update model card README.md

README.md

@@ -0,0 +1,98 @@
+---
+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: test
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8132267441860465
+    - name: Recall
+      type: recall
+      value: 0.8375748502994012
+    - name: F1
+      type: f1
+      value: 0.8252212389380531
+    - name: Accuracy
+      type: accuracy
+      value: 0.8408319185059423
+---
+
+<!-- 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: 1.0498
+- Precision: 0.8132
+- Recall: 0.8376
+- F1: 0.8252
+- Accuracy: 0.8408
+
+## 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        | 41.67  | 250  | 1.0301          | 0.7519    | 0.7964 | 0.7735 | 0.7954   |
+| 1.0019        | 83.33  | 500  | 0.8414          | 0.7996    | 0.8331 | 0.8160 | 0.8396   |
+| 1.0019        | 125.0  | 750  | 0.9117          | 0.8097    | 0.8376 | 0.8234 | 0.8417   |
+| 0.0561        | 166.67 | 1000 | 0.9434          | 0.8097    | 0.8376 | 0.8234 | 0.8408   |
+| 0.0561        | 208.33 | 1250 | 1.0014          | 0.8049    | 0.8338 | 0.8191 | 0.8404   |
+| 0.0208        | 250.0  | 1500 | 1.0132          | 0.8081    | 0.8353 | 0.8215 | 0.8400   |
+| 0.0208        | 291.67 | 1750 | 1.0279          | 0.8154    | 0.8398 | 0.8274 | 0.8417   |
+| 0.0133        | 333.33 | 2000 | 1.0402          | 0.8119    | 0.8368 | 0.8242 | 0.8408   |
+| 0.0133        | 375.0  | 2250 | 1.0495          | 0.8154    | 0.8398 | 0.8274 | 0.8417   |
+| 0.0108        | 416.67 | 2500 | 1.0498          | 0.8132    | 0.8376 | 0.8252 | 0.8408   |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3