Update README.md

README.md

@@ -86,12 +86,13 @@ Test|2414|5577|179|2445|1097|183|2838|272|374|356
 This model was trained using a NVIDIA RTX A6000 GPU with the following hyperparameters:
 
 - learning rate: 2e-05
-- train batch size: 16
+- train batch size: 24
 - epochs: 10
 - optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
 - scheduler: linear scheduler with num_warmup_steps=round(len(train_dataloader)/5) and num_training_steps=len(train_dataloader)*epochs
 - maximum length of data sequence: 512
 - patience: 2 epochs
+- classifier dropout: 0.3
 
 In the preprocessing stage, the input texts were split into chunks with a maximum length of 300 tokens,
 in order to avoid the tokenized chunks exceeding the maximum length of 512. Tokenization was performed
@@ -106,15 +107,15 @@ Evaluation results using the test dataset are listed below:
 
 ||Precision|Recall|F1-score
 -|-|-|-
-PERSON|0.91|0.91|0.91
-ORG|0.88|0.89|0.89
-LOC|0.87|0.89|0.88
-GPE|0.93|0.94|0.93
-PRODUCT|0.77|0.82|0.80
-EVENT|0.66|0.71|0.69
-DATE|0.89|0.92|0.91
-JON|0.78|0.83|0.80
-FIBC|0.88|0.94|0.69
+PERSON|0.90|0.91|0.90
+ORG|0.84|0.87|0.86
+LOC|0.84|0.86|0.85
+GPE|0.91|0.91|0.91
+PRODUCT|0.73|0.77|0.75
+EVENT|0.69|0.73|0.71
+DATE|0.90|0.92|0.91
+JON|0.83|0.95|0.89
+FIBC|0.95|0.99|0.97
 NORP|0.91|0.95|0.93
 
 The metrics were calculated using the [seqeval](https://github.com/chakki-works/seqeval) library.