Update README.md

README.md

@@ -13,14 +13,16 @@ widget:
   example_title: "Star Wars"
 
 ---
-Multi-Label Text classification model used to decode if passages contain a misfortunate event, a cause for misfortune, and/or an action to mollify or prevent some misfortune.
+Text Multi-Label Sequence Classification model used to decode if passages contain a misfortunate event, a cause for misfortune, and/or an action to mollify or prevent some misfortune.
 8293 passages were used for Training and split into 5 folds (~6634 for the train set, ~1659 for the validation set over 5 folds). Although multiple parameters were tested, the current model uses:
+<br>Transformer: distilbert-base-uncased
+<br>Tokenizer: distilbert-base-uncased
 <br>learning rate: 2e-5 
 <br>weight decay: .01
 <br>Dropout: .1
 <br>Batch Size: 8
-<br>Epochs: 15 (epoch 13 used here)
-<br><br>The current F1 micro score of 2074 passages not used for training is .637. individual class f1 scores shown below. Note that at this moment, some labels have been excluded as they are not relevant for the final use of the model.
+<br>Epochs: 15
+<br><br>Using epoch 13, the current F1 micro score of 2074 passages not used for training is .637. individual class f1 scores are shown below. Note that at this moment, some labels have been excluded as they are not relevant for the final use of the model.
 <ul>
   <li>EVENT:  -
     <ul>