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: 0.9328908554572272
+    - name: Recall
+      type: recall
+      value: 0.9468562874251497
+    - name: F1
+      type: f1
+      value: 0.9398216939078752
+    - 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: 0.2213
+- Precision: 0.9329
+- Recall: 0.9469
+- F1: 0.9398
+- 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  | 1.0664          | 0.6765    | 0.7530 | 0.7127 | 0.7818   |
+| 1.4379        | 3.12  | 500  | 0.6115          | 0.8199    | 0.8518 | 0.8355 | 0.8646   |
+| 1.4379        | 4.69  | 750  | 0.4192          | 0.8794    | 0.9004 | 0.8898 | 0.9028   |
+| 0.4232        | 6.25  | 1000 | 0.3239          | 0.9180    | 0.9296 | 0.9238 | 0.9304   |
+| 0.4232        | 7.81  | 1250 | 0.2840          | 0.9197    | 0.9341 | 0.9268 | 0.9389   |
+| 0.2273        | 9.38  | 1500 | 0.2562          | 0.9217    | 0.9341 | 0.9279 | 0.9376   |
+| 0.2273        | 10.94 | 1750 | 0.2574          | 0.9304    | 0.9401 | 0.9352 | 0.9410   |
+| 0.157         | 12.5  | 2000 | 0.2327          | 0.9293    | 0.9439 | 0.9365 | 0.9482   |
+| 0.157         | 14.06 | 2250 | 0.2217          | 0.9351    | 0.9491 | 0.9421 | 0.9520   |
+| 0.1208        | 15.62 | 2500 | 0.2213          | 0.9329    | 0.9469 | 0.9398 | 0.9516   |
+
+
+### Framework versions
+
+- Transformers 4.23.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.6.1
+- Tokenizers 0.13.1