update reproduction

fastNLP/io/embed_loader.py

@@ -101,9 +101,12 @@ class EmbedLoader(BaseLoader):
         """
         if vocab is None:
             raise RuntimeError("You must provide a vocabulary.")
-        embedding_matrix = np.zeros(shape=(len(vocab), emb_dim))
+        embedding_matrix = np.zeros(shape=(len(vocab), emb_dim), dtype=np.float32)
         hit_flags = np.zeros(shape=(len(vocab),), dtype=int)
         with open(emb_file, "r", encoding="utf-8") as f:
+            startline = f.readline()
+            if len(startline.split()) > 2:
+                f.seek(0)
             for line in f:
                 word, vector = EmbedLoader.parse_glove_line(line)
                 if word in vocab:

reproduction/Biaffine_parser/cfg.cfg

@@ -26,7 +26,7 @@ arc_mlp_size = 500
 label_mlp_size = 100
 num_label = -1
 dropout = 0.3
-encoder="transformer"
+encoder="var-lstm"
 use_greedy_infer=false
 
 [optim]

reproduction/Biaffine_parser/run.py

@@ -10,9 +10,13 @@ from fastNLP.core.trainer import Trainer
 from fastNLP.core.instance import Instance
 from fastNLP.api.pipeline import Pipeline
 from fastNLP.models.biaffine_parser import BiaffineParser, ParserMetric, ParserLoss
+from fastNLP.core.vocabulary import Vocabulary
+from fastNLP.core.dataset import DataSet
 from fastNLP.core.tester import Tester
 from fastNLP.io.config_io import ConfigLoader, ConfigSection
 from fastNLP.io.model_io import ModelLoader
+from fastNLP.io.embed_loader import EmbedLoader
+from fastNLP.io.model_io import ModelSaver
 from fastNLP.io.dataset_loader import ConllxDataLoader
 from fastNLP.api.processor import *
 from fastNLP.io.embed_loader import EmbedLoader
@@ -156,6 +160,8 @@ print('test len {}'.format(len(test_data)))
 def train(path):
     # test saving pipeline
     save_pipe(path)
+    embed = EmbedLoader.fast_load_embedding(model_args['word_emb_dim'], emb_file_name, word_v)
+    embed = torch.tensor(embed, dtype=torch.float32)
 
     # embed = EmbedLoader.fast_load_embedding(emb_dim=model_args['word_emb_dim'], emb_file=emb_file_name, vocab=word_v)
     # embed = torch.tensor(embed, dtype=torch.float32)