update model card README.md

README.md

@@ -0,0 +1,105 @@
+---
+tags:
+- generated_from_trainer
+datasets:
+- sroie
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-invoice
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: sroie
+      type: sroie
+      args: sroie
+    metrics:
+    - name: Precision
+      type: precision
+      value: 1.0
+    - name: Recall
+      type: recall
+      value: 1.0
+    - name: F1
+      type: f1
+      value: 1.0
+    - name: Accuracy
+      type: accuracy
+      value: 1.0
+---
+
+<!-- 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
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0018
+- Precision: 1.0
+- Recall: 1.0
+- F1: 1.0
+- Accuracy: 1.0
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 2.0   | 100  | 0.0967          | 0.958     | 0.9716 | 0.9648 | 0.9956   |
+| No log        | 4.0   | 200  | 0.0222          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| No log        | 6.0   | 300  | 0.0171          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| No log        | 8.0   | 400  | 0.0136          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| 0.1307        | 10.0  | 500  | 0.0117          | 0.964     | 0.9777 | 0.9708 | 0.9962   |
+| 0.1307        | 12.0  | 600  | 0.0099          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| 0.1307        | 14.0  | 700  | 0.0094          | 0.972     | 0.9858 | 0.9789 | 0.9971   |
+| 0.1307        | 16.0  | 800  | 0.0071          | 0.9918    | 0.9838 | 0.9878 | 0.9983   |
+| 0.1307        | 18.0  | 900  | 0.0026          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
+| 0.0089        | 20.0  | 1000 | 0.0018          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0089        | 22.0  | 1100 | 0.0016          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0089        | 24.0  | 1200 | 0.0015          | 1.0       | 0.9980 | 0.9990 | 0.9998   |
+| 0.0089        | 26.0  | 1300 | 0.0015          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
+| 0.0089        | 28.0  | 1400 | 0.0014          | 0.9980    | 0.9980 | 0.9980 | 0.9998   |
+| 0.0025        | 30.0  | 1500 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0025        | 32.0  | 1600 | 0.0012          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0025        | 34.0  | 1700 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0025        | 36.0  | 1800 | 0.0010          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0025        | 38.0  | 1900 | 0.0010          | 1.0       | 1.0    | 1.0    | 1.0      |
+| 0.0019        | 40.0  | 2000 | 0.0011          | 1.0       | 1.0    | 1.0    | 1.0      |
+
+
+### Framework versions
+
+- Transformers 4.21.0.dev0
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.2
+- Tokenizers 0.12.1