Update README.md

README.md

@@ -11,7 +11,7 @@ pipeline_tag: fill-mask
 ---
 # 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.
+**Herbert** 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.
 
 ---
 
@@ -22,7 +22,7 @@ This model is optimized for TCM-related tasks, including but not limited to:
 - Domain-specific word embedding
 - Classification, labeling, and sequence prediction tasks in TCM research
 
-Herberta combines the strengths of modern pretraining techniques and domain knowledge, allowing it to excel in TCM-related text processing tasks.
+Herbert combines the strengths of modern pretraining techniques and domain knowledge, allowing it to excel in TCM-related text processing tasks.
 
 ---
 
@@ -40,9 +40,6 @@ Herberta combines the strengths of modern pretraining techniques and domain know
 }
 ### requirements
 "transformers_version": "4.45.1"
-```bash
-pip install herberta
-```
 
 ###  Quickstart
 
@@ -92,9 +89,8 @@ outputs = model(**inputs)
 If you find our work helpful, feel free to give us a cite.
 
 ```bibtex
-@misc{herberta-embedding,
-  title = {Herberta: A Pretrain_Bert_Model for TCM_herb and downstream Tasks as Text Embedding Generation},
-  url = {https://github.com/15392778677/herberta},
+@misc{herbert-embedding,
+  title = {Herbert: A Pretrain_Bert_Model for TCM_herb and downstream Tasks as Text Embedding Generation},
   author = {Yehan Yang,Xinhan Zheng},
   month = {December},
   year = {2024}