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: distilbert-base-uncased-finetuned-pos
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9109037731744458
+    - name: Recall
+      type: recall
+      value: 0.9143515710299648
+    - name: F1
+      type: f1
+      value: 0.9126244157605404
+    - name: Accuracy
+      type: accuracy
+      value: 0.9245555785025498
 ---
 
 <!-- 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. -->
 
 # distilbert-base-uncased-finetuned-pos
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2068
-- Precision: 0.0
-- Recall: 0.0
-- F1: 0.0
-- Accuracy: 0.9352
+- Loss: 0.3165
+- Precision: 0.9109
+- Recall: 0.9144
+- F1: 0.9126
+- Accuracy: 0.9246
 
 ## Model description
 
@@ -52,11 +74,11 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
-| No log        | 1.0   | 233  | 0.2078          | 0.0       | 0.0    | 0.0 | 0.9352   |
-| No log        | 2.0   | 466  | 0.2034          | 0.0       | 0.0    | 0.0 | 0.9352   |
-| 0.2079        | 3.0   | 699  | 0.2068          | 0.0       | 0.0    | 0.0 | 0.9352   |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.7941        | 1.0   | 878  | 0.3504          | 0.8995    | 0.9026 | 0.9011 | 0.9176   |
+| 0.2533        | 2.0   | 1756 | 0.3216          | 0.9091    | 0.9104 | 0.9098 | 0.9233   |
+| 0.2047        | 3.0   | 2634 | 0.3165          | 0.9109    | 0.9144 | 0.9126 | 0.9246   |
 
 
 ### Framework versions