update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+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: 0.9256806475349522
+    - name: Recall
+      type: recall
+      value: 0.9416167664670658
+    - name: F1
+      type: f1
+      value: 0.9335807050092764
+    - name: Accuracy
+      type: accuracy
+      value: 0.9460950764006791
+---
+
+<!-- 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: 0.2933
+- Precision: 0.9257
+- Recall: 0.9416
+- F1: 0.9336
+- Accuracy: 0.9461
+
+## 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        | 4.17  | 250  | 1.0415          | 0.7691    | 0.8129 | 0.7904 | 0.8132   |
+| 1.3968        | 8.33  | 500  | 0.5604          | 0.8509    | 0.8757 | 0.8632 | 0.8722   |
+| 1.3968        | 12.5  | 750  | 0.4191          | 0.8833    | 0.9064 | 0.8947 | 0.9092   |
+| 0.3531        | 16.67 | 1000 | 0.3352          | 0.9139    | 0.9296 | 0.9217 | 0.9308   |
+| 0.3531        | 20.83 | 1250 | 0.3185          | 0.9189    | 0.9326 | 0.9257 | 0.9351   |
+| 0.161         | 25.0  | 1500 | 0.3069          | 0.9177    | 0.9349 | 0.9262 | 0.9389   |
+| 0.161         | 29.17 | 1750 | 0.2989          | 0.9270    | 0.9409 | 0.9339 | 0.9448   |
+| 0.0956        | 33.33 | 2000 | 0.2897          | 0.9242    | 0.9394 | 0.9317 | 0.9440   |
+| 0.0956        | 37.5  | 2250 | 0.2893          | 0.9242    | 0.9401 | 0.9321 | 0.9452   |
+| 0.0704        | 41.67 | 2500 | 0.2933          | 0.9257    | 0.9416 | 0.9336 | 0.9461   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3