Update README.md

README.md

@@ -13,7 +13,7 @@ tags:
 ---
 ## ProkBERT-mini-long-phage Model
 
-This finetuned model is specifically designed for promoter identification and is based on the [ProkBERT-mini-long model](https://huggingface.co/neuralbioinfo/prokbert-mini-long). 
+This finetuned model is specifically designed for promoter identification and is based on the [ProkBERT-mini-c model](https://huggingface.co/neuralbioinfo/prokbert-mini-long). 
 
 For more details, refer to the [pahge dataset description](https://huggingface.co/datasets/neuralbioinfo/phage-test-10k) used for training and evaluating this model.
 
@@ -38,8 +38,8 @@ The following example demonstrates how to use the ProkBERT-mini-promoter model f
 from prokbert.prokbert_tokenizer import ProkBERTTokenizer
 from transformers import MegatronBertForSequenceClassification
 finetuned_model = "neuralbioinfo/prokbert-mini-long-phage"
-kmer = 6
-shift= 2
+kmer = 1
+shift= 1
 
 tok_params = {'kmer' : kmer,
              'shift' : shift}
@@ -76,7 +76,7 @@ print(outputs)
 
 ### Intended Use
 
-**Intended Use Cases:** ProkBERT-mini-phage is intended for bioinformatics researchers and practitioners focusing on genomic sequence analysis, including:
+**Intended Use Cases:** ProkBERT-mini-c-phage is intended for bioinformatics researchers and practitioners focusing on genomic sequence analysis, including:
 - sequence classification tasks
 - Exploration of genomic patterns and features