Update README.md

README.md

@@ -9,7 +9,7 @@ language:
 - zh
 pipeline_tag: fill-mask
 ---
-# Herberta: Pretrained Language Model for Herbal Medicine
+# Herbert: Pretrained Bert Model for Herbal Medicine
 
 **Herberta** is a pretrained model for herbal medicine research, developed based on the `bert-base-chinese` model. The model has been fine-tuned on domain-specific data from 675 ancient books and 32 Traditional Chinese Medicine (TCM) textbooks. It is designed to support a variety of TCM-related NLP tasks.
 
@@ -50,8 +50,8 @@ pip install herberta
 ```python
 from transformers import AutoTokenizer, AutoModel
 
-# Replace "XiaoEnn/herberta" with the Hugging Face model repository name
-model_name = "XiaoEnn/herberta"
+# Replace "Chengfengke/herbert" with the Hugging Face model repository name
+model_name = "Chengfengke/herbert"
 
 # Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -80,7 +80,7 @@ print("Embedding vector:", sentence_embedding)
 from transformers import BertTokenizer, BertForMaskedLM
 
 # Load the model and tokenizer
-model_name = "Chengfengke/herberta"
+model_name = "Chengfengke/herbert"
 tokenizer = BertTokenizer.from_pretrained(model_name)
 model = BertForMaskedLM.from_pretrained(model_name)
 inputs = tokenizer("This is an example text for herbal medicine.", return_tensors="pt")