Update README.md

README.md

@@ -44,35 +44,6 @@ All the above experiments were tested on GTX 1080 GPU with memory 8000MiB.
 This section provides a solution to visualize the BERT attention matrix.
 For more detail, you can check dictionary "BERT-GCN". 
 
-2020/5/11: add TextGCN and TextSAGE for text classification.
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-2020/5/5: add GIN, GraphSAGE for graph classfication.
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-2020/4/25: add GAN, GIN model, based on message passing methods.
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-2020/4/23: add GCN model, based on message passing methods.
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-2020/4/16：currently focusing on models of GNN in nlp, and trying to integrate some GNN models into fennlp.
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-2020/4/2: add GPT2 model, could used parameters released by OpenAI (base,medium,large). 
-More detail reference dictionary "TG/EN/interactive.py"
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-2020/3/26: add Bilstm+Attention example for classification
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-2020/3/23: add RAdam optimizer.
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-2020/3/19: add test example "albert_ner_train.py" "albert_ner_test.py"
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-2020/3/16: add model for training sub word embedding based on bpe methods.
-The trained embedding is used in TextCNN model for improve it's improvement.
-See "tran_bpe_embeding.py" for more details.
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-2020/3/8: add test example "run_tucker.py" for train TuckER on WN18.
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-2020/3/3: add test example "tran_text_cnn.py" for train TextCNN model. 
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-2020/3/2: add test example "train_bert_classification.py" for text classification based on bert.
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 # Requirement
 * tensorflow-gpu>=2.0
 * typeguard