Update README.md

README.md

@@ -1,307 +1,307 @@
 ---
 language:
-- en
+  - en
 license: apache-2.0
 library_name: transformers
+base_model: zhihan1996/DNABERT-2-117M
 tags:
-- genomics
-- virology
-- dnabert
-- foundation-model
-- hvilm
-- pathogenicity
-- transmissibility
-- host-tropism
-- viral-genomics
+  - genomics
+  - virology
+  - dnabert
+  - foundation-model
+  - hvilm
+  - viral-genomics
+  - pathogenicity
+  - transmissibility
+  - host-tropism
+  - hvue-v2
 datasets:
-- VIRION
-- BV-BRC
-- VHDB
-- duttaprat/HVUE
+  - duttaprat/HVUE-v2
 pipeline_tag: feature-extraction
 widget:
-- text: "ATGCGTACGTTAGCCGATCG"
-  example_title: "Viral Sequence Example"
+  - text: "ATGCGTACGTTAGCCGATCG"
+    example_title: "Virus sequence example"
 ---
 
 # HViLM-base: A Foundation Model for Viral Genomics
 
 <div align="center">
 
-[![Paper](https://img.shields.io/badge/Paper-RECOMB%202026-blue)](https://github.com/duttaprat/HViLM)
-[![GitHub](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/duttaprat/HViLM)
+[![Preprint](https://img.shields.io/badge/bioRxiv-2026-B31B1B)](https://www.biorxiv.org/content/10.64898/2026.03.18.712700v1)
+[![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/duttaprat/HViLM)
+[![Dataset](https://img.shields.io/badge/Dataset-HVUE--v2-yellow)](https://huggingface.co/datasets/duttaprat/HVUE-v2)
 [![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](LICENSE)
-[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-HViLM--base-yellow)](https://huggingface.co/duttaprat/HViLM-base)
 
 </div>
 
+> [!IMPORTANT]
+> **HVUE v2 supersedes the original HVUE benchmark.**  
+> The original HVUE v1 benchmark contained substantial cross-split sequence similarity that could inflate held-out performance estimates. HVUE v2 was rebuilt using source-sequence clustering **before** train/validation/test assignment and chunking, followed by exact- and near-match leakage auditing.  
+> **Use [duttaprat/HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2) for current HViLM evaluation.**
+
 ## Model Description
 
-**HViLM (Human Virome Language Model)** is the first foundation model specifically designed for comprehensive viral risk assessment through multi-task prediction of pathogenicity, host tropism, and transmissibility. Built through continued pre-training of [DNABERT-2](https://github.com/MAGICS-LAB/DNABERT_2) on 5 million viral genome sequences from the [VIRION database](https://virion.verena.org), HViLM captures universal viral genomic patterns relevant for human disease risk assessment.
+**HViLM (Human Virome Language Model)** is a genomic foundation model adapted to virus sequences through continued pre-training of [DNABERT-2](https://huggingface.co/zhihan1996/DNABERT-2-117M). HViLM-base was trained on approximately **5 million non-redundant virus-derived sequence fragments** from the [VIRION](https://virion.verena.org/) resource, representing approximately 9,000 virus species across 45+ families.
+
+The architecture and tokenizer remain those of DNABERT-2; continued pre-training updates the model weights using a masked-language-modeling objective on the virus-focused corpus.
 
-**Paper**: *HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism* (RECOMB 2026)
+**Preprint:** *HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism*  
+[bioRxiv 2026.03.18.712700](https://www.biorxiv.org/content/10.64898/2026.03.18.712700v1)
 
-**Authors**: Pratik Dutta, Jack Vaska, Pallavi Surana, Rekha Sathian, Max Chao, Zhihan Zhou, Han Liu, and Ramana V. Davuluri
+**Authors:** Pratik Dutta, Jack Vaska, Pallavi Surana, Rekha Sathian, Max Chao, Zhihan Zhou, Han Liu, and Ramana V. Davuluri
 
-**Code & Benchmarks**: [GitHub Repository](https://github.com/duttaprat/HViLM)
+**Code:** [github.com/duttaprat/HViLM](https://github.com/duttaprat/HViLM)
 
 ---
 
-## Key Features
+## HViLM Model Family
 
-- 🦠 **Viral-specialized pre-training** on 5M sequences from 10.8M genomes spanning 45+ viral families
-- 🎯 **Multi-task predictions** across 3 epidemiologically critical tasks:
-  - **Pathogenicity classification**: 95.32% average accuracy
-  - **Host tropism prediction**: 96.25% accuracy
-  - **Transmissibility assessment**: 97.36% average accuracy
-- 📊 **[HVUE Benchmark](https://huggingface.co/datasets/duttaprat/HVUE)**: 7 curated datasets totaling 60K+ viral sequences
-- 🔍 **Mechanistic interpretability**: Identifies transcription factor binding site mimicry (42 conserved motifs)
-- ⚡ **Parameter-efficient fine-tuning**: LoRA adaptation (~0.3M trainable parameters per task)
-- 🚀 **State-of-the-art performance**: Outperforms Nucleotide Transformer, GENA-LM, and DNABERT-MB
+HViLM-base is the continued-pretrained foundation model. Official task-specific models fine-tuned on HVUE v2 are released as standalone checkpoints:
 
----
+| Resource | Purpose |
+|---|---|
+| [HViLM-base](https://huggingface.co/duttaprat/HViLM-base) | Continued-pretrained foundation model / sequence representations |
+| [HViLM-Patho](https://huggingface.co/duttaprat/HViLM-Patho) | Pathogenicity classification |
+| [HViLM-R0](https://huggingface.co/duttaprat/HViLM-R0) | Transmissibility classification |
+| [HViLM-Tropism](https://huggingface.co/duttaprat/HViLM-Tropism) | Human host-tropism classification |
+| [HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2) | Leakage-controlled benchmark |
 
-## Model Architecture
+The complete project is also grouped in the **HViLM: Human Virome Language Model** collection on the [duttaprat Collections page](https://huggingface.co/duttaprat/collections).
 
-HViLM is built upon **DNABERT-2** (117M parameters), which uses the MosaicBERT architecture with:
-- **Tokenization**: Byte Pair Encoding (BPE) with vocabulary size 4,096
-- **Max sequence length**: 1,000 base pairs
-- **Hidden size**: 768
-- **Attention heads**: 12
-- **Layers**: 12
-- **Positional encoding**: Attention with Linear Biases (ALiBi)
+---
 
-**Continued pre-training**:
-- **Objective**: Masked Language Modeling (MLM)
-- **Training data**: 5M viral sequence chunks (non-overlapping, 1000 bp)
-- **Data source**: VIRION database (clustered at 80% identity with MMseqs2)
-- **Training**: 10 epochs, AdamW optimizer, learning rate 5e-5
-- **Hardware**: 4x NVIDIA A100 GPUs (72 hours)
-- **Performance**: 94.2% MLM accuracy on validation set
+## Key Features
 
----
+- **Virus-focused continued pre-training:** approximately 5M non-redundant fragments derived from VIRION-linked virus sequences.
+- **DNABERT-2 initialization:** preserves the DNABERT-2 architecture and BPE tokenizer while adapting model weights to virus sequence data.
+- **Three official downstream models:** pathogenicity, transmissibility, and host tropism.
+- **HVUE v2 evaluation:** cluster-aware splitting before chunking, with multiple similarity stringencies and sequence lengths.
+- **Parameter-efficient downstream adaptation:** official task models were trained with LoRA.
+- **Public reproducibility resources:** base model, three task-specific checkpoints, HVUE v2 benchmark, and project code are released publicly.
 
-## Installation
+---
 
-```bash
-pip install transformers torch
-```
+## Model Architecture and Continued Pre-training
+
+HViLM-base is derived from **DNABERT-2 (117M parameters)**.
+
+| Property | Value |
+|---|---|
+| Architecture | MosaicBERT / DNABERT-2 |
+| Parameters | ~117M |
+| Hidden size | 768 |
+| Transformer layers | 12 |
+| Attention heads | 12 |
+| Tokenization | Byte Pair Encoding (BPE) |
+| Positional method | ALiBi |
+| Continued-pretraining objective | Masked Language Modeling |
+| Pretraining fragment length | 1000 nt |
+| Final virus-focused corpus | ~5M non-redundant fragments |
+| Redundancy reduction | MMseqs2 clustering at 80% identity / 80% coverage |
+| Optimizer | AdamW |
+| Learning rate | 5e-5 |
+| Training | 10 epochs |
+| Hardware | 4 × NVIDIA A100 GPUs |
+| Approximate training time | 72 hours |
+| Held-out MLM accuracy | 94.2% |
+
+**Sequence-length note:** HViLM uses BPE tokenization, so nucleotide length and model-token length are not equivalent. The continued-pretraining corpus used 1000-nt sequence fragments; downstream configurations are described by nucleotide length in HVUE v2.
 
 ---
 
 ## Quick Start
 
-### Basic Usage: Extract Sequence Embeddings
+### Extract sequence representations from HViLM-base
 
 ```python
-from transformers import AutoTokenizer, AutoModel
 import torch
+from transformers import AutoTokenizer, AutoModel
+
+model_id = "duttaprat/HViLM-base"
 
-# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained(
-    "duttaprat/HViLM-base",
-    trust_remote_code=True  # Required for custom architecture
+    model_id,
+    trust_remote_code=True,
 )
+
 model = AutoModel.from_pretrained(
-    "duttaprat/HViLM-base",
-    trust_remote_code=True
+    model_id,
+    trust_remote_code=True,
 )
 
-# Example: Get embeddings for a viral sequence
-viral_sequence = "ATGCGTACGTTAGCCGATCGATTACGCGTACGTAGCTAGCTAGCT"
+sequence = "ATGCGTACGTTAGCCGATCGATTACGCGTACGTAGCTAGCTAGCT"
 
-# Tokenize
 inputs = tokenizer(
-    viral_sequence,
+    sequence,
     return_tensors="pt",
     truncation=True,
-    max_length=512,
-    padding=True
+    padding=True,
 )
 
-# Generate embeddings
 with torch.no_grad():
     outputs = model(**inputs)
-    embeddings = outputs.last_hidden_state  # [batch_size, seq_len, 768]
 
-print(f"Sequence embeddings shape: {embeddings.shape}")
+token_embeddings = outputs.last_hidden_state
+print(token_embeddings.shape)
+```
+
+For sequence-level representations, pooling strategy should be chosen according to the downstream task rather than treated as a fixed property of HViLM-base.
 
-# Mean pooling for sequence-level representation
-attention_mask = inputs['attention_mask']
-mask_expanded = attention_mask.unsqueeze(-1).expand(embeddings.size()).float()
-sum_embeddings = torch.sum(embeddings * mask_expanded, dim=1)
-sum_mask = torch.clamp(mask_expanded.sum(dim=1), min=1e-9)
-mean_embeddings = sum_embeddings / sum_mask
+---
 
-print(f"Mean sequence embedding shape: {mean_embeddings.shape}")  # [batch_size, 768]
-```
+## Use the Official Fine-tuned Models
 
-### Fine-tuning on Your Own Task
+If the goal is one of the three HVUE v2 tasks, users can load the corresponding task model directly; `HViLM-base` does not need to be loaded separately.
 
-For fine-tuning HViLM on custom viral classification tasks, please refer to the [GitHub repository](https://github.com/duttaprat/HViLM) for complete training scripts and examples.
+### Pathogenicity
 
 ```python
-# Example fine-tuning setup (see GitHub for complete code)
-from transformers import AutoModel, TrainingArguments, Trainer
-from peft import LoraConfig, get_peft_model
-
-# Load base model
-model = AutoModel.from_pretrained("duttaprat/HViLM-base", trust_remote_code=True)
-
-# Configure LoRA for parameter-efficient fine-tuning
-lora_config = LoraConfig(
-    r=8,                    # rank
-    lora_alpha=16,         # scaling factor
-    target_modules=["query", "value"],  # attention layers
-    lora_dropout=0.1,
-    bias="none"
-)
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# Apply LoRA
-model = get_peft_model(model, lora_config)
+model_id = "duttaprat/HViLM-Patho"
 
-# Add classification head and train (see GitHub for details)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForSequenceClassification.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+)
 ```
 
----
+Labels:
 
-## Performance on HVUE Benchmark
+- `0` → `NON_PATHOGENIC`
+- `1` → `PATHOGENIC`
 
-### Pathogenicity Classification
+### Transmissibility
 
-| Dataset | Sequences | Accuracy | F1-Score | MCC |
-|---------|-----------|----------|----------|-----|
-| CINI | 159 | **87.74%** | 86.98 | 74.48 |
-| BVBRC-CoV | 18,066 | **98.26%** | 98.26 | 96.52 |
-| BVBRC-Calici | 31,089 | **99.95%** | 99.93 | 99.90 |
-| **Average** | **49,314** | **95.32%** | **95.06** | **90.30** |
+Use:
 
-### Host Tropism Prediction
+```text
+duttaprat/HViLM-R0
+```
 
-| Dataset | Sequences | Accuracy | F1-Score | MCC |
-|---------|-----------|----------|----------|-----|
-| VHDB | 9,428 | **96.25%** | 91.34 | 91.24 |
+Labels:
 
-### Transmissibility Assessment (R₀-based Classification)
+- `0` → `R0_LT_1`
+- `1` → `R0_GE_1`
 
-| Viral Family | Sequences | Accuracy | F1-Score | MCC |
-|--------------|-----------|----------|----------|-----|
-| Coronaviridae | ~3,000 | **97.45%** | 97.37 | 93.43 |
-| Orthomyxoviridae | ~2,500 | **95.62%** | 95.44 | 91.07 |
-| Caliciviridae | ~1,800 | **99.95%** | 99.95 | 99.90 |
-| **Average** | **~7,300** | **97.36%** | **97.59** | **94.80** |
+### Host Tropism
 
-**Comparison with baselines**: HViLM consistently outperforms Nucleotide Transformer 500M-1000g, GENA-LM, and DNABERT-MB across all tasks.
+Use:
 
----
+```text
+duttaprat/HViLM-Tropism
+```
 
-## Interpretability: Transcription Factor Mimicry
+Labels:
 
-HViLM's attention mechanisms reveal biologically meaningful pathogenicity determinants through **molecular mimicry of host regulatory elements**:
+- `0` → `NON_HUMAN_TROPIC`
+- `1` → `HUMAN_TROPIC`
 
-- **42 conserved motifs** identified in high-attention regions of pathogenic coronaviruses
-- **10 vertebrate transcription factors** targeted, including:
-  - **Irf1** (Interferon Regulatory Factor 1): 8 convergent motifs for immune evasion
-  - **Foxq1**: Multiple motifs for epithelial cell tropism
-  - **ZNF354A**: 6 motifs for chromatin regulation
-  
-This demonstrates that HViLM captures genuine biological mechanisms rather than spurious correlations.
+See the individual model cards for full usage examples and task-specific limitations.
 
 ---
 
-## Training Data
+## HVUE v2 Benchmark
 
-### Pre-training Corpus
+[HVUE v2](https://huggingface.co/datasets/duttaprat/HVUE-v2) is the current evaluation benchmark for HViLM. It replaces HVUE v1.
 
-- **Source**: [VIRION database](https://virion.verena.org) (476,242 virus-host associations)
-- **Genomes**: 10,817,265 unique NCBI accession numbers
-- **Processing**: 
-  - Segmented into non-overlapping 1000 bp chunks
-  - Clustered with MMseqs2 at 80% identity threshold
-- **Final dataset**: 5 million unique sequences
-- **Coverage**: 45+ viral families across all Baltimore classification groups
+The benchmark was reconstructed to reduce supervised train-test leakage using the following ordering:
 
+1. consolidate and deduplicate source sequences;
+2. cluster source sequences with MMseqs2;
+3. assign complete clusters to train/validation/test splits;
+4. chunk sequences only **after** split assignment;
+5. remove exact duplicate chunks;
+6. audit cross-split exact and near matches.
 
----
+HVUE v2 includes:
 
-## HVUE Benchmark Datasets
+- **Pathogenicity**
+- **Transmissibility**
+- **Host Tropism**
 
-The **Human Virome Understanding Evaluation (HVUE)** benchmark consists of 7 curated datasets:
+Across the benchmark, configurations evaluate different sequence lengths (500, 1000, and 2000 nt where applicable), sequence-similarity stringencies, and temporal generalization where reliable collection-date metadata are available.
 
-### Pathogenicity Prediction (3 datasets)
-- **CINI**: 159 sequences, 4 viral families, manual literature curation
-- **BVBRC-CoV**: 18,066 coronaviruses
-- **BVBRC-Calici**: 31,089 caliciviruses
+### Primary HViLM Results
 
-### Host Tropism Prediction (1 dataset)
-- **VHDB**: 9,428 sequences, 30 viral families
-- Binary classification: human-tropic (13.1%) vs non-human-tropic (86.9%)
+The primary results below use the **standard 1000-nt configuration** for each task.
 
-### Transmissibility Prediction (3 datasets)
-- **Coronaviridae**: R₀-based classification (R₀<1 vs R₀≥1)
-- **Orthomyxoviridae**: R₀-based classification
-- **Caliciviridae**: R₀-based classification
+| Task | HVUE v2 configuration | Accuracy | F1 | MCC | Official model |
+|---|---|---:|---:|---:|---|
+| Pathogenicity | `standard_capped_1000bp` | **92.39** | **91.32** | **83.10** | [HViLM-Patho](https://huggingface.co/duttaprat/HViLM-Patho) |
+| Transmissibility | `standard_capped_1000bp` | **87.50** | **86.16** | **72.66** | [HViLM-R0](https://huggingface.co/duttaprat/HViLM-R0) |
+| Host Tropism | `standard_95_1000bp` | **96.49** | **74.49** | **48.99** | [HViLM-Tropism](https://huggingface.co/duttaprat/HViLM-Tropism) |
 
-All datasets available at: **[🤗 duttaprat/HVUE](https://huggingface.co/datasets/duttaprat/HVUE)**
+The directory/configuration identifiers retain `bp` for release stability; manuscript and descriptive text use **nt** for nucleotide sequence length.
 
-### Download and Use
-```python
-from datasets import load_dataset
+### Interpretation of the HVUE v2 Results
 
-# Load specific task
-host_tropism = load_dataset("duttaprat/HVUE", data_dir="Host_Tropism")
-pathogenicity = load_dataset("duttaprat/HVUE", data_dir="Pathogenecity")
-transmissibility = load_dataset("duttaprat/HVUE", data_dir="Transmissibility")
+Under leakage-controlled evaluation, the effect of virus-focused continued pre-training is **task dependent**:
 
-# Load specific split
-train_data = load_dataset("duttaprat/HVUE", data_files="Host_Tropism/train.csv")
-```
+- **Pathogenicity:** HViLM improves F1 by 1.28 points over vanilla DNABERT-2 (91.32 vs. 90.04).
+- **Transmissibility:** HViLM and DNABERT-2 are close (86.16 vs. 85.81 F1), and HViLM is essentially tied with DNABERT-MB (86.16 vs. 86.15 F1).
+- **Host Tropism:** HViLM shows the largest F1 improvement, reaching 74.49 compared with 64.82 for class-balanced DNABERT-2.
+
+These results support a more specific conclusion than the original HVUE v1 evaluation: virus-focused continued pre-training provides its clearest benefit on the more challenging Host Tropism task, while gains on Pathogenicity and Transmissibility are smaller.
+
+For complete baseline comparisons, hard-split evaluations, temporal evaluations, and sequence-length analyses, see the [HViLM GitHub repository](https://github.com/duttaprat/HViLM) and [HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2).
 
 ---
 
-## Reproducing Paper Results
+## Training Data
 
-### Step 1: Download HVUE Benchmark
-```python
-from datasets import load_dataset
+### Continued-pretraining corpus
 
-# Download all datasets
-host_tropism = load_dataset("duttaprat/HVUE", data_dir="Host_Tropism")
-pathogenicity = load_dataset("duttaprat/HVUE", data_dir="Pathogenecity")
-transmissibility = load_dataset("duttaprat/HVUE", data_dir="Transmissibility")
-```
+HViLM-base was trained using virus sequences associated with the **VIRION** resource.
+
+Processing included:
 
-### Step 2: Fine-tune and Evaluate
+- retrieval and quality control of VIRION-linked nucleotide sequences;
+- removal of short sequences and exact duplicates;
+- segmentation into non-overlapping 1000-nt fragments;
+- MMseqs2 clustering at 80% sequence identity and 80% coverage;
+- selection of approximately 5M representative fragments for continued pre-training.
 
-To reproduce the results reported in the paper, clone the repository and follow the fine-tuning instructions:
+The corpus spans approximately 9,000 virus species and 45+ virus families across the Baltimore classification groups.
 
+---
 
+## Interpretability
 
-```bash
-# Clone repository
-git clone https://github.com/duttaprat/HViLM.git
-cd HViLM
+Attention-guided analyses associated with the HViLM study identified **candidate sequence motifs** in pathogenic coronavirus sequences, including motifs with similarity to vertebrate transcription-factor binding motifs such as IRF1, FOXQ1, and ZNF354A.
 
-# Install dependencies
-pip install -r requirements.txt
+These observations are **hypothesis-generating**. Sequence similarity between virus motifs and host transcription-factor binding motifs does not by itself establish molecular mimicry, causal regulation, immune evasion, or another biological mechanism. Experimental validation and additional controls are required for mechanistic interpretation.
 
-# Reproduce pathogenicity results on CINI dataset
-cd finetune
-bash scripts/run_patho_cini.sh
+---
 
-# Reproduce host tropism results
-bash scripts/run_tropism_vhdb.sh
+## Limitations
 
-# Reproduce transmissibility results
-bash scripts/run_r0_coronaviridae.sh
-```
+- HVUE v2 controls supervised split leakage through source-level clustering and auditing, but sequence-similarity thresholds cannot eliminate every form of biological relatedness.
+- The complete historical training exposure of the original DNABERT-2 model cannot be reconstructed; therefore, absence of all possible ancestral pretraining exposure to benchmark-related sequences cannot be guaranteed.
+- Host association is biologically context-dependent and may include multi-host, zoonotic, and reverse-zoonotic relationships; the benchmark uses a simplified binary formulation.
+- R₀-based transmissibility labels simplify a continuous, context-dependent epidemiological quantity into a binary benchmark task.
+- Performance differences between closely matched models should not be interpreted as statistically meaningful without uncertainty estimates or repeated evaluations.
+- Attention-based motif analyses should be considered exploratory rather than direct evidence of mechanism.
+- HViLM predictions are research outputs and are not intended to replace experimental, clinical, epidemiological, or public-health assessment.
+
+---
+
+## Reproducibility and Resources
 
-For detailed instructions, see the [GitHub repository](https://github.com/duttaprat/HViLM).
+- **Base model:** [duttaprat/HViLM-base](https://huggingface.co/duttaprat/HViLM-base)
+- **Pathogenicity model:** [duttaprat/HViLM-Patho](https://huggingface.co/duttaprat/HViLM-Patho)
+- **Transmissibility model:** [duttaprat/HViLM-R0](https://huggingface.co/duttaprat/HViLM-R0)
+- **Host Tropism model:** [duttaprat/HViLM-Tropism](https://huggingface.co/duttaprat/HViLM-Tropism)
+- **Benchmark:** [duttaprat/HVUE-v2](https://huggingface.co/datasets/duttaprat/HVUE-v2)
+- **Code:** [github.com/duttaprat/HViLM](https://github.com/duttaprat/HViLM)
+- **Collections:** [duttaprat's Hugging Face Collections](https://huggingface.co/duttaprat/collections)
 
 ---
 
 ## Citation
-If you use HViLM in your research, please cite our paper:
+
+If you use HViLM in your research, please cite:
 
 ```bibtex
 @article{dutta2026hvilm,
@@ -313,37 +313,37 @@ If you use HViLM in your research, please cite our paper:
   publisher={Cold Spring Harbor Laboratory}
 }
 ```
+
+If you use DNABERT-2 directly or build on its architecture, please also cite the DNABERT-2 publication.
+
 ---
 
 ## Model Card Authors
 
-- **Pratik Dutta** (Senior Research Scientist, Stony Brook University)
-- **Ramana V. Davuluri** (Professor, Stony Brook University)
+- **Pratik Dutta** — Stony Brook University
+- **Ramana V. Davuluri** — Stony Brook University
 
 ---
 
 ## Contact
 
-- **Email**: pratik.dutta@stonybrook.edu
-- **Lab**: [Davuluri Lab, Stony Brook University](https://davulurilab.github.io/)
-- **GitHub Issues**: [Report bugs or request features](https://github.com/duttaprat/HViLM/issues)
+- **GitHub Issues:** [github.com/duttaprat/HViLM/issues](https://github.com/duttaprat/HViLM/issues)
+- **Lab:** [Davuluri Lab, Stony Brook University](https://davulurilab.github.io/)
 
 ---
 
 ## Acknowledgments
 
-This work builds upon [DNABERT-2](https://github.com/MAGICS-LAB/DNABERT_2) by Zhou et al. Pre-training data from the [VIRION database](https://virion.verena.org) maintained by the Viral Emergence Research Initiative (Verena).
-
-
+HViLM builds on DNABERT-2 by Zhou et al. Continued-pretraining data were derived from the VIRION resource maintained by the Viral Emergence Research Initiative (Verena).
 
 ---
 
 ## License
 
-This model is released under the **Apache License 2.0**.
+HViLM-base is released under the **Apache License 2.0**.
 
 ---
 
 ## Disclaimer
 
-HViLM is a research tool for computational biology and should not be used as the sole basis for clinical or public health decisions. Predictions should be validated through experimental methods and expert analysis.
+HViLM is a research model for computational biology. It should not be used as the sole basis for clinical, diagnostic, epidemiological, biosurveillance, or public-health decisions. Model outputs should be interpreted alongside appropriate biological evidence and expert assessment.