update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- funsd
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-funsd
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: funsd
+      type: funsd
+      config: funsd
+      split: test
+      args: funsd
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7998102466793169
+    - name: Recall
+      type: recall
+      value: 0.8375558867362146
+    - name: F1
+      type: f1
+      value: 0.8182479980587235
+    - name: Accuracy
+      type: accuracy
+      value: 0.826102460477832
+---
+
+<!-- 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-funsd
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.0068
+- Precision: 0.7998
+- Recall: 0.8376
+- F1: 0.8182
+- Accuracy: 0.8261
+
+## 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: 2
+- eval_batch_size: 2
+- 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        | 3.33  | 250  | 0.5828          | 0.7015    | 0.8033 | 0.7490 | 0.8022   |
+| 0.6702        | 6.67  | 500  | 0.5765          | 0.7499    | 0.8073 | 0.7775 | 0.8253   |
+| 0.6702        | 10.0  | 750  | 0.7082          | 0.7755    | 0.8236 | 0.7988 | 0.8160   |
+| 0.1797        | 13.33 | 1000 | 0.7819          | 0.7807    | 0.8366 | 0.8077 | 0.8256   |
+| 0.1797        | 16.67 | 1250 | 0.8199          | 0.7997    | 0.8311 | 0.8151 | 0.8227   |
+| 0.0745        | 20.0  | 1500 | 0.9025          | 0.7943    | 0.8286 | 0.8111 | 0.8231   |
+| 0.0745        | 23.33 | 1750 | 0.9159          | 0.7941    | 0.8470 | 0.8197 | 0.8248   |
+| 0.041         | 26.67 | 2000 | 1.0012          | 0.7989    | 0.8385 | 0.8182 | 0.8210   |
+| 0.041         | 30.0  | 2250 | 0.9852          | 0.8024    | 0.8450 | 0.8231 | 0.8301   |
+| 0.0246        | 33.33 | 2500 | 1.0068          | 0.7998    | 0.8376 | 0.8182 | 0.8261   |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0+cu118
+- Datasets 2.11.0
+- Tokenizers 0.13.3