Update README.md

README.md

@@ -10,9 +10,6 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # AmpGPT2
 
 AmpGPT2 is a language model capable of generating de novo antimicrobial peptides (AMPs). Generated sequences are predicted to be AMPs 95.83% of the time.
@@ -20,13 +17,20 @@ AmpGPT2 is a language model capable of generating de novo antimicrobial peptides
 ## Model description
 
 AmpGPT2 is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) based on the GPT2 Transformer architecture. 
-To validate the results the Antimicrobial Peptide Scanner vr.2 (https://www.dveltri.com/ascan/v2/ascan.html) was used. It is a 
+
+To validate the results the Antimicrobial Peptide Scanner vr.2 (https://www.dveltri.com/ascan/v2/ascan.html) was used.
+It is a deep learning tool specifically designed for AMP recognition.
+
 
 ## Training and evaluation data
 
 AmpGPT2 was trained using 32014 AMP sequences from the Compass (https://compass.mathematik.uni-marburg.de/) database. 
 
 ## How to use AmpGPT2 
+
+The example code below contains the ideal generation settings found while testing. 
+The 'num_return_sequences' parameter specifies the amount of sequences generated. When generating more than 100 sequences at the same time, I recommend doing it in batches.
+The results can then be checked with the peptide scanner (https://www.dveltri.com/ascan/v2/ascan.html).
 ```
 from transformers import pipeline
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
@@ -44,6 +48,7 @@ for i, seq in enumerate(amp_sequences):
 
     print(f">{sequence_identifier}\n{sequence}")
 ```
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -55,8 +60,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 50.0
 
+The model was trained on four NVIDIA A100 GPUs.
+
 ### Training results
-these are the training losses after the final epoch
 
 | Training Loss | Epoch | Validation Loss | Accuracy |
 |:-------------:|:-----:|:---------------:|:--------:|