update model card README.md

README.md

@@ -3,6 +3,8 @@ license: apache-2.0
 base_model: NlpHUST/electra-base-vn
 tags:
 - generated_from_trainer
+datasets:
+- hts98/UIT
 metrics:
 - precision
 - recall
@@ -10,7 +12,26 @@ metrics:
 - accuracy
 model-index:
 - name: Ner-vibert4news-base-cased
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: hts98/UIT
+      type: hts98/UIT
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6231584388730939
+    - name: Recall
+      type: recall
+      value: 0.6730876605248465
+    - name: F1
+      type: f1
+      value: 0.6471614548382768
+    - name: Accuracy
+      type: accuracy
+      value: 0.7937549199685122
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,13 +39,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Ner-vibert4news-base-cased
 
-This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on an unknown dataset.
+This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on the hts98/UIT dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.2552
-- Precision: 0.6164
-- Recall: 0.6728
-- F1: 0.6434
-- Accuracy: 0.7944
+- Loss: 2.2270
+- Precision: 0.6232
+- Recall: 0.6731
+- F1: 0.6472
+- Accuracy: 0.7938
 
 ## Model description