update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- sroie
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: test_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: sroie
+      type: sroie
+      config: discharge
+      split: test
+      args: discharge
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9343065693430657
+    - name: Recall
+      type: recall
+      value: 0.9696969696969697
+    - name: F1
+      type: f1
+      value: 0.9516728624535317
+    - name: Accuracy
+      type: accuracy
+      value: 0.9976019184652278
+---
+
+<!-- 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. -->
+
+# test_model
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the sroie dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0114
+- Precision: 0.9343
+- Recall: 0.9697
+- F1: 0.9517
+- Accuracy: 0.9976
+
+## 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: 1000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 8.33  | 100  | 0.0292          | 0.8732    | 0.9394 | 0.9051 | 0.9928   |
+| No log        | 16.67 | 200  | 0.0110          | 0.9343    | 0.9697 | 0.9517 | 0.9976   |
+| No log        | 25.0  | 300  | 0.0130          | 0.9209    | 0.9697 | 0.9446 | 0.9971   |
+| No log        | 33.33 | 400  | 0.0110          | 0.9412    | 0.9697 | 0.9552 | 0.9981   |
+| 0.0466        | 41.67 | 500  | 0.0114          | 0.9275    | 0.9697 | 0.9481 | 0.9976   |
+| 0.0466        | 50.0  | 600  | 0.0117          | 0.9275    | 0.9697 | 0.9481 | 0.9976   |
+| 0.0466        | 58.33 | 700  | 0.0114          | 0.9275    | 0.9697 | 0.9481 | 0.9976   |
+| 0.0466        | 66.67 | 800  | 0.0114          | 0.9343    | 0.9697 | 0.9517 | 0.9976   |
+| 0.0466        | 75.0  | 900  | 0.0115          | 0.9343    | 0.9697 | 0.9517 | 0.9976   |
+| 0.0006        | 83.33 | 1000 | 0.0114          | 0.9343    | 0.9697 | 0.9517 | 0.9976   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.0+cu118
+- Datasets 2.2.2
+- Tokenizers 0.13.3