End of training

README.md

@@ -6,7 +6,10 @@ tags:
 datasets:
 - xtreme
 metrics:
+- precision
+- recall
 - f1
+- accuracy
 model-index:
 - name: roberta-base-NER
   results:
@@ -20,9 +23,18 @@ model-index:
       split: validation
       args: PAN-X.en
     metrics:
+    - name: Precision
+      type: precision
+      value: 0.7779299014238773
+    - name: Recall
+      type: recall
+      value: 0.8005071851225697
     - name: F1
       type: f1
-      value: 0.7749031543995573
+      value: 0.7890570754061935
+    - name: Accuracy
+      type: accuracy
+      value: 0.912818107767877
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3068
-- F1: 0.7749
+- Loss: 0.2988
+- Precision: 0.7779
+- Recall: 0.8005
+- F1: 0.7891
+- Accuracy: 0.9128
 
 ## Model description
 
@@ -58,14 +73,15 @@ 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: 2
+- num_epochs: 3
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:------:|
-| No log        | 1.0   | 417  | 0.3361          | 0.7477 |
-| 0.5196        | 2.0   | 834  | 0.3068          | 0.7749 |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 417  | 0.3344          | 0.7337    | 0.7732 | 0.7529 | 0.9019   |
+| 0.5039        | 2.0   | 834  | 0.2995          | 0.7588    | 0.7932 | 0.7756 | 0.9104   |
+| 0.2841        | 3.0   | 1251 | 0.2988          | 0.7779    | 0.8005 | 0.7891 | 0.9128   |
 
 
 ### Framework versions