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: 0.9393042190969653
+    - name: Recall
+      type: recall
+      value: 0.9498502994011976
+    - name: F1
+      type: f1
+      value: 0.9445478228507629
+    - name: Accuracy
+      type: accuracy
+      value: 0.9494906621392191
+---
+
+<!-- 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: 0.2454
+- Precision: 0.9393
+- Recall: 0.9499
+- F1: 0.9445
+- Accuracy: 0.9495
+
+## 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        | 2.5   | 250  | 1.0544          | 0.7297    | 0.7822 | 0.7551 | 0.7852   |
+| 1.4348        | 5.0   | 500  | 0.5651          | 0.8477    | 0.8705 | 0.8589 | 0.8693   |
+| 1.4348        | 7.5   | 750  | 0.4012          | 0.8833    | 0.9012 | 0.8922 | 0.9083   |
+| 0.4052        | 10.0  | 1000 | 0.3168          | 0.9208    | 0.9311 | 0.9259 | 0.9338   |
+| 0.4052        | 12.5  | 1250 | 0.2823          | 0.9304    | 0.9401 | 0.9352 | 0.9410   |
+| 0.2039        | 15.0  | 1500 | 0.2626          | 0.9242    | 0.9394 | 0.9317 | 0.9397   |
+| 0.2039        | 17.5  | 1750 | 0.2504          | 0.9305    | 0.9424 | 0.9364 | 0.9448   |
+| 0.1333        | 20.0  | 2000 | 0.2425          | 0.9324    | 0.9491 | 0.9407 | 0.9503   |
+| 0.1333        | 22.5  | 2250 | 0.2442          | 0.9371    | 0.9484 | 0.9427 | 0.9486   |
+| 0.1042        | 25.0  | 2500 | 0.2454          | 0.9393    | 0.9499 | 0.9445 | 0.9495   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3