update model card README.md

README.md

@@ -24,16 +24,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.4870775347912525
+      value: 0.5759878419452887
     - name: Recall
       type: recall
-      value: 0.22706209453197404
+      value: 0.35125115848007415
     - name: F1
       type: f1
-      value: 0.3097345132743363
+      value: 0.436384571099597
     - name: Accuracy
       type: accuracy
-      value: 0.9376255824889915
+      value: 0.9444206926036768
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -41,13 +41,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # token_classification_finetune
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
+This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2891
-- Precision: 0.4871
-- Recall: 0.2271
-- F1: 0.3097
-- Accuracy: 0.9376
+- Loss: 0.2489
+- Precision: 0.5760
+- Recall: 0.3513
+- F1: 0.4364
+- Accuracy: 0.9444
 
 ## Model description
 
@@ -78,8 +78,8 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 1.0   | 107  | 0.3080          | 0.3528    | 0.1010 | 0.1571 | 0.9317   |
-| No log        | 2.0   | 214  | 0.2891          | 0.4871    | 0.2271 | 0.3097 | 0.9376   |
+| No log        | 1.0   | 107  | 0.2573          | 0.6011    | 0.3003 | 0.4005 | 0.9409   |
+| No log        | 2.0   | 214  | 0.2489          | 0.5760    | 0.3513 | 0.4364 | 0.9444   |
 
 
 ### Framework versions