Update README.md

README.md

@@ -13,12 +13,10 @@ model-index:
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
 
 # bert-base-uncased-finetuned-srl_arg
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+This model is a baseline fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the English Universal Propbank dataset for the Semantics Role Labeling (SRL) task.
 It achieves the following results on the evaluation set:
 - Loss: 0.1094
 - Precision: 0.8207
@@ -28,15 +26,18 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+The appraoch used for the baseline model is basically converting the sentence into the following form:
+> [CLS] This is the sentence content [SEP] is [SEP].
 
-## Intended uses & limitations
+And this is realized by simply using the logic of the auto tokenizer: `tokenizer(list1,list2)` will return [CLS] list1 content [SEP] list2 content [SEP].
 
-More information needed
+## Usages
+
+The model labels semantics roles given input sentences. See usage examples at https://github.com/dannashao/bertsrl/blob/main/Evaluation.ipynb
 
 ## Training and evaluation data
 
-More information needed
+The English Universal Proposition Bank v1.0 data. See details at https://github.com/UniversalPropositions/UP-1.0
 
 ## Training procedure