Update README.md

README.md

@@ -1,128 +1,134 @@
 ---
 library_name: transformers
-tags: []
+tags: 
+  - nucleotide-transformer
+  - AMR-prediction
+  - bioinformatics
+  - sequence-classification
+  - LoRA
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for DraGNOME-2.5b-v1
 
+This model is a fine-tuned version of the Nucleotide Transformer (2.5B parameters, multi-species) for Antimicrobial Resistance (AMR) prediction, optimized for handling class imbalance and training efficiency.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned version of InstaDeepAI's Nucleotide Transformer (2.5B parameters, multi-species) designed for binary classification of nucleotide sequences to predict Antimicrobial Resistance (AMR). It leverages LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning and includes optimizations for class imbalance and training efficiency, with checkpointing to handle Google Colab's 24-hour runtime limit. The model was trained on a dataset of positive (AMR) and negative (non-AMR) sequences.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
+- **Developed by:** Blaise Alako
 - **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Shared by [optional]:** alakob
+- **Model type:** Sequence Classification
+- **Language(s) (NLP):** Nucleotide sequences
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** InstaDeepAI/nucleotide-transformer-2.5b-multi-species
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [More Information Needed]
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be used directly for predicting whether a given nucleotide sequence is associated with Antimicrobial Resistance (AMR) without additional fine-tuning.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be further fine-tuned for specific AMR-related tasks or integrated into larger bioinformatics pipelines for genomic analysis.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not intended for general-purpose sequence analysis beyond AMR prediction, nor for non-biological sequence data. Misuse could include applying it to unrelated classification tasks where its training data and architecture are not applicable.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model may exhibit bias due to imbalances in the training dataset or underrepresentation of certain AMR mechanisms. It is limited by the quality and diversity of the training sequences and may not generalize well to rare or novel AMR variants.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Validation on diverse datasets and careful interpretation of predictions are recommended.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the code below to get started with the model:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import get_peft_model, LoraConfig
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-2.5b-multi-species")
+model = AutoModelForSequenceClassification.from_pretrained("alakob/DraGNOME-2.5b-2025-03-18_XX.XX.XX-R-32-A-64")
+
+# Example inference
+sequence = "ATGC..."  # Replace with your nucleotide sequence
+inputs = tokenizer(sequence, truncation=True, max_length=1000, return_tensors="pt")
+outputs = model(**inputs)
+prediction = outputs.logits.argmax(-1).item()  # 0 = non-AMR, 1 = AMR
 
 ## 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. -->
+The model was trained on the DraGNOME-2.5b-v1 dataset, consisting of 1200 overlapping sequences:
 
-[More Information Needed]
+- **Negative sequences (non-AMR):**  
+  `DSM_20231.fasta`, `ecoli-k12.fasta`, `FDA.fasta`
 
-### Training Procedure
+- **Positive sequences (AMR):**  
+  `28227009.fasta`, `nucleotide_fasta_protein_homolog_model_variants.fasta`, `40859916.fasta`,  
+  `nucleotide_fasta_protein_overexpression_model_variants.fasta`, `all_resfinder.fasta`,  
+  `nucleotide_fasta_protein_variant_model_variants.fasta`, `efaecium.fasta`,  
+  `nucleotide_fasta_rRNA_gene_variant_model_variants.fasta`
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Procedure
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+Sequences were tokenized using the Nucleotide Transformer tokenizer with a maximum length of 1000 tokens and truncation applied where necessary.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Training regime:** fp16 mixed precision  
+- **Learning rate:** 5e-5  
+- **Batch size:** 8 (with gradient accumulation steps = 8)  
+- **Epochs:** 10  
+- **Optimizer:** AdamW (default in Hugging Face Trainer)  
+- **Scheduler:** Linear with 10% warmup  
+- **LoRA parameters:** `r=32`, `alpha=64`, `dropout=0.1`, `target_modules=["query", "value"]`
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Training was performed on Google Colab with checkpointing every 500 steps, retaining the last 3 checkpoints.  
+Exact throughput and times depend on Colab's hardware allocation (typically T4 GPU).
 
-[More Information Needed]
+---
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The test set was derived from a 10% split of the DraGNOME-2.5b-v1 dataset, stratified by AMR labels.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Evaluation was performed across AMR and non-AMR classes.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** Proportion of correct predictions  
+- **F1 Score:** Harmonic mean of precision and recall (primary metric)  
+- **Precision:** Positive predictive value  
+- **Recall:** Sensitivity  
+- **ROC-AUC:** Area under the receiver operating characteristic curve
 
 ### Results
 
@@ -130,70 +136,81 @@ Use the code below to get started with the model.
 
 #### Summary
 
+[More Information Needed]
 
+---
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
-
 [More Information Needed]
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Environmental Impact
 
 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).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
+- **Hardware Type:** Google Colab GPU (typically NVIDIA T4)  
+- **Hours used:** [More Information Needed]  
+- **Cloud Provider:** Google Colab  
+- **Compute Region:** [More Information Needed]  
 - **Carbon Emitted:** [More Information Needed]
 
+---
+
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model uses the Nucleotide Transformer architecture (2.5B parameters) with a sequence classification head, fine-tuned with LoRA for AMR prediction.
 
 ### Compute Infrastructure
 
-[More Information Needed]
+Training was performed on Google Colab with persistent storage via Google Drive.
 
 #### Hardware
 
-[More Information Needed]
+- NVIDIA T4 GPU (typical Colab allocation)
 
 #### Software
 
-[More Information Needed]
+- Transformers (Hugging Face)  
+- PyTorch  
+- PEFT (Parameter-Efficient Fine-Tuning)  
+- Weights & Biases (wandb) for logging
 
-## 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. -->
+---
 
-**BibTeX:**
+## Citation [optional]
 
+**BibTeX:**  
 [More Information Needed]
 
-**APA:**
-
+**APA:**  
 [More Information Needed]
 
+---
+
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **AMR:** Antimicrobial Resistance  
+- **LoRA:** Low-Rank Adaptation  
+- **Nucleotide Transformer:** A transformer-based model for nucleotide sequence analysis
 
-[More Information Needed]
+---
 
 ## More Information [optional]
 
 [More Information Needed]
 
+---
+
 ## Model Card Authors [optional]
 
-[More Information Needed]
+Blaise Alako
+
+---
 
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed]