update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8614552827213336
+      value: 0.8734693877551021
     - name: Recall
       type: recall
-      value: 0.8786764705882353
+      value: 0.8851102941176471
     - name: F1
       type: f1
-      value: 0.8699806620407236
+      value: 0.8792513124857338
     - name: Accuracy
       type: accuracy
-      value: 0.9785031604018444
+      value: 0.979541360041528
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0944
-- Precision: 0.8615
-- Recall: 0.8787
-- F1: 0.8700
-- Accuracy: 0.9785
+- Loss: 0.0911
+- Precision: 0.8735
+- Recall: 0.8851
+- F1: 0.8793
+- Accuracy: 0.9795
 
 ## Model description
 
@@ -78,9 +78,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.104         | 1.0   | 521  | 0.0823          | 0.8616    | 0.8814 | 0.8714 | 0.9789   |
-| 0.034         | 2.0   | 1042 | 0.0855          | 0.8601    | 0.8773 | 0.8686 | 0.9777   |
-| 0.0197        | 3.0   | 1563 | 0.0944          | 0.8615    | 0.8787 | 0.8700 | 0.9785   |
+| 0.0981        | 1.0   | 521  | 0.0849          | 0.8612    | 0.8814 | 0.8712 | 0.9789   |
+| 0.0327        | 2.0   | 1042 | 0.0833          | 0.8634    | 0.8814 | 0.8723 | 0.9796   |
+| 0.0193        | 3.0   | 1563 | 0.0911          | 0.8735    | 0.8851 | 0.8793 | 0.9795   |
 
 
 ### Framework versions