update model card README.md

README.md

@@ -2,8 +2,6 @@
 license: mit
 tags:
 - generated_from_trainer
-datasets:
-- conll2003
 metrics:
 - precision
 - recall
@@ -11,27 +9,7 @@ metrics:
 - accuracy
 model-index:
 - name: roberta-base-finetuned-ner
-  results:
-  - task:
-      name: Token Classification
-      type: token-classification
-    dataset:
-      name: conll2003
-      type: conll2003
-      args: conll2003
-    metrics:
-    - name: Precision
-      type: precision
-      value: 0.9510234601364613
-    - name: Recall
-      type: recall
-      value: 0.9609935776350585
-    - name: F1
-      type: f1
-      value: 0.9559825245454973
-    - name: Accuracy
-      type: accuracy
-      value: 0.989046055659538
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -39,13 +17,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # roberta-base-finetuned-ner
 
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0504
-- Precision: 0.9510
-- Recall: 0.9610
-- F1: 0.9560
-- Accuracy: 0.9890
+- Loss: 0.0738
+- Precision: 0.9232
+- Recall: 0.9437
+- F1: 0.9333
+- Accuracy: 0.9825
 
 ## Model description
 
@@ -76,9 +54,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.2225        | 1.0   | 878  | 0.0563          | 0.9313    | 0.9392 | 0.9352 | 0.9849   |
-| 0.0464        | 2.0   | 1756 | 0.0519          | 0.9491    | 0.9598 | 0.9544 | 0.9882   |
-| 0.0269        | 3.0   | 2634 | 0.0504          | 0.9510    | 0.9610 | 0.9560 | 0.9890   |
+| 0.1397        | 1.0   | 1368 | 0.0957          | 0.9141    | 0.9048 | 0.9094 | 0.9753   |
+| 0.0793        | 2.0   | 2736 | 0.0728          | 0.9274    | 0.9324 | 0.9299 | 0.9811   |
+| 0.0499        | 3.0   | 4104 | 0.0738          | 0.9232    | 0.9437 | 0.9333 | 0.9825   |
 
 
 ### Framework versions