update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- cord
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cord
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cord
+      type: cord
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9190581309786607
+    - name: Recall
+      type: recall
+      value: 0.9348802395209581
+    - name: F1
+      type: f1
+      value: 0.9269016697588126
+    - name: Accuracy
+      type: accuracy
+      value: 0.9384550084889643
+---
+
+<!-- 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-cord
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3056
+- Precision: 0.9191
+- Recall: 0.9349
+- F1: 0.9269
+- Accuracy: 0.9385
+
+## 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        | 2.0   | 100  | 1.6054          | 0.52      | 0.6130 | 0.5627 | 0.6367   |
+| No log        | 4.0   | 200  | 0.9172          | 0.7923    | 0.8278 | 0.8097 | 0.8315   |
+| No log        | 6.0   | 300  | 0.6382          | 0.8367    | 0.8630 | 0.8497 | 0.8667   |
+| No log        | 8.0   | 400  | 0.4974          | 0.8648    | 0.8907 | 0.8776 | 0.8960   |
+| 1.1589        | 10.0  | 500  | 0.4124          | 0.8769    | 0.9064 | 0.8914 | 0.9164   |
+| 1.1589        | 12.0  | 600  | 0.3767          | 0.8961    | 0.9169 | 0.9064 | 0.9236   |
+| 1.1589        | 14.0  | 700  | 0.3388          | 0.9120    | 0.9304 | 0.9211 | 0.9338   |
+| 1.1589        | 16.0  | 800  | 0.3138          | 0.9198    | 0.9356 | 0.9276 | 0.9393   |
+| 1.1589        | 18.0  | 900  | 0.3073          | 0.9176    | 0.9334 | 0.9254 | 0.9376   |
+| 0.2992        | 20.0  | 1000 | 0.3056          | 0.9191    | 0.9349 | 0.9269 | 0.9385   |
+
+
+### Framework versions
+
+- Transformers 4.19.0.dev0
+- Pytorch 1.11.0+cu113
+- Datasets 2.0.0
+- Tokenizers 0.11.6