Training complete

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.957203615098352
+      value: 0.969802244788883
     - name: Recall
       type: recall
-      value: 0.9714054491502563
+      value: 0.9789587267332075
     - name: F1
       type: f1
-      value: 0.964252242602758
+      value: 0.9743589743589745
     - name: Accuracy
       type: accuracy
-      value: 0.9885975250441956
+      value: 0.9894519740718916
 ---
 
 <!-- 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 [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the wikiann dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0523
-- Precision: 0.9572
-- Recall: 0.9714
-- F1: 0.9643
-- Accuracy: 0.9886
+- Loss: 0.0686
+- Precision: 0.9698
+- Recall: 0.9790
+- F1: 0.9744
+- Accuracy: 0.9895
 
 ## Model description
 
@@ -67,12 +67,12 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 6
+- learning_rate: 2e-05
+- train_batch_size: 4
 - eval_batch_size: 8
 - seed: 42
 - gradient_accumulation_steps: 4
-- total_train_batch_size: 24
+- total_train_batch_size: 16
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 3
@@ -81,9 +81,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 285  | 0.0795          | 0.9222    | 0.9342 | 0.9282 | 0.9763   |
-| 0.112         | 2.0   | 570  | 0.0613          | 0.9295    | 0.9560 | 0.9426 | 0.9844   |
-| 0.112         | 3.0   | 855  | 0.0523          | 0.9572    | 0.9714 | 0.9643 | 0.9886   |
+| No log        | 1.0   | 427  | 0.0717          | 0.9367    | 0.9701 | 0.9531 | 0.9862   |
+| 0.0221        | 2.0   | 855  | 0.0715          | 0.9560    | 0.9733 | 0.9646 | 0.9880   |
+| 0.0113        | 3.0   | 1281 | 0.0686          | 0.9698    | 0.9790 | 0.9744 | 0.9895   |
 
 
 ### Framework versions