Update README.md

README.md

@@ -22,7 +22,7 @@ TCMNER is a fine-tuned BERT model that is ready to use for Named Entity Recognit
 
 Specifically, this model is a TCMRoBERTa model, a fine-tuned model of RoBERTa for Traditional Chinese medicine, that was fine-tuned on the Chinese version of the [Haiwei AI Lab](https://www.haiweikexin.com/)'s Named Entity Recognition dataset.
 
-**Currently, TCMRoBERTa is just a closed source model for my own company and will be open source in the future.**
+**Currently, TCMRoBERTa is just a closed-source model for my own company and will be open-source in the future.**
 
 
 # How to use
@@ -46,7 +46,7 @@ print(ner_results)
 
 ## Training data
 
-This model was fine-tuned on My own dataset. 
+This model was fine-tuned on MY DATASET. 
 
 Abbreviation|Description
 -|-
@@ -55,7 +55,7 @@ B-方剂 |Beginning of a prescription entity right after another prescription en
 I-方剂 | Prescription entity
 B-本草 |Beginning of a herb entity right after another herb entity
 I-本草 |Herb entity
-B-来源 |Beginning of a soure of prescription right after another soure of prescription
+B-来源 |Beginning of a source of prescription right after another source of prescription
 I-来源 |Source entity
 B-病名 |Beginning of a disease's name right after another disease's name
 I-病名 |Disease's name
@@ -79,9 +79,9 @@ I-证型 |Syndrome
 # Bonus
 
 All of our TCM domain models will be open-sourced soon, including: 
-1. pre-trained models
-2. Named entity recognition
-3. Text localization for ancient images
+1. A series of pre-trained models
+2. Named entity recognition for TCM
+3. Text localization in ancient images
 4. OCR for ancient images
    
 And so on