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: train
+      args: cord
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9289415247964471
+    - name: Recall
+      type: recall
+      value: 0.9393712574850299
+    - name: F1
+      type: f1
+      value: 0.9341272794938594
+    - name: Accuracy
+      type: accuracy
+      value: 0.9393039049235993
+---
+
+<!-- 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: 0.3066
+- Precision: 0.9289
+- Recall: 0.9394
+- F1: 0.9341
+- Accuracy: 0.9393
+
+## 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  | 0.9691          | 0.7365    | 0.7867 | 0.7608 | 0.7992   |
+| 1.3706        | 8.33  | 500  | 0.5325          | 0.8645    | 0.8885 | 0.8763 | 0.8858   |
+| 1.3706        | 12.5  | 750  | 0.3943          | 0.8939    | 0.9139 | 0.9038 | 0.9151   |
+| 0.3211        | 16.67 | 1000 | 0.3364          | 0.9209    | 0.9319 | 0.9263 | 0.9342   |
+| 0.3211        | 20.83 | 1250 | 0.3217          | 0.9246    | 0.9364 | 0.9305 | 0.9346   |
+| 0.1405        | 25.0  | 1500 | 0.3100          | 0.9296    | 0.9394 | 0.9345 | 0.9355   |
+| 0.1405        | 29.17 | 1750 | 0.3113          | 0.9275    | 0.9386 | 0.9330 | 0.9363   |
+| 0.076         | 33.33 | 2000 | 0.3183          | 0.9280    | 0.9364 | 0.9322 | 0.9351   |
+| 0.076         | 37.5  | 2250 | 0.3125          | 0.9211    | 0.9356 | 0.9283 | 0.9363   |
+| 0.0549        | 41.67 | 2500 | 0.3066          | 0.9289    | 0.9394 | 0.9341 | 0.9393   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2