add_squence_extraction

examples/causal_generation.py

@@ -0,0 +1,46 @@
+# -*-coding:utf-8-*-
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..")))
+import argparse
+from openks.models import OpenKSModel
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--num_epoch", type=int, default=50, help="Number of epoches for fine-tuning.")
+parser.add_argument("--learning_rate", type=float, default=1e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--train_data", type=str, default='../openks/data/data_for_causality_extraction/train-corpus.json', help="train data")
+parser.add_argument("--test_data", type=str, default='../openks/data/data_for_causality_extraction/test-corpus.json', help="test data")
+
+parser.add_argument("--predict_save_path", type=str, default='../openks/data/data_for_causality_extraction/predict.json', help="predict data save path")
+parser.add_argument("--predict_data", type=str, default='../openks/data/data_for_causality_extraction/test-corpus.json', help="predict data")
+parser.add_argument("--MLP_save_path", type=str, default='checkpoints/MLP', help="predict data")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--warmup_proportion", type=float, default=0.1,
+                    help="Warmup proportion params for warmup strategy")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--valid_step", type=int, default=100, help="validation step")
+parser.add_argument("--skip_step", type=int, default=20, help="skip step")
+parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
+parser.add_argument("--checkpoints", type=str, default='checkpoints/Erine', help="Directory to model checkpoint")
+parser.add_argument("--init_ckpt", type=str, default='checkpoints',
+                    help="already pretraining trigger detection model checkpoint")
+parser.add_argument("--seed", type=int, default=1000, help="random seed for initialization")
+parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu",
+                    help="Select which device to train model, defaults to gpu.")
+
+args = parser.parse_args()
+
+platform = 'Paddle'
+executor = 'Causality_Extraction'
+model = 'Causality_Extraction'
+print("根据配置，使用 {} 框架，{} 执行器训练 {} 模型。".format(platform, executor, model))
+print("-----------------------------------------------")
+# 模型训练
+executor = OpenKSModel.get_module(platform, executor)
+Event_Extraction = executor(args=args)
+Event_Extraction.run()
+
+print("-----------------------------------------------")
+
+print("-----------------------------------------------")

examples/sequence_extraction.py

@@ -0,0 +1,51 @@
+# -*-coding:utf-8-*-
+import sys
+import os
+sys.path.append(os.path.abspath(os.path.join(__file__, "..", "..")))
+import argparse
+from openks.models import OpenKSModel
+
+
+
+parser = argparse.ArgumentParser()
+parser = argparse.ArgumentParser()
+parser.add_argument("--num_epoch", type=int, default=5, help="Number of epoches for fine-tuning.")
+parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
+parser.add_argument("--train_data_path", type=str, default='data/SeRI_mod/train_pair.dat',
+                    help="train_data_path")
+parser.add_argument("--test_data_path", type=str, default='data/SeRI_mod/test_pair.dat', help="test_data_path")
+
+parser.add_argument("--predict_data", type=str, default='', help="predict data")
+parser.add_argument("--do_train", type=ast.literal_eval, default=True, help="do train")
+parser.add_argument("--do_predict", type=ast.literal_eval, default=True, help="do predict")
+parser.add_argument("--weight_decay", type=float, default=0.01, help="Weight decay rate for L2 regularizer.")
+parser.add_argument("--warmup_proportion", type=float, default=0.1,
+                    help="Warmup proportion params for warmup strategy")
+parser.add_argument("--max_seq_len", type=int, default=512, help="Number of words of the longest seqence.")
+parser.add_argument("--valid_step", type=int, default=100, help="validation step")
+parser.add_argument("--skip_step", type=int, default=20, help="skip step")
+parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
+parser.add_argument("--checkpoints", type=str, default='checkpoints/sub_Erine',
+                    help="Directory to model checkpoint(save model)")
+parser.add_argument("--init_ckpt", type=str, default='checkpoints/sub_Erine',
+                    help="already pretraining model checkpoint()")
+parser.add_argument("--predict_save_path", type=str, default='data/predict.json', help="predict data save path")
+parser.add_argument("--seed", type=int, default=1000, help="random seed for initialization")
+parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu",
+                    help="Select which device to train model, defaults to gpu.")
+
+args = parser.parse_args()
+
+args = parser.parse_args()
+
+platform = 'Paddle'
+executor = 'Sequence_Extraction'
+model = 'Sequence_Extraction'
+print("根据配置，使用 {} 框架，{} 执行器训练 {} 模型。".format(platform, executor, model))
+print("-----------------------------------------------")
+# 模型训练
+executor = OpenKSModel.get_module(platform, executor)
+Event_Extraction = executor(args=args)
+Event_Extraction.run()
+
+print("-----------------------------------------------")