update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- cord-layoutlmv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cord_100
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cord-layoutlmv3
+      type: cord-layoutlmv3
+      config: cord
+      split: train
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8719646799116998
+    - name: Recall
+      type: recall
+      value: 0.8869760479041916
+    - name: F1
+      type: f1
+      value: 0.8794063079777364
+    - name: Accuracy
+      type: accuracy
+      value: 0.8790322580645161
+---
+
+<!-- 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_100
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7215
+- Precision: 0.8720
+- Recall: 0.8870
+- F1: 0.8794
+- Accuracy: 0.8790
+
+## 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: 5
+- eval_batch_size: 5
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 12.5  | 250  | 1.0892          | 0.7345    | 0.7867 | 0.7597 | 0.7806   |
+| 1.3039        | 25.0  | 500  | 0.7150          | 0.8054    | 0.8428 | 0.8237 | 0.8281   |
+| 1.3039        | 37.5  | 750  | 0.6320          | 0.8335    | 0.8615 | 0.8473 | 0.8540   |
+| 0.2171        | 50.0  | 1000 | 0.6427          | 0.8651    | 0.8832 | 0.8741 | 0.8722   |
+| 0.2171        | 62.5  | 1250 | 0.6640          | 0.8672    | 0.8847 | 0.8759 | 0.8765   |
+| 0.0654        | 75.0  | 1500 | 0.6758          | 0.8650    | 0.8825 | 0.8737 | 0.8731   |
+| 0.0654        | 87.5  | 1750 | 0.7028          | 0.8684    | 0.8840 | 0.8761 | 0.8765   |
+| 0.0338        | 100.0 | 2000 | 0.7252          | 0.8710    | 0.8847 | 0.8778 | 0.8769   |
+| 0.0338        | 112.5 | 2250 | 0.7227          | 0.8710    | 0.8847 | 0.8778 | 0.8778   |
+| 0.0257        | 125.0 | 2500 | 0.7215          | 0.8720    | 0.8870 | 0.8794 | 0.8790   |
+
+
+### Framework versions
+
+- Transformers 4.21.2
+- Pytorch 1.12.1+cu113
+- Datasets 2.4.0
+- Tokenizers 0.12.1