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: 0.9407407407407408
+    - name: Recall
+      type: recall
+      value: 0.9505988023952096
+    - name: F1
+      type: f1
+      value: 0.9456440804169769
+    - name: Accuracy
+      type: accuracy
+      value: 0.9584040747028862
+---
+
+<!-- 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: 0.2012
+- Precision: 0.9407
+- Recall: 0.9506
+- F1: 0.9456
+- Accuracy: 0.9584
+
+## 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  | 1.0522          | 0.6964    | 0.7657 | 0.7294 | 0.7831   |
+| 1.4089        | 3.12  | 500  | 0.5725          | 0.84      | 0.8645 | 0.8521 | 0.8786   |
+| 1.4089        | 4.69  | 750  | 0.3936          | 0.8720    | 0.9027 | 0.8871 | 0.9104   |
+| 0.3977        | 6.25  | 1000 | 0.3240          | 0.9204    | 0.9349 | 0.9276 | 0.9397   |
+| 0.3977        | 7.81  | 1250 | 0.2827          | 0.9244    | 0.9341 | 0.9293 | 0.9414   |
+| 0.2176        | 9.38  | 1500 | 0.2381          | 0.9225    | 0.9349 | 0.9286 | 0.9452   |
+| 0.2176        | 10.94 | 1750 | 0.2497          | 0.9161    | 0.9319 | 0.9239 | 0.9419   |
+| 0.1565        | 12.5  | 2000 | 0.2149          | 0.9392    | 0.9484 | 0.9438 | 0.9520   |
+| 0.1565        | 14.06 | 2250 | 0.2075          | 0.9348    | 0.9446 | 0.9397 | 0.9542   |
+| 0.1192        | 15.62 | 2500 | 0.2012          | 0.9407    | 0.9506 | 0.9456 | 0.9584   |
+
+
+### Framework versions
+
+- Transformers 4.28.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3