update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+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: 0.9296817172464841
+    - name: Recall
+      type: recall
+      value: 0.9401197604790419
+    - name: F1
+      type: f1
+      value: 0.9348716040193524
+    - name: Accuracy
+      type: accuracy
+      value: 0.9435483870967742
+---
+
+<!-- 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: 0.2908
+- Precision: 0.9297
+- Recall: 0.9401
+- F1: 0.9349
+- Accuracy: 0.9435
+
+## 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        | 4.17  | 250  | 1.0995          | 0.6869    | 0.7635 | 0.7231 | 0.7789   |
+| 1.4568        | 8.33  | 500  | 0.5676          | 0.8382    | 0.8765 | 0.8569 | 0.8773   |
+| 1.4568        | 12.5  | 750  | 0.4044          | 0.8920    | 0.9147 | 0.9032 | 0.9202   |
+| 0.3562        | 16.67 | 1000 | 0.3518          | 0.9086    | 0.9229 | 0.9157 | 0.9270   |
+| 0.3562        | 20.83 | 1250 | 0.3060          | 0.9245    | 0.9349 | 0.9297 | 0.9372   |
+| 0.1509        | 25.0  | 1500 | 0.3032          | 0.9261    | 0.9379 | 0.9319 | 0.9419   |
+| 0.1509        | 29.17 | 1750 | 0.2980          | 0.9261    | 0.9386 | 0.9323 | 0.9368   |
+| 0.0848        | 33.33 | 2000 | 0.2996          | 0.9226    | 0.9371 | 0.9298 | 0.9385   |
+| 0.0848        | 37.5  | 2250 | 0.2924          | 0.9276    | 0.9394 | 0.9334 | 0.9440   |
+| 0.0619        | 41.67 | 2500 | 0.2908          | 0.9297    | 0.9401 | 0.9349 | 0.9435   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.13.1
+- Tokenizers 0.13.3