update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- doc_lay_net-small
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: Layoutlmv3-finetuned-DocLayNet-test
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: doc_lay_net-small
+      type: doc_lay_net-small
+      config: DocLayNet_2022.08_processed_on_2023.01
+      split: test
+      args: DocLayNet_2022.08_processed_on_2023.01
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.5207226354941552
+    - name: Recall
+      type: recall
+      value: 0.7111756168359942
+    - name: F1
+      type: f1
+      value: 0.6012269938650306
+    - name: Accuracy
+      type: accuracy
+      value: 0.842051017778923
+---
+
+<!-- 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-DocLayNet-test
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the doc_lay_net-small dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5038
+- Precision: 0.5207
+- Recall: 0.7112
+- F1: 0.6012
+- Accuracy: 0.8421
+
+## 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
+- lr_scheduler_warmup_ratio: 0.1
+- training_steps: 1000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.5092        | 0.37  | 250  | 0.8072          | 0.1922    | 0.2342 | 0.2111 | 0.8227   |
+| 0.8608        | 0.73  | 500  | 0.6402          | 0.3963    | 0.6108 | 0.4807 | 0.8596   |
+| 0.6463        | 1.1   | 750  | 0.8042          | 0.5702    | 0.6297 | 0.5985 | 0.8080   |
+| 0.4495        | 1.46  | 1000 | 0.8439          | 0.5353    | 0.6234 | 0.5760 | 0.8033   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.4
+- Tokenizers 0.13.3