update model card README.md

README.md

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+---
+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
+      args: funsd
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9026198714780029
+    - name: Recall
+      type: recall
+      value: 0.913
+    - name: F1
+      type: f1
+      value: 0.9077802634849614
+    - name: Accuracy
+      type: accuracy
+      value: 0.8330271015158475
+---
+
+<!-- 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: 1.1164
+- Precision: 0.9026
+- Recall: 0.913
+- F1: 0.9078
+- Accuracy: 0.8330
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 10.0  | 100  | 0.5238          | 0.8366    | 0.886  | 0.8606 | 0.8410   |
+| No log        | 20.0  | 200  | 0.6930          | 0.8751    | 0.8965 | 0.8857 | 0.8322   |
+| No log        | 30.0  | 300  | 0.7784          | 0.8902    | 0.908  | 0.8990 | 0.8414   |
+| No log        | 40.0  | 400  | 0.9056          | 0.8916    | 0.905  | 0.8983 | 0.8364   |
+| 0.2429        | 50.0  | 500  | 1.0016          | 0.8954    | 0.9075 | 0.9014 | 0.8298   |
+| 0.2429        | 60.0  | 600  | 1.0097          | 0.8899    | 0.897  | 0.8934 | 0.8294   |
+| 0.2429        | 70.0  | 700  | 1.0722          | 0.9035    | 0.9085 | 0.9060 | 0.8315   |
+| 0.2429        | 80.0  | 800  | 1.0884          | 0.8905    | 0.9105 | 0.9004 | 0.8269   |
+| 0.2429        | 90.0  | 900  | 1.1292          | 0.8938    | 0.909  | 0.9013 | 0.8279   |
+| 0.0098        | 100.0 | 1000 | 1.1164          | 0.9026    | 0.913  | 0.9078 | 0.8330   |
+
+
+### Framework versions
+
+- Transformers 4.19.0.dev0
+- Pytorch 1.11.0+cu113
+- Datasets 2.0.0
+- Tokenizers 0.11.6