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README.md

@@ -165,7 +165,7 @@ def train_classifier():
         l2_loss_weight=l2_loss_weight,
         use_lr_scheduler=True,
         train_run=train_run,
-        auto_find_batch_size=True,
+        auto_find_batch_size=False
     )
 
     new_model, history = trainer.train(dataset_name='philipp-zettl/GGU-xx', target_heads=['GGU'])
@@ -179,7 +179,8 @@ def train_classifier():
     }
 
     trainer.classifier.add_head('sentiment', 3)
-    new_model, history = trainer.train(dataset_name='philipp-zettl/sentiment', target_heads=['sentiment'], sample_key='text', num_epochs=5, lr=1e-4)
+    trainer.auto_find_batch_size = False
+    new_model, history = trainer.train(dataset_name='philipp-zettl/sentiment', target_heads=['sentiment'], sample_key='text', num_epochs=10, lr=1e-4)
     metrics = history['metrics']
     history['loss_plot'] = trainer._plot_history(**metrics)
     res = trainer.eval({'sentiment': sentiment_label_map}, sample_key='text')
@@ -244,18 +245,18 @@ For evaluation, we used the following metrics: accuracy, precision, recall, f1-s
 **GGU:**
 |    | index        |   precision |   recall |   f1-score |   support |
 |---:|:-------------|------------:|---------:|-----------:|----------:|
-|  0 | Greeting     |    0.885714 | 0.96875  |   0.925373 |        32 |
-|  1 | Gratitude    |    0.962963 | 0.742857 |   0.83871  |        35 |
-|  2 | Other        |    0.883721 | 1        |   0.938272 |        38 |
-|  3 | macro avg    |    0.910799 | 0.903869 |   0.900785 |       105 |
-|  4 | weighted avg |    0.910742 | 0.904762 |   0.901153 |       105 |
+|  0 | Greeting     |    0.904762 | 0.974359 |   0.938272 |        39 |
+|  1 | Gratitude    |    0.958333 | 0.851852 |   0.901961 |        27 |
+|  2 | Other        |    1        | 1        |   1        |        39 |
+|  3 | macro avg    |    0.954365 | 0.94207  |   0.946744 |       105 |
+|  4 | weighted avg |    0.953912 | 0.952381 |   0.951862 |       105 |
 
 **sentiment:**
 |    | index        |   precision |   recall |   f1-score |   support |
 |---:|:-------------|------------:|---------:|-----------:|----------:|
-|  0 | Positive     |    0.79948  | 0.844561 |   0.821403 |     12751 |
-|  1 | Negative     |    0.797005 | 0.835251 |   0.81568  |     14337 |
-|  2 | Neutral      |    0.786099 | 0.701784 |   0.741552 |     13118 |
-|  3 | macro avg    |    0.794195 | 0.793865 |   0.792878 |     40206 |
-|  4 | weighted avg |    0.794232 | 0.794658 |   0.793309 |     40206 |
+|  0 | Positive     |    0.783088 | 0.861878 |   0.820596 |     12851 |
+|  1 | Negative     |    0.802105 | 0.819524 |   0.810721 |     14229 |
+|  2 | Neutral      |    0.7874   | 0.6913   |   0.736227 |     13126 |
+|  3 | macro avg    |    0.790864 | 0.790901 |   0.789181 |     40206 |
+|  4 | weighted avg |    0.791226 | 0.7912   |   0.789557 |     40206 |
 

heads/GGU.pth

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 size 7552

heads/sentiment.pth

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multi-head-sequence-classification-model-model.pth

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