update model card README.md

README.md

@@ -1,7 +1,8 @@
 ---
-license: apache-2.0
 tags:
 - generated_from_trainer
+datasets:
+- conll2003
 metrics:
 - precision
 - recall
@@ -9,7 +10,29 @@ metrics:
 - accuracy
 model-index:
 - name: bert-finetuned-ner-ime
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: train
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.998195331607817
+    - name: Recall
+      type: recall
+      value: 0.9982190349544073
+    - name: F1
+      type: f1
+      value: 0.9982071831403979
+    - name: Accuracy
+      type: accuracy
+      value: 0.9979751132733664
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -17,13 +40,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # bert-finetuned-ner-ime
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
+This model is a fine-tuned version of [snunlp/KR-BERT-char16424](https://huggingface.co/snunlp/KR-BERT-char16424) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.4592
-- Precision: 0.6456
-- Recall: 0.3813
-- F1: 0.4794
-- Accuracy: 0.6108
+- Loss: 0.0076
+- Precision: 0.9982
+- Recall: 0.9982
+- F1: 0.9982
+- Accuracy: 0.9980
 
 ## Model description
 
@@ -54,9 +77,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 221  | 2.6044          | 0.6263    | 0.3774 | 0.4710 | 0.6081   |
-| No log        | 2.0   | 442  | 2.5040          | 0.6286    | 0.3848 | 0.4774 | 0.6100   |
-| 2.7612        | 3.0   | 663  | 2.4592          | 0.6456    | 0.3813 | 0.4794 | 0.6108   |
+| 0.0378        | 1.0   | 1756 | 0.0290          | 0.9934    | 0.9939 | 0.9936 | 0.9920   |
+| 0.0214        | 2.0   | 3512 | 0.0138          | 0.9969    | 0.9970 | 0.9970 | 0.9965   |
+| 0.0151        | 3.0   | 5268 | 0.0076          | 0.9982    | 0.9982 | 0.9982 | 0.9980   |
 
 
 ### Framework versions