update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+base_model: microsoft/layoutlmv3-base
+tags:
+- generated_from_trainer
+datasets:
+- cne-layoutlmv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cne_nvidia_100
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: cne-layoutlmv3
+      type: cne-layoutlmv3
+      config: cne-dataset
+      split: test
+      args: cne-dataset
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9950738916256158
+    - name: Recall
+      type: recall
+      value: 0.9950738916256158
+    - name: F1
+      type: f1
+      value: 0.9950738916256159
+    - name: Accuracy
+      type: accuracy
+      value: 0.9992716678805535
+---
+
+<!-- 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-cne_nvidia_100
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cne-layoutlmv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0064
+- Precision: 0.9951
+- Recall: 0.9951
+- F1: 0.9951
+- Accuracy: 0.9993
+
+## 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: 3
+- eval_batch_size: 3
+- 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        | 7.81  | 250  | 0.0143          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.1596        | 15.62 | 500  | 0.0085          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.1596        | 23.44 | 750  | 0.0074          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.0195        | 31.25 | 1000 | 0.0068          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.0195        | 39.06 | 1250 | 0.0067          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.008         | 46.88 | 1500 | 0.0067          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.008         | 54.69 | 1750 | 0.0064          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.0034        | 62.5  | 2000 | 0.0063          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.0034        | 70.31 | 2250 | 0.0063          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+| 0.0023        | 78.12 | 2500 | 0.0064          | 0.9951    | 0.9951 | 0.9951 | 0.9993   |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1
+- Datasets 2.14.3
+- Tokenizers 0.13.3