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: 0.9485842026825634
+    - name: Recall
+      type: recall
+      value: 0.9528443113772455
+    - name: F1
+      type: f1
+      value: 0.9507094846900671
+    - name: Accuracy
+      type: accuracy
+      value: 0.9592529711375212
+---
+
+<!-- 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: 0.1978
+- Precision: 0.9486
+- Recall: 0.9528
+- F1: 0.9507
+- Accuracy: 0.9593
+
+## 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  | 0.9543          | 0.7832    | 0.8166 | 0.7996 | 0.8226   |
+| 1.3644        | 3.12  | 500  | 0.5338          | 0.8369    | 0.8683 | 0.8523 | 0.8824   |
+| 1.3644        | 4.69  | 750  | 0.3658          | 0.8840    | 0.9072 | 0.8955 | 0.9232   |
+| 0.3802        | 6.25  | 1000 | 0.3019          | 0.9156    | 0.9251 | 0.9203 | 0.9334   |
+| 0.3802        | 7.81  | 1250 | 0.2833          | 0.9094    | 0.9237 | 0.9165 | 0.9346   |
+| 0.2061        | 9.38  | 1500 | 0.2241          | 0.9377    | 0.9469 | 0.9423 | 0.9525   |
+| 0.2061        | 10.94 | 1750 | 0.2282          | 0.9304    | 0.9409 | 0.9356 | 0.9474   |
+| 0.1416        | 12.5  | 2000 | 0.2017          | 0.9509    | 0.9566 | 0.9537 | 0.9610   |
+| 0.1416        | 14.06 | 2250 | 0.2006          | 0.9472    | 0.9536 | 0.9504 | 0.9614   |
+| 0.1056        | 15.62 | 2500 | 0.1978          | 0.9486    | 0.9528 | 0.9507 | 0.9593   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1