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_vimal
+  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: 0.725
+    - name: Recall
+      type: recall
+      value: 0.7631578947368421
+    - name: F1
+      type: f1
+      value: 0.7435897435897436
+    - name: Accuracy
+      type: accuracy
+      value: 0.7407407407407407
+---
+
+<!-- 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_vimal
+
+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: 1.9129
+- Precision: 0.725
+- Recall: 0.7632
+- F1: 0.7436
+- Accuracy: 0.7407
+
+## 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        | 125.0  | 250  | 1.2365          | 0.7195    | 0.7763 | 0.7468 | 0.7556   |
+| 0.8612        | 250.0  | 500  | 1.4859          | 0.7375    | 0.7763 | 0.7564 | 0.7481   |
+| 0.8612        | 375.0  | 750  | 1.6108          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0297        | 500.0  | 1000 | 1.7046          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0297        | 625.0  | 1250 | 1.7805          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0134        | 750.0  | 1500 | 1.8187          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0134        | 875.0  | 1750 | 1.8624          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0089        | 1000.0 | 2000 | 1.8866          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0089        | 1125.0 | 2250 | 1.9056          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+| 0.0073        | 1250.0 | 2500 | 1.9129          | 0.725     | 0.7632 | 0.7436 | 0.7407   |
+
+
+### Framework versions
+
+- Transformers 4.26.0
+- Pytorch 1.13.1+cu116
+- Datasets 2.9.0
+- Tokenizers 0.13.2