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: 0.917960088691796
+    - name: Recall
+      type: recall
+      value: 0.9296407185628742
+    - name: F1
+      type: f1
+      value: 0.9237634808478989
+    - name: Accuracy
+      type: accuracy
+      value: 0.9303904923599321
+---
+
+<!-- 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: 0.2854
+- Precision: 0.9180
+- Recall: 0.9296
+- F1: 0.9238
+- Accuracy: 0.9304
+
+## 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: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 0.62  | 250  | 1.2967          | 0.6175    | 0.7021 | 0.6571 | 0.7296   |
+| 1.6872        | 1.25  | 500  | 0.7576          | 0.8140    | 0.8383 | 0.8260 | 0.8383   |
+| 1.6872        | 1.88  | 750  | 0.5695          | 0.8301    | 0.8518 | 0.8408 | 0.8544   |
+| 0.6109        | 2.5   | 1000 | 0.4778          | 0.8564    | 0.875  | 0.8656 | 0.8812   |
+| 0.6109        | 3.12  | 1250 | 0.3825          | 0.8694    | 0.8922 | 0.8807 | 0.8986   |
+| 0.3905        | 3.75  | 1500 | 0.3546          | 0.8831    | 0.9049 | 0.8939 | 0.9143   |
+| 0.3905        | 4.38  | 1750 | 0.3153          | 0.8998    | 0.9207 | 0.9101 | 0.9223   |
+| 0.275         | 5.0   | 2000 | 0.3065          | 0.8926    | 0.9147 | 0.9035 | 0.9202   |
+| 0.275         | 5.62  | 2250 | 0.2872          | 0.9131    | 0.9281 | 0.9206 | 0.9291   |
+| 0.2275        | 6.25  | 2500 | 0.2854          | 0.9180    | 0.9296 | 0.9238 | 0.9304   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.1+cpu
+- Datasets 2.8.0
+- Tokenizers 0.13.2