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:
+- 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: 0.953125
+    - name: Recall
+      type: recall
+      value: 0.9588323353293413
+    - name: F1
+      type: f1
+      value: 0.9559701492537314
+    - name: Accuracy
+      type: accuracy
+      value: 0.965195246179966
+---
+
+<!-- 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: 0.1913
+- Precision: 0.9531
+- Recall: 0.9588
+- F1: 0.9560
+- Accuracy: 0.9652
+
+## 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        | 1.56  | 250  | 1.0033          | 0.7434    | 0.7957 | 0.7686 | 0.8060   |
+| 1.3714        | 3.12  | 500  | 0.5413          | 0.8534    | 0.8757 | 0.8644 | 0.8769   |
+| 1.3714        | 4.69  | 750  | 0.3792          | 0.9013    | 0.9162 | 0.9087 | 0.9219   |
+| 0.3763        | 6.25  | 1000 | 0.2743          | 0.9333    | 0.9431 | 0.9382 | 0.9457   |
+| 0.3763        | 7.81  | 1250 | 0.2404          | 0.9313    | 0.9439 | 0.9375 | 0.9495   |
+| 0.2026        | 9.38  | 1500 | 0.2479          | 0.9325    | 0.9409 | 0.9367 | 0.9431   |
+| 0.2026        | 10.94 | 1750 | 0.2001          | 0.9338    | 0.9499 | 0.9417 | 0.9559   |
+| 0.1349        | 12.5  | 2000 | 0.2102          | 0.9407    | 0.9499 | 0.9453 | 0.9571   |
+| 0.1349        | 14.06 | 2250 | 0.1961          | 0.9560    | 0.9603 | 0.9582 | 0.9648   |
+| 0.104         | 15.62 | 2500 | 0.1913          | 0.9531    | 0.9588 | 0.9560 | 0.9652   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.0
+- Tokenizers 0.13.3