End of training

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.7423200924863519
+      value: 0.7917968510685142
     - name: Recall
       type: recall
-      value: 0.6864113851587973
+      value: 0.7643218821508218
     - name: F1
       type: f1
-      value: 0.6947659539044735
+      value: 0.7714894659273394
     - name: Accuracy
       type: accuracy
-      value: 0.8409785932721713
+      value: 0.8942255801403131
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [Goader/liberta-large](https://huggingface.co/Goader/liberta-large) on the universal_dependencies dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3890
-- Precision: 0.7423
-- Recall: 0.6864
-- F1: 0.6948
-- Accuracy: 0.8410
+- Loss: 0.2970
+- Precision: 0.7918
+- Recall: 0.7643
+- F1: 0.7715
+- Accuracy: 0.8942
 
 ## Model description
 
@@ -73,7 +73,7 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 10
+- num_epochs: 20
 
 ### Training results
 

model.safetensors

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training_args.bin

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