update config

conf/config.yaml

@@ -11,6 +11,6 @@ defaults:
   - preprocess
   - train
   - embedding
-  - model: cnn
+  - model: capsule
 
 

conf/embedding.yaml

@@ -1,6 +1,6 @@
 # populated at runtime
 vocab_size: ???
-word_dim: 50
+word_dim: 60
 pos_size: ??? # 2 * pos_limit + 2
 pos_dim: 10   # 当为 sum 时，此值无效，和 word_dim 强行相同
 

conf/train.yaml

@@ -3,7 +3,7 @@ seed: 1
 use_gpu: True
 gpu_id: 0
 
-epoch: 50
+epoch: 5
 batch_size: 32
 learning_rate: 3e-4
 lr_factor: 0.7 # 学习率的衰减率

dataset.py

@@ -24,8 +24,9 @@ def collate_fn(cfg):
             if cfg.model_name != 'lm':
                 head_pos.append(_padding(data['head_pos'], max_len))
                 tail_pos.append(_padding(data['tail_pos'], max_len))
-                if cfg.use_pcnn:
-                    pcnn_mask.append(_padding(data['entities_pos'], max_len))
+                if cfg.model_name == 'cnn':
+                    if cfg.use_pcnn:
+                        pcnn_mask.append(_padding(data['entities_pos'], max_len))
 
         x['word'] = torch.tensor(word)
         x['lens'] = torch.tensor(word_len)

main.py

@@ -27,6 +27,11 @@ def main(cfg):
 
     __Model__ = {
         'cnn': models.PCNN,
+        'rnn': models.BiLSTM,
+        'transformer': models.Transformer,
+        'gcn': models.GCN,
+        'capsule': models.Capsule,
+        'lm': models.LM,
     }
 
     # device

models/__init__.py

@@ -1,2 +1,7 @@
 from .BasicModule import BasicModule
 from .PCNN import PCNN
+from .BiLSTM import BiLSTM
+from .Transformer import Transformer
+from .Capsule import Capsule
+from .GCN import GCN
+from .LM import LM

preprocess.py

@@ -30,11 +30,12 @@ def _add_pos_seq(train_data: List[Dict], cfg):
         d['tail_pos'] = list(map(lambda i: i - d['tail_idx'], list(range(d['seq_len']))))
         d['tail_pos'] = _handle_pos_limit(d['tail_pos'], int(cfg.pos_limit))
 
-        if cfg.use_pcnn:
-            # 当句子无法分隔成三段时，无法使用PCNN
-            # 比如： [head, ... tail] or [... head, tail, ...] 无法使用统一方式 mask 分段
-            d['entities_pos'] = [1] * (entities_idx[0] + 1) + [2] * (entities_idx[1] - entities_idx[0] - 1) +\
-                                [3] * (d['seq_len'] - entities_idx[1])
+        if cfg.model_name == 'cnn':
+            if cfg.use_pcnn:
+                # 当句子无法分隔成三段时，无法使用PCNN
+                # 比如： [head, ... tail] or [... head, tail, ...] 无法使用统一方式 mask 分段
+                d['entities_pos'] = [1] * (entities_idx[0] + 1) + [2] * (entities_idx[1] - entities_idx[0] - 1) +\
+                                    [3] * (d['seq_len'] - entities_idx[1])
 
 
 def _convert_tokens_into_index(data: List[Dict], vocab):