update performance

CogNLP · Jul 31, 2022 · bcfb653 · bcfb653
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diff --git a/experiments/performance_table.md b/experiments/performance_table.md
@@ -4,20 +4,20 @@
 
 ### 1.1 SST-2 (DEV)
 
-| Model | P | R | F1 | Acc |
-|---|---|---|---|---|
-| bert-base-cased | 0.90343 | 0.94819 | 0.92527 | 0.92201 |
-| bert-base-cased+KT-Emb | 8 | 9 | 6 | 6 |
-| bert-base-cased+KG-Emb | 8 | 9 | 6 | 6 |
-| bert-base-cased+KT-Attn | 8 | 9 | 6 | 6 |
+| Model | P | R | F1 | Acc | file |
+|---|---|---|---|---|---|
+| bert-base-cased | 0.90343 | 0.94819 | 0.92527 | 0.92201 | sst2_bert_base_cased.py |
+| bert-base-cased+KT-Emb | 8 | 9 | 6 | 6 | 6 |
+| bert-base-cased+KG-Emb | 8 | 9 | 6 | 6 | 6 |
+| bert-base-cased+KT-Attn | 8 | 9 | 6 | 6 | 6 |
 
-### 1.2 SST-5
+### 1.2 SST-5 (DEV)
 
-| Model | micro_P | micro_R | micro_F1 | macro_P | macro_R | macro_F1 | Acc |
-|---|---|---|---|---|---|---|---|
-| bert-base-cased | 0.48319 | 0.48319 | 0.48319 | 0.48459 | 0.46527 | 0.47156 | 0.48319 | 
-| bert-base-cased+KT-Emb | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 
-| bert-base-cased+KG-Emb | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 
-| bert-base-cased+KT-Attn | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 
+| Model | micro_P | micro_R | micro_F1 | macro_P | macro_R | macro_F1 | Acc | file |
+|---|---|---|---|---|---|---|---|---|
+| bert-base-cased | 0.48319 | 0.48319 | 0.48319 | 0.48459 | 0.46527 | 0.47156 | 0.48319 | sst5_bert_base_cased.py | 
+| bert-base-cased+KT-Emb | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 6 | 
+| bert-base-cased+KG-Emb | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 6 | 
+| bert-base-cased+KT-Attn | 8 | 9 | 4 | 5 | 6 | 5 | 6 | 6 | 
 
 ## 2.Sentence Pair
diff --git a/experiments/text_classification/SST-2/sst2_bert_base_cased.py b/experiments/text_classification/SST-2/sst2_bert_base_cased.py
@@ -19,7 +19,7 @@
 
 plm = PlmAutoModel(pretrained_model_name="bert-base-cased")
 model = BaseTextClassificationModel(plm=plm, vocab=vocab)
-metric = BaseClassificationMetric(mode="binary")  # SST_2
+metric = BaseClassificationMetric(mode="binary")
 loss = nn.CrossEntropyLoss()
 optimizer = optim.Adam(model.parameters(), lr=0.00001)
 

diff --git a/test/test_base_text_classification.py b/test/test_base_text_classification.py
@@ -4,26 +4,22 @@
 
 device, output_path = init_cogktr(
     device_id=7,
-    # output_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_2/experimental_result",  # SST_2
-    output_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_5/experimental_result",  # SST_5
+    output_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_2/experimental_result",
     folder_tag="simple_test",
 )
 
-# reader = Sst2Reader(raw_data_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_2/raw_data")  # SST_2
-reader = Sst5Reader(raw_data_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_5/raw_data")  # SST_5
+reader = Sst2Reader(raw_data_path="/data/mentianyi/code/CogKTR/datapath/text_classification/SST_2/raw_data")
 train_data, dev_data, test_data = reader.read_all()
 vocab = reader.read_vocab()
 
-# processor = Sst2Processor(plm="bert-base-cased", max_token_len=128, vocab=vocab)  # SST_2
-processor = Sst5Processor(plm="bert-base-cased", max_token_len=128, vocab=vocab)  # SST_5
+processor = Sst2Processor(plm="bert-base-cased", max_token_len=128, vocab=vocab)
 train_dataset = processor.process_train(train_data)
 dev_dataset = processor.process_dev(dev_data)
 test_dataset = processor.process_test(test_data)
 
 plm = PlmAutoModel(pretrained_model_name="bert-base-cased")
 model = BaseTextClassificationModel(plm=plm, vocab=vocab)
-# metric = BaseClassificationMetric(mode="binary")  # SST_2
-metric = BaseClassificationMetric(mode="multi")  # SST_5
+metric = BaseClassificationMetric(mode="binary")
 loss = nn.CrossEntropyLoss()
 optimizer = optim.Adam(model.parameters(), lr=0.00001)