Update README.md

README.md

@@ -21,16 +21,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.9026198714780029
+      value: 0.918177	
     - name: Recall
       type: recall
-      value: 0.913
+      value: 0.930949
     - name: F1
       type: f1
-      value: 0.9077802634849614
+      value: 0.924519
     - name: Accuracy
       type: accuracy
-      value: 0.8330271015158475
+      value: 0.859979
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -40,86 +40,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the nielsr/funsd-layoutlmv3 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.1164
-- Precision: 0.9026
-- Recall: 0.913
-- F1: 0.9078
-- Accuracy: 0.8330
+- Loss: 1.038318	
+- Precision: 0.918177	
+- Recall: 0.930949	
+- F1: 0.924519
+- Accuracy: 0.859979
 
-## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- training_steps: 1000
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 10.0  | 100  | 0.5238          | 0.8366    | 0.886  | 0.8606 | 0.8410   |
-| No log        | 20.0  | 200  | 0.6930          | 0.8751    | 0.8965 | 0.8857 | 0.8322   |
-| No log        | 30.0  | 300  | 0.7784          | 0.8902    | 0.908  | 0.8990 | 0.8414   |
-| No log        | 40.0  | 400  | 0.9056          | 0.8916    | 0.905  | 0.8983 | 0.8364   |
-| 0.2429        | 50.0  | 500  | 1.0016          | 0.8954    | 0.9075 | 0.9014 | 0.8298   |
-| 0.2429        | 60.0  | 600  | 1.0097          | 0.8899    | 0.897  | 0.8934 | 0.8294   |
-| 0.2429        | 70.0  | 700  | 1.0722          | 0.9035    | 0.9085 | 0.9060 | 0.8315   |
-| 0.2429        | 80.0  | 800  | 1.0884          | 0.8905    | 0.9105 | 0.9004 | 0.8269   |
-| 0.2429        | 90.0  | 900  | 1.1292          | 0.8938    | 0.909  | 0.9013 | 0.8279   |
-| 0.0098        | 100.0 | 1000 | 1.1164          | 0.9026    | 0.913  | 0.9078 | 0.8330   |
-| No log        | 10.0  | 100  | 0.5238          | 0.8366    | 0.886  | 0.8606 | 0.8410   |
-| No log        | 20.0  | 200  | 0.6930          | 0.8751    | 0.8965 | 0.8857 | 0.8322   |
-| No log        | 30.0  | 300  | 0.7784          | 0.8902    | 0.908  | 0.8990 | 0.8414   |
-| No log        | 40.0  | 400  | 0.9056          | 0.8916    | 0.905  | 0.8983 | 0.8364   |
-| 0.2429        | 50.0  | 500  | 1.0016          | 0.8954    | 0.9075 | 0.9014 | 0.8298   |
-| 0.2429        | 60.0  | 600  | 1.0097          | 0.8899    | 0.897  | 0.8934 | 0.8294   |
-| 0.2429        | 70.0  | 700  | 1.0722          | 0.9035    | 0.9085 | 0.9060 | 0.8315   |
-| 0.2429        | 80.0  | 800  | 1.0884          | 0.8905    | 0.9105 | 0.9004 | 0.8269   |
-| 0.2429        | 90.0  | 900  | 1.1292          | 0.8938    | 0.909  | 0.9013 | 0.8279   |
-| 0.0098        | 100.0 | 1000 | 1.1164          | 0.9026    | 0.913  | 0.9078 | 0.8330   |
-
-
- [4000/4000 20:34, Epoch 53/54]
-Step	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
-250	No log	0.435449	0.854588	0.902136	0.877719	0.835968
-500	0.505800	0.611310	0.869822	0.876304	0.873051	0.839177
-750	0.505800	0.635022	0.879886	0.917039	0.898078	0.853085
-1000	0.097000	0.765935	0.900818	0.929459	0.914914	0.860097
-1250	0.097000	0.887739	0.885533	0.903130	0.894245	0.842625
-1500	0.029900	0.948754	0.898018	0.923000	0.910338	0.843575
-1750	0.029900	1.102811	0.900433	0.929955	0.914956	0.840128
-2000	0.009700	1.039040	0.901415	0.917536	0.909404	0.852728
-2250	0.009700	1.044235	0.904716	0.924491	0.914496	0.849519
-2500	0.002500	1.013194	0.913086	0.918530	0.915800	0.849637
-2750	0.002500	1.017520	0.908605	0.928465	0.918428	0.854986
-3000	0.000900	1.029559	0.914216	0.926478	0.920306	0.859384
-3250	0.000900	1.038318	0.918177	0.930949	0.924519	0.859979
-3500	0.000800	1.045578	0.914216	0.926478	0.920306	0.858552
-3750	0.000800	1.040568	0.913894	0.927968	0.920877	0.858433
-4000	0.000700	1.041146	0.913894	0.927968	0.920877	0.8585528552
-
-
-### Framework versions
-
-- Transformers 4.19.0.dev0
-- Pytorch 1.11.0+cu113
-- Datasets 2.0.0
-- Tokenizers 0.11.6