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: 0.9349593495934959
+    - name: Recall
+      type: recall
+      value: 0.9468562874251497
+    - name: F1
+      type: f1
+      value: 0.9408702119747119
+    - name: Accuracy
+      type: accuracy
+      value: 0.9473684210526315
+---
+
+<!-- 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: 0.2702
+- Precision: 0.9350
+- Recall: 0.9469
+- F1: 0.9409
+- Accuracy: 0.9474
+
+## 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  | 1.0496          | 0.6714    | 0.7507 | 0.7088 | 0.7746   |
+| 1.4245        | 8.33  | 500  | 0.5492          | 0.8401    | 0.8728 | 0.8561 | 0.8735   |
+| 1.4245        | 12.5  | 750  | 0.3773          | 0.8934    | 0.9162 | 0.9047 | 0.9240   |
+| 0.3461        | 16.67 | 1000 | 0.3212          | 0.9287    | 0.9364 | 0.9325 | 0.9380   |
+| 0.3461        | 20.83 | 1250 | 0.2888          | 0.9276    | 0.9401 | 0.9338 | 0.9440   |
+| 0.1502        | 25.0  | 1500 | 0.2749          | 0.9299    | 0.9431 | 0.9365 | 0.9474   |
+| 0.1502        | 29.17 | 1750 | 0.2741          | 0.9321    | 0.9446 | 0.9383 | 0.9469   |
+| 0.0866        | 33.33 | 2000 | 0.2715          | 0.9328    | 0.9454 | 0.9390 | 0.9465   |
+| 0.0866        | 37.5  | 2250 | 0.2740          | 0.9314    | 0.9446 | 0.9379 | 0.9452   |
+| 0.0635        | 41.67 | 2500 | 0.2702          | 0.9350    | 0.9469 | 0.9409 | 0.9474   |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2