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: 0.9457652303120356
+    - name: Recall
+      type: recall
+      value: 0.9528443113772455
+    - name: F1
+      type: f1
+      value: 0.9492915734526474
+    - name: Accuracy
+      type: accuracy
+      value: 0.9490662139219015
+---
+
+<!-- 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: 0.2296
+- Precision: 0.9458
+- Recall: 0.9528
+- F1: 0.9493
+- Accuracy: 0.9491
+
+## 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  | 1.1659          | 0.6767    | 0.7552 | 0.7138 | 0.7738   |
+| 1.4723        | 3.12  | 500  | 0.6092          | 0.8320    | 0.8600 | 0.8458 | 0.8667   |
+| 1.4723        | 4.69  | 750  | 0.4107          | 0.8730    | 0.9004 | 0.8865 | 0.9045   |
+| 0.4246        | 6.25  | 1000 | 0.3370          | 0.9143    | 0.9259 | 0.9200 | 0.9270   |
+| 0.4246        | 7.81  | 1250 | 0.2909          | 0.9267    | 0.9371 | 0.9319 | 0.9372   |
+| 0.2225        | 9.38  | 1500 | 0.2571          | 0.9355    | 0.9439 | 0.9396 | 0.9414   |
+| 0.2225        | 10.94 | 1750 | 0.2547          | 0.9383    | 0.9454 | 0.9418 | 0.9431   |
+| 0.1514        | 12.5  | 2000 | 0.2412          | 0.9306    | 0.9431 | 0.9368 | 0.9435   |
+| 0.1514        | 14.06 | 2250 | 0.2329          | 0.9443    | 0.9513 | 0.9478 | 0.9478   |
+| 0.1168        | 15.62 | 2500 | 0.2296          | 0.9458    | 0.9528 | 0.9493 | 0.9491   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.10.2+cpu
+- Datasets 2.8.0
+- Tokenizers 0.13.2