Update README.md

README.md

@@ -2,4 +2,61 @@
 license: mit
 base_model:
 - zhihan1996/DNABERT-2-117M
----
+tags:
+- genomics
+- bioinformatics
+- DNA
+- sequence-classification
+- introns
+- exons
+- DNABERT2
+---
+# Exons and Introns Classifier
+
+BERT finetuned model for **classifying DNA sequences** into **introns** and **exons**, trained on a large cross-species GenBank dataset.
+
+## Architecture
+- Base model: DNABERT2
+- Approach: Full-sequence classification
+- Framework: PyTorch + Hugging Face Transformers
+   
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+tokenizer = AutoTokenizer.from_pretrained("GustavoHCruz/ExInDNABERT2")
+model = AutoModelForSequenceClassification.from_pretrained("GustavoHCruz/ExInDNABERT2")
+```
+
+Prompt format:
+
+The model expects nucleotide sequences.
+
+The model should predict the next token as the class label: 0 (Intron) or 1 (Exon).
+
+## Data
+
+The model was trained on a processed version of GenBank sequences spanning multiple species.
+
+## Publications
+
+- **Full Paper – 2nd Place (National)**  
+  Achieved **2nd place** at the _Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2025)_, organized by the Brazilian Computer Society (SBC), held in Fortaleza, Ceará, Brazil.  
+  [https://doi.org/10.5753/kdmile.2025.247575](https://doi.org/10.5753/kdmile.2025.247575)
+- **Short Paper (International)**  
+  Presented at the _IEEE International Conference on Bioinformatics and BioEngineering (BIBE 2025)_, held in Athens, Greece.  
+  [https://doi.org/10.1109/BIBE66822.2025.00113](https://doi.org/10.1109/BIBE66822.2025.00113)
+  
+## Training
+
+- Trained on an architecture with 8x H100 GPUs.
+
+## GitHub Repository
+
+The full code for **data processing, model training, and inference** is available on GitHub:  
+[CodingDNATransformers](https://github.com/GustavoHCruz/CodingDNATransformers)
+
+You can find scripts for:  
+- Preprocessing GenBank sequences  
+- Fine-tuning models  
+- Evaluating and using the trained models