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: 0.9385640266469282
+    - name: Recall
+      type: recall
+      value: 0.9491017964071856
+    - name: F1
+      type: f1
+      value: 0.9438034983252697
+    - name: Accuracy
+      type: accuracy
+      value: 0.9516129032258065
+---
+
+<!-- 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: 0.2144
+- Precision: 0.9386
+- Recall: 0.9491
+- F1: 0.9438
+- Accuracy: 0.9516
+
+## 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  | 1.0830          | 0.6854    | 0.7582 | 0.7200 | 0.7725   |
+| 1.4266        | 3.12  | 500  | 0.5944          | 0.8379    | 0.8630 | 0.8503 | 0.8680   |
+| 1.4266        | 4.69  | 750  | 0.3868          | 0.8828    | 0.9079 | 0.8952 | 0.9155   |
+| 0.4084        | 6.25  | 1000 | 0.3146          | 0.9133    | 0.9304 | 0.9218 | 0.9338   |
+| 0.4084        | 7.81  | 1250 | 0.2658          | 0.9240    | 0.9371 | 0.9305 | 0.9419   |
+| 0.2139        | 9.38  | 1500 | 0.2432          | 0.9299    | 0.9439 | 0.9368 | 0.9474   |
+| 0.2139        | 10.94 | 1750 | 0.2333          | 0.9291    | 0.9416 | 0.9353 | 0.9482   |
+| 0.1478        | 12.5  | 2000 | 0.2098          | 0.9358    | 0.9491 | 0.9424 | 0.9529   |
+| 0.1478        | 14.06 | 2250 | 0.2134          | 0.9379    | 0.9491 | 0.9435 | 0.9516   |
+| 0.1124        | 15.62 | 2500 | 0.2144          | 0.9386    | 0.9491 | 0.9438 | 0.9516   |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1