Update README.md

README.md

@@ -6,14 +6,19 @@ tags:
 model-index:
 - name: bert-base-uncased-finetuned-negation_scope
   results: []
+datasets:
+- dannashao/sem2012forNegbert
+language:
+- en
+metrics:
+- dannashao/span_metric
+pipeline_tag: token-classification
 ---
 
-<!-- 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-negation_scope
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the [SEM 2012 shared task](http://www.clips.ua.ac.be/sem2012-st-neg/) corpus (cd-sco).
 It achieves the following results on the evaluation set:
 - Loss: 0.0618
 - Token Precision: 0.9190
@@ -25,15 +30,20 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+We follow the Augment method described in [NegBERT (Khandelwal, et al. 2020)](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.704.pdf).
+That is, adding a special token ([NEG]) immediately before the predicate:
+> This is [NEG] not a sentence.
+
+Note that **the special token and the predicate is considered a whole**. That is, the actual sentence is like
+> 'This' 'is' **'[NEG] not'** 'a' 'sentence' '.'
 
 ## Intended uses & limitations
 
-More information needed
+See details at https://github.com/dannashao/portfolio-NLP/blob/main/NEG/Fine%20tune%20BERT.ipynb
 
 ## Training and evaluation data
 
-More information needed
+See details at https://www.clips.ua.ac.be/sem2012-st-neg/
 
 ## Training procedure
 
@@ -62,4 +72,4 @@ The following hyperparameters were used during training:
 - Transformers 4.37.0
 - Pytorch 2.0.1+cu117
 - Datasets 2.16.1
-- Tokenizers 0.15.1
+- Tokenizers 0.15.1