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: test
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9135893648449039
+    - name: Recall
+      type: recall
+      value: 0.9258982035928144
+    - name: F1
+      type: f1
+      value: 0.9197026022304833
+    - name: Accuracy
+      type: accuracy
+      value: 0.9252971137521222
+---
+
+<!-- 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: 0.3248
+- Precision: 0.9136
+- Recall: 0.9259
+- F1: 0.9197
+- Accuracy: 0.9253
+
+## 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  | 1.0188          | 0.7447    | 0.7949 | 0.7690 | 0.8031   |
+| 1.4061        | 8.33  | 500  | 0.5545          | 0.8420    | 0.8653 | 0.8535 | 0.8616   |
+| 1.4061        | 12.5  | 750  | 0.4298          | 0.8884    | 0.9057 | 0.8970 | 0.9045   |
+| 0.3563        | 16.67 | 1000 | 0.3477          | 0.9094    | 0.9244 | 0.9169 | 0.9295   |
+| 0.3563        | 20.83 | 1250 | 0.3189          | 0.9137    | 0.9274 | 0.9205 | 0.9312   |
+| 0.1617        | 25.0  | 1500 | 0.3189          | 0.9210    | 0.9341 | 0.9275 | 0.9393   |
+| 0.1617        | 29.17 | 1750 | 0.3158          | 0.9096    | 0.9259 | 0.9177 | 0.9300   |
+| 0.0942        | 33.33 | 2000 | 0.3198          | 0.9117    | 0.9274 | 0.9195 | 0.9283   |
+| 0.0942        | 37.5  | 2250 | 0.3259          | 0.9112    | 0.9289 | 0.9199 | 0.9300   |
+| 0.0674        | 41.67 | 2500 | 0.3248          | 0.9136    | 0.9259 | 0.9197 | 0.9253   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.0
+- Tokenizers 0.13.2