End of training

README.md

@@ -4,51 +4,51 @@ base_model: microsoft/layoutlmv3-base
 tags:
 - generated_from_trainer
 datasets:
-- generated
+- funsd
 metrics:
 - precision
 - recall
 - f1
 - accuracy
 model-index:
-- name: layoutlmv3-finetuned-invoice
+- name: layoutlmv3-finetuned-funsd
   results:
   - task:
       name: Token Classification
       type: token-classification
     dataset:
-      name: generated
-      type: generated
-      config: sroie
+      name: funsd
+      type: funsd
+      config: funsd
       split: test
-      args: sroie
+      args: funsd
     metrics:
     - name: Precision
       type: precision
-      value: 0.010438413361169102
+      value: 0.7467652495378928
     - name: Recall
       type: recall
-      value: 0.02028397565922921
+      value: 0.8027819175360159
     - name: F1
       type: f1
-      value: 0.013783597518952447
+      value: 0.7737610725401005
     - name: Accuracy
       type: accuracy
-      value: 0.6785338108278913
+      value: 0.8188517770117675
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# layoutlmv3-finetuned-invoice
+# layoutlmv3-finetuned-funsd
 
-This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.1320
-- Precision: 0.0104
-- Recall: 0.0203
-- F1: 0.0138
-- Accuracy: 0.6785
+- Loss: 0.5984
+- Precision: 0.7468
+- Recall: 0.8028
+- F1: 0.7738
+- Accuracy: 0.8189
 
 ## Model description
 
@@ -73,17 +73,22 @@ 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: 5
+- training_steps: 1000
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| No log        | 0.01  | 1    | 2.3858          | 0.0114    | 0.0649 | 0.0194 | 0.1904   |
-| No log        | 0.02  | 2    | 2.2795          | 0.0108    | 0.0527 | 0.0180 | 0.3240   |
-| No log        | 0.03  | 3    | 2.2072          | 0.0131    | 0.0446 | 0.0203 | 0.5155   |
-| No log        | 0.04  | 4    | 2.1575          | 0.0103    | 0.0243 | 0.0145 | 0.6345   |
-| No log        | 0.05  | 5    | 2.1320          | 0.0104    | 0.0203 | 0.0138 | 0.6785   |
+| No log        | 0.67  | 100  | 1.0197          | 0.5025    | 0.5981 | 0.5462 | 0.6622   |
+| No log        | 1.34  | 200  | 0.6833          | 0.6203    | 0.7238 | 0.6680 | 0.7608   |
+| No log        | 2.01  | 300  | 0.6237          | 0.6401    | 0.7794 | 0.7030 | 0.7846   |
+| No log        | 2.68  | 400  | 0.6028          | 0.6892    | 0.7392 | 0.7133 | 0.7771   |
+| 0.8343        | 3.36  | 500  | 0.5948          | 0.7175    | 0.7884 | 0.7512 | 0.7991   |
+| 0.8343        | 4.03  | 600  | 0.5953          | 0.7135    | 0.8028 | 0.7555 | 0.7961   |
+| 0.8343        | 4.7   | 700  | 0.5925          | 0.7354    | 0.7953 | 0.7642 | 0.8174   |
+| 0.8343        | 5.37  | 800  | 0.6055          | 0.7397    | 0.7933 | 0.7656 | 0.8134   |
+| 0.8343        | 6.04  | 900  | 0.5940          | 0.7535    | 0.8077 | 0.7797 | 0.8199   |
+| 0.3468        | 6.71  | 1000 | 0.5984          | 0.7468    | 0.8028 | 0.7738 | 0.8189   |
 
 
 ### Framework versions