update model card README.md

README.md

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+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- data_cartas_layoutv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-letter_100
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: data_cartas_layoutv3
+      type: data_cartas_layoutv3
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.7411894273127754
+    - name: Recall
+      type: recall
+      value: 0.8672680412371134
+    - name: F1
+      type: f1
+      value: 0.7992874109263659
+    - name: Accuracy
+      type: accuracy
+      value: 0.9631952889969916
+---
+
+<!-- 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-letter_100
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cartas_layoutv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1884
+- Precision: 0.7412
+- Recall: 0.8673
+- F1: 0.7993
+- Accuracy: 0.9632
+
+## 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: 5e-06
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- training_steps: 3000
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 3.57  | 250  | 0.4503          | 0.2934    | 0.2242 | 0.2542 | 0.8928   |
+| 0.5521        | 7.14  | 500  | 0.2833          | 0.4291    | 0.4639 | 0.4458 | 0.9209   |
+| 0.5521        | 10.71 | 750  | 0.2116          | 0.5702    | 0.6753 | 0.6183 | 0.9437   |
+| 0.173         | 14.29 | 1000 | 0.1786          | 0.6414    | 0.7835 | 0.7053 | 0.9562   |
+| 0.173         | 17.86 | 1250 | 0.1772          | 0.6815    | 0.8492 | 0.7562 | 0.9581   |
+| 0.077         | 21.43 | 1500 | 0.1737          | 0.7144    | 0.8737 | 0.7861 | 0.9616   |
+| 0.077         | 25.0  | 1750 | 0.1768          | 0.7311    | 0.8724 | 0.7955 | 0.9615   |
+| 0.0441        | 28.57 | 2000 | 0.1694          | 0.7726    | 0.8273 | 0.7990 | 0.9646   |
+| 0.0441        | 32.14 | 2250 | 0.1874          | 0.7400    | 0.8621 | 0.7964 | 0.9620   |
+| 0.0293        | 35.71 | 2500 | 0.1862          | 0.7321    | 0.8698 | 0.7951 | 0.9622   |
+| 0.0293        | 39.29 | 2750 | 0.1887          | 0.7332    | 0.8711 | 0.7962 | 0.9620   |
+| 0.0237        | 42.86 | 3000 | 0.1884          | 0.7412    | 0.8673 | 0.7993 | 0.9632   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3