Update README.md

README.md

@@ -1,11 +1,18 @@
 ---
 license: cc-by-nc-sa-4.0
 widget:
-- text: AATTTTAACTAGCCCCTTCGGCCCTTCCCATCGACATATATACGAAGAGACAAAACAACATATCAACAGAATGTCAGAATTACAGACACCACGCTTGACATGTCTGTGACGCAGACCATAGAGGATGTGTCATGTTCATGTGTCCAATGGGGGCAATGGTATTGCAAGGGCACAAAATACTGCTAACATGTTTCGTAGCGCTATAGGTTACAGAGGTCATGACGTTAT
+- text: >-
+    AATTTTAACTAGCCCCTTCGGCCCTTCCCATCGACATATATACGAAGAGACAAAACAACATATCAACAGAATGTCAGAATTACAGACACCACGCTTGACATGTCTGTGACGCAGACCATAGAGGATGTGTCATGTTCATGTGTCCAATGGGGGCAATGGTATTGCAAGGGCACAAAATACTGCTAACATGTTTCGTAGCGCTATAGGTTACAGAGGTCATGACGTTAT
 tags:
 - DNA
 - biology
 - genomics
+datasets:
+- zhangtaolab/plant-multi-species-histone-modifications
+metrics:
+- accuracy
+base_model:
+- zhangtaolab/plant-nucleotide-transformer-BPE
 ---
 # Plant foundation DNA large language models
 
@@ -38,7 +45,7 @@ Here is a simple code for inference:
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
 
-model_name = 'plant-nucleotide-transformer-H3K27me3'
+model_name = 'plant-nucleotide-transformer-BPE-H3K27me3'
 # load model and tokenizer
 model = AutoModelForSequenceClassification.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(f'zhangtaolab/{model_name}', trust_remote_code=True)
@@ -60,4 +67,4 @@ Detailed training procedure can be found in our manuscript.
 
 
 #### Hardware
-Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).
+Model was trained on a NVIDIA GTX1080Ti GPU (11 GB).