 | 47.3% |
+| Others (18 types) | ~1,986 | varies | varies |
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Preprocessing
 
-#### Preprocessing [optional]
+1. **Text cleaning:** Rows with missing `text`, `OS_MONTHS`, or `OS_STATUS` dropped
+2. **Survival labels:** `OS_STATUS` mapped to binary events (`1:DECEASED` → 1.0, `0:LIVING` → 0.0)
+3. **Tokenization:** WordPiece tokenizer from Bio_ClinicalBERT, `max_length=512`, right-truncation, `max_length` padding
+4. **No text augmentation** — raw pathological reports used as-is to preserve clinical accuracy
 
-[More Information Needed]
+#### Fine-Tuning Method: LoRA (Low-Rank Adaptation)
 
+Instead of updating all 110M parameters, we use **LoRA adapters** via the [PEFT library](https://github.com/huggingface/peft) to efficiently fine-tune only ~0.5% of parameters:
 
-#### Training Hyperparameters
+| LoRA Parameter | Value |
+|---------------|-------|
+| **Rank (r)** | 8 |
+| **Alpha (α)** | 32 |
+| **Target modules** | `query`, `value` (attention layers) |
+| **Dropout** | 0.1 |
+| **Task type** | `FEATURE_EXTRACTION` |
+| **Trainable parameters** | ~590K (~0.5% of total) |
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+#### Loss Function: Cox Proportional Hazards (Cox PH)
 
-#### Speeds, Sizes, Times [optional]
+The model is trained with the **negative partial log-likelihood of the Cox PH model**, which:
+- Handles **right-censored data** (patients still alive at last follow-up)
+- Models **relative hazard** — ranking patients by risk, not predicting absolute survival time
+- Is the gold standard for survival analysis in clinical research
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+```
+L(β) = -Σ [log(h_i) - log(Σ exp(h_j))] × event_i
+        i                j∈R(t_i)
+```
 
-[More Information Needed]
+Where `h_i` is the predicted log-hazard for patient `i`, and `R(t_i)` is the risk set at time `t_i`.
 
-## Evaluation
+#### Training Hyperparameters
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+| Hyperparameter | Value |
+|---------------|-------|
+| **Optimizer** | AdamW |
+| **Learning rate** | 1e-4 |
+| **Batch size** | 8 |
+| **Max epochs** | 20 |
+| **Early stopping patience** | 3 epochs |
+| **Validation split** | 15% (random, seed=42) |
+| **Precision** | FP32 (full precision) |
+| **Gradient clipping** | None |
+| **Scheduler** | None (constant LR) |
+| **Weight decay** | AdamW default (0.01) |
+
+#### Training Results
+
+📈 **Weights & Biases Dashboard:** [View Full Training Run & Loss Curves](https://wandb.ai/dr-kareem-kamal/cancer-survival-analysis/runs/bd7qqvhj)
+
+The model was trained for **all 20 epochs** (early stopping was not triggered, indicating continuous improvement):
+
+| Epoch | Train Loss | Val Loss | Best? |
+|-------|-----------|----------|-------|
+| 1 | 1.1658 | 0.9934 | |
+| 2 | 1.0408 | 0.9006 | |
+| 3 | 0.9440 | 0.8677 | |
+| 4 | 0.8720 | 0.8249 | |
+| 5 | 0.8122 | 0.7941 | |
+| 6 | 0.7347 | 0.7653 | |
+| 7 | 0.7011 | 0.7099 | |
+| 8 | 0.6649 | 0.7331 | |
+| 9 | 0.6167 | 0.6881 | |
+| 10 | 0.5849 | 0.6672 | |
+| 11 | 0.5562 | 0.6481 | |
+| 12 | 0.5424 | 0.6050 | |
+| 13 | 0.5150 | 0.6253 | |
+| 14 | 0.4998 | 0.6108 | |
+| 15 | 0.4705 | 0.5765 | |
+| 16 | 0.4630 | 0.6028 | |
+| 17 | 0.4347 | 0.5442 | |
+| 18 | 0.4230 | 0.5298 | |
+| 19 | 0.4104 | 0.5605 | |
+| **20** | **0.4003** | **0.5290** | **✅** |
+
+**Key observations:**
+- Consistent downward trend in both train and validation loss over 20 epochs
+- Best validation loss: **0.5290** at epoch 20
+- Final training loss: **0.4003**
+- No signs of catastrophic overfitting — the gap between train/val loss remains reasonable
+- Model checkpoint saved at epoch 20 (~415 MB)
+
+#### Speeds, Sizes, Times
+
+| Property | Value |
+|----------|-------|
+| **Total training time** | ~4.5 hours (20 epochs on RTX 3090) |
+| **VRAM usage** | ~3.8 GB (FP32, batch_size=8) |
+| **Checkpoint size** | 415 MB (full state dict with LoRA adapters + risk head) |
+| **Embeddings output** | 162 MB CSV (19,637 samples × 768 dimensions + risk scores) |
+| **Throughput** | ~120 samples/second (inference) |
 
-### Testing Data, Factors & Metrics
+---
 
-#### Testing Data
+## Evaluation
 
-<!-- This should link to a Dataset Card if possible. -->
+### Metrics
 
-[More Information Needed]
+| Metric | Description |
+|--------|-------------|
+| **Cox PH Loss** | Primary training objective — negative partial log-likelihood |
+| **C-index (Concordance Index)** | How well the model ranks patients by survival (0.5 = random, >0.7 = strong) |
+| **Kaplan-Meier Curves** | Visual separation between predicted high-risk and low-risk groups |
+| **Risk Score Distribution** | Separation of scores between alive vs deceased patients |
 
-#### Factors
+### Results
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+| Metric | Value |
+|--------|-------|
+| **Best Validation Cox PH Loss** | 0.5290 |
+| **Final Training Cox PH Loss** | 0.4003 |
+| **Total epochs trained** | 20 / 20 |
+| **Embedding dimension** | 768 |
 
-[More Information Needed]
+### Evaluation Outputs
 
-#### Metrics
+The following evaluation artifacts are generated during training:
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+| File | Description |
+|------|-------------|
+| `clinicalbert_training_loss.png` | Train vs Validation loss curves with best epoch marked |
+| `clinicalbert_training_results.csv` | Per-epoch numerical loss values |
+| `finetuned_text_embeddings.csv` | 768-dim embeddings + risk scores for all 19,637 samples |
 
-[More Information Needed]
+---
 
-### Results
+## Technical Specifications
 
-[More Information Needed]
+### Model Architecture and Objective
 
-#### Summary
+```
+Input: Raw pathological text (up to 512 tokens)
+  │
+  ▼
+┌─────────────────────────────────────────────┐
+│     Bio_ClinicalBERT (Frozen backbone)      │
+│     12 Transformer layers, 768 hidden dim   │
+│     + LoRA adapters on query/value (r=8)    │
+│     ~110M total params, ~590K trainable     │
+└──────────────────┬──────────────────────────┘
+                   │
+                   ▼
+          [CLS] Token Embedding (768-dim)
+                   │
+            ┌──────┴──────┐
+            ▼             ▼
+       Risk Head     Embeddings
+    (Linear 768→1)  (768-dim vector)
+            │             │
+            ▼             ▼
+     Cox PH Loss    Downstream Tasks
+```
 
+### Compute Infrastructure
 
+#### Hardware
 
-## Model Examination [optional]
+| Component | Specification |
+|-----------|--------------|
+| **GPU** | NVIDIA GeForce RTX 3090 |
+| **GPU Memory** | 24,576 MiB (24 GB GDDR6X) |
+| **CUDA Compute Capability** | 8.6 (Ampere architecture) |
+| **NVIDIA Driver** | 580.126.09 |
+| **CUDA Version** | 12.4 (PyTorch) / 13.0 (driver) |
 
-<!-- Relevant interpretability work for the model goes here -->
+#### Software
 
-[More Information Needed]
+| Package | Version |
+|---------|---------|
+| **Python** | 3.10+ |
+| **PyTorch** | 2.6.0+cu124 |
+| **Transformers** | 5.7.0 |
+| **PEFT** | 0.19.1 |
+| **CUDA Toolkit** | 12.4 |
+| **OS** | Linux (Ubuntu) |
+| **Package Manager** | [uv](https://github.com/astral-sh/uv) |
+| **Experiment Tracking** | [Weights & Biases](https://wandb.ai/) |
+
+### How to Reproduce
+
+```bash
+# 1. Clone the repository
+git clone https://github.com/drkareemkamal/cancer-survival-analysis.git
+cd cancer-survival-analysis
+
+# 2. Set up environment with uv
+uv venv && source .venv/bin/activate
+uv sync
+
+# 3. Configure API keys in .env
+cat > .env << 'EOF'
+HF_TOKEN="hf_your_huggingface_token"
+HF_REPO_ID="your-username/your-repo-name"
+WANDB_API_KEY="your_wandb_api_key"
+WANDB_PROJECT="cancer-survival-analysis"
+EOF
+
+# 4. Run fine-tuning (baseline strategy)
+python src/training/text_finetune.py
+
+# Model will automatically push to HuggingFace Hub on completion
+```
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Fine-Tuning Strategies Available
 
-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).
+This repository implements **three fine-tuning strategies**, each with both Bio_ClinicalBERT and OpenBioLLM-8B variants:
 
-- **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]
+### Strategy 1: Pan-Cancer Baseline (This Model)
+Single model trained on all 19,637 samples. Maximum data, simplest approach.
+```bash
+python src/training/text_finetune.py
+```
 
-## Technical Specifications [optional]
+### Strategy 2: Cancer-Type Conditioning Token
+Prepends a cancer-type tag to each text to enable cancer-aware representations:
+```
+Before: "Invasive ductal carcinoma, Nottingham grade 3..."
+After:  "[DUCTAL AND LOBULAR NEOPLASMS] Invasive ductal carcinoma..."
+```
+```bash
+python src/training/text_finetune_conditioned.py
+```
 
-### Model Architecture and Objective
+### Strategy 3: Hierarchical Two-Stage
+Stage 1 trains on all cancers, Stage 2 fine-tunes per cancer type (500+ samples):
+```bash
+python src/training/text_finetune_hierarchical.py
+```
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
+## Bias, Risks, and Limitations
 
-[More Information Needed]
+### Dataset Bias
+- **Geographic bias:** TCGA data originates from US academic medical centers, which may not represent global patient populations
+- **Demographic bias:** The cohort reflects the demographics of TCGA participants and may underrepresent certain racial/ethnic groups
+- **Institutional bias:** Pathology report styles vary by institution; model performance may degrade on reports with different formatting conventions
 
-#### Hardware
+### Clinical Limitations
+- **Not a diagnostic tool** — predicts survival risk only, not disease diagnosis
+- **Text quality dependency** — performance is directly tied to report completeness and detail
+- **No external validation** — requires independent cohort validation before any clinical consideration
+- **Censoring assumptions** — Cox PH model assumes non-informative censoring, which may not always hold
 
-[More Information Needed]
+### Technical Limitations
+- **Max 512 tokens** — longer reports are truncated from the right, potentially losing relevant information
+- **Single-modality** — text-only; does not incorporate imaging, genomics, or structured clinical variables (see multimodal pipeline in repository)
+- **FP32 only** — not optimized for mixed-precision inference
 
-#### Software
+### Recommendations
 
-[More Information Needed]
+- **Always pair with clinical judgment** — this model is a decision-support tool, not a replacement for clinical expertise
+- **Validate on your institution's data** before use — report styles differ across institutions
+- **Monitor for bias** — regularly audit predictions across demographics, cancer types, and institutions
+- **Regulatory compliance** — any clinical deployment requires appropriate regulatory approval (e.g., FDA, CE marking)
 
-## Citation [optional]
+---
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@software{kamal2026cancer_survival_biobert,
+  title={Cancer Survival Prediction from Pathological Text Reports using Fine-Tuned Bio_ClinicalBERT},
+  author={Kareem Kamal},
+  year={2026},
+  url={https://huggingface.co/drkareemkamal/finetunePathologicalTextUsingBioBERT},
+  note={Fine-tuned on TCGA pathological reports with Cox PH loss and LoRA adapters, trained on NVIDIA RTX 3090}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+Kamal, K. (2026). *Cancer Survival Prediction from Pathological Text Reports using Fine-Tuned Bio_ClinicalBERT* [Computer software]. Hugging Face. https://huggingface.co/drkareemkamal/finetunePathologicalTextUsingBioBERT
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## References
 
-## More Information [optional]
+1. **Bio_ClinicalBERT:** Alsentzer, E., et al. (2019). *Publicly Available Clinical BERT Embeddings.* NAACL Clinical NLP Workshop. [HuggingFace](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) | [Paper](https://arxiv.org/abs/1904.03323)
+2. **BioBERT:** Lee, J., et al. (2020). *BioBERT: a pre-trained biomedical language representation model for biomedical text mining.* Bioinformatics, 36(4), 1234–1240. [Paper](https://arxiv.org/abs/1901.08746)
+3. **TCGA:** The Cancer Genome Atlas Research Network. [GDC Data Portal](https://portal.gdc.cancer.gov/)
+4. **cBioPortal:** Cerami, E., et al. (2012). *The cBio Cancer Genomics Portal.* Cancer Discovery, 2(5), 401–404. [Website](https://www.cbioportal.org/)
+5. **Cox PH Model:** Cox, D.R. (1972). *Regression Models and Life-Tables.* Journal of the Royal Statistical Society, Series B, 34(2), 187–220.
+6. **LoRA:** Hu, E., et al. (2022). *LoRA: Low-Rank Adaptation of Large Language Models.* ICLR 2022. [Paper](https://arxiv.org/abs/2106.09685)
+7. **PEFT:** HuggingFace. *Parameter-Efficient Fine-Tuning.* [GitHub](https://github.com/huggingface/peft)
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+- **Dr. Kareem Kamal** — [@drkareemkamal](https://github.com/drkareemkamal)
 
 ## Model Card Contact
 
-[More Information Needed]
+- **GitHub:** [github.com/drkareemkamal](https://github.com/drkareemkamal)
+- **HuggingFace:** [huggingface.co/drkareemkamal](https://huggingface.co/drkareemkamal)
+
+---
+
+*This model is for research purposes only. Always consult qualified medical professionals for clinical decisions. Not approved for clinical use.*