update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- invoices
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-invoice
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: invoices
+      type: invoices
+      config: sroie
+      split: train
+      args: sroie
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.975
+    - name: Recall
+      type: recall
+      value: 0.975
+    - name: F1
+      type: f1
+      value: 0.975
+    - name: Accuracy
+      type: accuracy
+      value: 0.975
+---
+
+<!-- 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 invoices dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2299
+- Precision: 0.975
+- Recall: 0.975
+- F1: 0.975
+- Accuracy: 0.975
+
+## 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        | 14.29  | 100  | 0.1616          | 0.975     | 0.975  | 0.975 | 0.975    |
+| No log        | 28.57  | 200  | 0.1909          | 0.975     | 0.975  | 0.975 | 0.975    |
+| No log        | 42.86  | 300  | 0.2046          | 0.975     | 0.975  | 0.975 | 0.975    |
+| No log        | 57.14  | 400  | 0.2134          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.1239        | 71.43  | 500  | 0.2299          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.1239        | 85.71  | 600  | 0.2309          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.1239        | 100.0  | 700  | 0.2342          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.1239        | 114.29 | 800  | 0.2407          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.1239        | 128.57 | 900  | 0.2428          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0007        | 142.86 | 1000 | 0.2449          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0007        | 157.14 | 1100 | 0.2465          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0007        | 171.43 | 1200 | 0.2488          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0007        | 185.71 | 1300 | 0.2515          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0007        | 200.0  | 1400 | 0.2525          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0004        | 214.29 | 1500 | 0.2540          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0004        | 228.57 | 1600 | 0.2557          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0004        | 242.86 | 1700 | 0.2564          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0004        | 257.14 | 1800 | 0.2570          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0004        | 271.43 | 1900 | 0.2573          | 0.975     | 0.975  | 0.975 | 0.975    |
+| 0.0003        | 285.71 | 2000 | 0.2574          | 0.975     | 0.975  | 0.975 | 0.975    |
+
+
+### Framework versions
+
+- Transformers 4.23.0.dev0
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1