update model card README.md

README.md

@@ -2,6 +2,8 @@
 license: apache-2.0
 tags:
 - generated_from_trainer
+datasets:
+- conll2003
 metrics:
 - precision
 - recall
@@ -9,7 +11,27 @@ metrics:
 - accuracy
 model-index:
 - name: albert-base-v2-finetuned-ner
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9301181102362205
+    - name: Recall
+      type: recall
+      value: 0.9376033513394334
+    - name: F1
+      type: f1
+      value: 0.9338457315399397
+    - name: Accuracy
+      type: accuracy
+      value: 0.9851613086447802
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -17,13 +39,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # albert-base-v2-finetuned-ner
 
-This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
+This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.1105
-- Precision: 0.9005
-- Recall: 0.9134
-- F1: 0.9069
-- Accuracy: 0.9777
+- Loss: 0.0700
+- Precision: 0.9301
+- Recall: 0.9376
+- F1: 0.9338
+- Accuracy: 0.9852
 
 ## Model description
 
@@ -54,9 +76,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1227        | 1.0   | 2245 | 0.1038          | 0.8918    | 0.8839 | 0.8879 | 0.9730   |
-| 0.081         | 2.0   | 4490 | 0.1006          | 0.8889    | 0.9099 | 0.8993 | 0.9761   |
-| 0.0409        | 3.0   | 6735 | 0.1105          | 0.9005    | 0.9134 | 0.9069 | 0.9777   |
+| 0.096         | 1.0   | 1756 | 0.0752          | 0.9163    | 0.9201 | 0.9182 | 0.9811   |
+| 0.0481        | 2.0   | 3512 | 0.0761          | 0.9169    | 0.9293 | 0.9231 | 0.9830   |
+| 0.0251        | 3.0   | 5268 | 0.0700          | 0.9301    | 0.9376 | 0.9338 | 0.9852   |
 
 
 ### Framework versions