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: test
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9430473372781065
+    - name: Recall
+      type: recall
+      value: 0.9543413173652695
+    - name: F1
+      type: f1
+      value: 0.9486607142857143
+    - name: Accuracy
+      type: accuracy
+      value: 0.9579796264855688
+---
+
+<!-- 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: 0.2188
+- Precision: 0.9430
+- Recall: 0.9543
+- F1: 0.9487
+- Accuracy: 0.9580
+
+## 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        | 1.56  | 250  | 1.0024          | 0.7392    | 0.7957 | 0.7664 | 0.8060   |
+| 1.3949        | 3.12  | 500  | 0.5684          | 0.8330    | 0.8660 | 0.8492 | 0.8727   |
+| 1.3949        | 4.69  | 750  | 0.3929          | 0.8931    | 0.9072 | 0.9001 | 0.9160   |
+| 0.3964        | 6.25  | 1000 | 0.3312          | 0.9236    | 0.9326 | 0.9281 | 0.9321   |
+| 0.3964        | 7.81  | 1250 | 0.2754          | 0.9275    | 0.9386 | 0.9330 | 0.9410   |
+| 0.216         | 9.38  | 1500 | 0.2447          | 0.9328    | 0.9454 | 0.9390 | 0.9478   |
+| 0.216         | 10.94 | 1750 | 0.2467          | 0.9363    | 0.9461 | 0.9412 | 0.9478   |
+| 0.1534        | 12.5  | 2000 | 0.2300          | 0.9436    | 0.9521 | 0.9478 | 0.9537   |
+| 0.1534        | 14.06 | 2250 | 0.2155          | 0.9459    | 0.9558 | 0.9509 | 0.9597   |
+| 0.119         | 15.62 | 2500 | 0.2188          | 0.9430    | 0.9543 | 0.9487 | 0.9580   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3