Update README.md

README.md

@@ -29,11 +29,11 @@ Here are scores on the devlopment set of six Chinese tasks:
 
 |Model|Score|douban|chnsenticorp|lcqmc|tnews(CLUE)|iflytek(CLUE)|ocnli(CLUE)|
 |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-|BERT-Tiny|0.0|83.0|91.4|81.8|62.0|55.0|60.3|
-|BERT-Mini|0.0|84.8|93.7|86.1|63.9|58.3|67.4|
-|BERT-Small|0.0|86.5|93.4||65.1|59.4|69.7|
-|BERT-Medium|0.0|87.6|94.8|88.1|65.6|59.5|71.2|
-|BERT-Base|0.0|89.1|95.2|89.2|67.0|60.9|75.5|
+|BERT-Tiny|72.3|83.0|91.4|81.8|62.0|55.0|60.3|
+|BERT-Mini|75.7|84.8|93.7|86.1|63.9|58.3|67.4|
+|BERT-Small|0.0|86.5|93.4|0.0|65.1|59.4|69.7|
+|BERT-Medium|77.8|87.6|94.8|88.1|65.6|59.5|71.2|
+|BERT-Base|79.5|89.1|95.2|89.2|67.0|60.9|75.5|
 
 For each task, we selected the best fine-tuning hyperparameters from the lists below:
 - epochs: 3, 5, 8
@@ -94,8 +94,6 @@ encoded_input = tokenizer(text, return_tensors='tf')
 output = model(encoded_input)
 ```
 
-
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 ## Training data
 
 CLUECorpusSmall is used as training data. We found that models pre-trained on CLUECorpusSmall outperform those pre-trained on CLUECorpus2020, although CLUECorpus2020 is much larger than CLUECorpusSmall.