update model card README.md

README.md

@@ -3,7 +3,7 @@ license: cc-by-nc-sa-4.0
 tags:
 - generated_from_trainer
 datasets:
-- cord-layoutlmv3
+- cord-layoutlmv
 metrics:
 - precision
 - recall
@@ -16,24 +16,24 @@ model-index:
       name: Token Classification
       type: token-classification
     dataset:
-      name: cord-layoutlmv3
-      type: cord-layoutlmv3
-      config: cord
+      name: cord-layoutlmv
+      type: cord-layoutlmv
+      config: default
       split: train
-      args: cord
+      args: default
     metrics:
     - name: Precision
       type: precision
-      value: 0.9304733727810651
+      value: 0.9476439790575916
     - name: Recall
       type: recall
-      value: 0.9416167664670658
+      value: 0.9679144385026738
     - name: F1
       type: f1
-      value: 0.9360119047619048
+      value: 0.9576719576719576
     - name: Accuracy
       type: accuracy
-      value: 0.9435483870967742
+      value: 0.9905873493975904
 ---
 
 <!-- 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. -->
 
 # layoutlmv3-finetuned-cord_100
 
-This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2920
-- Precision: 0.9305
-- Recall: 0.9416
-- F1: 0.9360
-- Accuracy: 0.9435
+- Loss: 0.0693
+- Precision: 0.9476
+- Recall: 0.9679
+- F1: 0.9577
+- Accuracy: 0.9906
 
 ## Model description
 
@@ -72,22 +72,14 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- training_steps: 2500
+- training_steps: 500
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 4.17  | 250  | 1.1252          | 0.6774    | 0.7530 | 0.7132 | 0.7721   |
-| 1.4792        | 8.33  | 500  | 0.5864          | 0.8395    | 0.8653 | 0.8522 | 0.8667   |
-| 1.4792        | 12.5  | 750  | 0.4279          | 0.8666    | 0.8997 | 0.8828 | 0.9032   |
-| 0.3807        | 16.67 | 1000 | 0.3512          | 0.9067    | 0.9237 | 0.9151 | 0.9317   |
-| 0.3807        | 20.83 | 1250 | 0.3030          | 0.9167    | 0.9311 | 0.9239 | 0.9368   |
-| 0.1615        | 25.0  | 1500 | 0.3022          | 0.9239    | 0.9356 | 0.9297 | 0.9385   |
-| 0.1615        | 29.17 | 1750 | 0.2931          | 0.9198    | 0.9356 | 0.9276 | 0.9385   |
-| 0.0879        | 33.33 | 2000 | 0.2968          | 0.9276    | 0.9401 | 0.9338 | 0.9427   |
-| 0.0879        | 37.5  | 2250 | 0.2853          | 0.9298    | 0.9424 | 0.9361 | 0.9448   |
-| 0.0641        | 41.67 | 2500 | 0.2920          | 0.9305    | 0.9416 | 0.9360 | 0.9435   |
+| No log        | 27.78 | 250  | 0.0689          | 0.9175    | 0.9519 | 0.9344 | 0.9898   |
+| 0.0141        | 55.56 | 500  | 0.0693          | 0.9476    | 0.9679 | 0.9577 | 0.9906   |
 
 
 ### Framework versions