add the support token_embedding of express (#5058)

PaddleNLP/examples/named_entity_recognition/express_ner/run_bigru_crf.py

@@ -18,6 +18,7 @@ import paddle.nn as nn
 from paddlenlp.data import Stack, Tuple, Pad
 from paddlenlp.layers import LinearChainCrf, ViterbiDecoder, LinearChainCrfLoss
 from paddlenlp.metrics import ChunkEvaluator
+from paddlenlp.embeddings import TokenEmbedding
 
 
 def parse_decodes(ds, decodes, lens):
@@ -95,9 +96,18 @@ class ExpressDataset(paddle.io.Dataset):
 
 
 class BiGRUWithCRF(nn.Layer):
-    def __init__(self, emb_size, hidden_size, word_num, label_num):
+    def __init__(self,
+                 emb_size,
+                 hidden_size,
+                 word_num,
+                 label_num,
+                 use_w2v_emb=False):
         super(BiGRUWithCRF, self).__init__()
-        self.word_emb = nn.Embedding(word_num, emb_size)
+        if use_w2v_emb:
+            self.word_emb = TokenEmbedding(
+                extended_vocab_path='./conf/word.dic', unknown_token='OOV')
+        else:
+            self.word_emb = nn.Embedding(word_num, emb_size)
         self.gru = nn.GRU(emb_size,
                           hidden_size,
                           num_layers=2,
@@ -153,7 +163,7 @@ if __name__ == '__main__':
     model = paddle.Model(network)
 
     optimizer = paddle.optimizer.Adam(
-        learning_rate=0.002, parameters=model.parameters())
+        learning_rate=0.001, parameters=model.parameters())
     crf_loss = LinearChainCrfLoss(network.crf.transitions)
     chunk_evaluator = ChunkEvaluator((train_ds.label_num + 2) // 2, 'IOB')
     model.prepare(optimizer, crf_loss, chunk_evaluator)