Update README.md

README.md

@@ -38,7 +38,7 @@ This model is a fine-tuned version of InstaDeepAI's Nucleotide Transformer (2.5B
 
 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]
+### Downstream Use 
 
 The model can be further fine-tuned for specific AMR-related tasks or integrated into larger bioinformatics pipelines for genomic analysis.
 
@@ -71,13 +71,12 @@ 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
 
-The model was trained on the DraGNOME-2.5b-v1 dataset, consisting of 1200 overlapping sequences:
-
 - **Negative sequences (non-AMR):**  
   `DSM_20231.fasta`, `ecoli-k12.fasta`, `FDA.fasta`
 
@@ -89,7 +88,7 @@ The model was trained on the DraGNOME-2.5b-v1 dataset, consisting of 1200 overla
 
 ### Training Procedure
 
-#### Preprocessing [optional]
+#### Preprocessing
 
 Sequences were tokenized using the Nucleotide Transformer tokenizer with a maximum length of 1000 tokens and truncation applied where necessary.
 
@@ -103,10 +102,10 @@ Sequences were tokenized using the Nucleotide Transformer tokenizer with a maxim
 - **Scheduler:** Linear with 10% warmup  
 - **LoRA parameters:** `r=32`, `alpha=64`, `dropout=0.1`, `target_modules=["query", "value"]`
 
-#### Speeds, Sizes, Times [optional]
+#### Speeds, Sizes, Times
 
 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).
+Exact throughput and times depend on Colab's hardware allocation NVIDIA A100 GPU.
 
 ---
 
@@ -150,7 +149,7 @@ Evaluation was performed across AMR and non-AMR classes.
 
 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:** Google Colab GPU (typically NVIDIA T4)  
+- **Hardware Type:** Google Colab NVIDIA A100 GPU  
 - **Hours used:** [More Information Needed]  
 - **Cloud Provider:** Google Colab  
 - **Compute Region:** [More Information Needed]  
@@ -170,7 +169,7 @@ Training was performed on Google Colab with persistent storage via Google Drive.
 
 #### Hardware
 
-- NVIDIA T4 GPU (typical Colab allocation)
+- NVIDIA A100 GPU 
 
 #### Software
 
@@ -191,7 +190,7 @@ Training was performed on Google Colab with persistent storage via Google Drive.
 
 ---
 
-## Glossary [optional]
+## Glossary 
 
 - **AMR:** Antimicrobial Resistance  
 - **LoRA:** Low-Rank Adaptation  
@@ -205,7 +204,7 @@ Training was performed on Google Colab with persistent storage via Google Drive.
 
 ---
 
-## Model Card Authors [optional]
+## Model Card Authors
 
 Blaise Alako