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: 0.9022777369581191
+    - name: Recall
+      type: recall
+      value: 0.9191616766467066
+    - name: F1
+      type: f1
+      value: 0.9106414534668149
+    - name: Accuracy
+      type: accuracy
+      value: 0.9202037351443124
+---
+
+<!-- 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: 0.3848
+- Precision: 0.9023
+- Recall: 0.9192
+- F1: 0.9106
+- Accuracy: 0.9202
+
+## 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        | 6.25  | 250  | 0.9576          | 0.7878    | 0.8196 | 0.8034 | 0.8166   |
+| 1.3167        | 12.5  | 500  | 0.5210          | 0.8536    | 0.8772 | 0.8653 | 0.8846   |
+| 1.3167        | 18.75 | 750  | 0.4077          | 0.8798    | 0.9042 | 0.8918 | 0.9113   |
+| 0.2603        | 25.0  | 1000 | 0.3943          | 0.8902    | 0.9102 | 0.9001 | 0.9147   |
+| 0.2603        | 31.25 | 1250 | 0.3691          | 0.8980    | 0.9162 | 0.9070 | 0.9194   |
+| 0.1009        | 37.5  | 1500 | 0.3496          | 0.9130    | 0.9274 | 0.9202 | 0.9266   |
+| 0.1009        | 43.75 | 1750 | 0.3700          | 0.9078    | 0.9214 | 0.9146 | 0.9266   |
+| 0.056         | 50.0  | 2000 | 0.3724          | 0.9065    | 0.9214 | 0.9139 | 0.9215   |
+| 0.056         | 56.25 | 2250 | 0.3773          | 0.9051    | 0.9207 | 0.9128 | 0.9202   |
+| 0.0413        | 62.5  | 2500 | 0.3848          | 0.9023    | 0.9192 | 0.9106 | 0.9202   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1