update model card README.md

README.md

@@ -0,0 +1,98 @@
+---
+license: cc-by-nc-sa-4.0
+tags:
+- generated_from_trainer
+datasets:
+- data_cedulas_layoutv3
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: layoutlmv3-finetuned-cedulas_v3
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: data_cedulas_layoutv3
+      type: data_cedulas_layoutv3
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.8991596638655462
+    - name: Recall
+      type: recall
+      value: 0.9067796610169492
+    - name: F1
+      type: f1
+      value: 0.9029535864978903
+    - name: Accuracy
+      type: accuracy
+      value: 0.9816565809379728
+---
+
+<!-- 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-cedulas_v3
+
+This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the data_cedulas_layoutv3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0832
+- Precision: 0.8992
+- Recall: 0.9068
+- F1: 0.9030
+- Accuracy: 0.9817
+
+## 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: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 3.12  | 250  | 0.7409          | 0.2850    | 0.2729 | 0.2788 | 0.8614   |
+| 0.9048        | 6.25  | 500  | 0.3660          | 0.6222    | 0.6559 | 0.6386 | 0.9393   |
+| 0.9048        | 9.38  | 750  | 0.2132          | 0.7492    | 0.7593 | 0.7542 | 0.9544   |
+| 0.2923        | 12.5  | 1000 | 0.1467          | 0.7830    | 0.7949 | 0.7889 | 0.9661   |
+| 0.2923        | 15.62 | 1250 | 0.1172          | 0.8114    | 0.8237 | 0.8175 | 0.9701   |
+| 0.1445        | 18.75 | 1500 | 0.1013          | 0.8560    | 0.8763 | 0.8660 | 0.9766   |
+| 0.1445        | 21.88 | 1750 | 0.0952          | 0.8811    | 0.8915 | 0.8863 | 0.9794   |
+| 0.0956        | 25.0  | 2000 | 0.0876          | 0.8923    | 0.8983 | 0.8953 | 0.9807   |
+| 0.0956        | 28.12 | 2250 | 0.0840          | 0.9005    | 0.9051 | 0.9028 | 0.9811   |
+| 0.0766        | 31.25 | 2500 | 0.0832          | 0.8992    | 0.9068 | 0.9030 | 0.9817   |
+
+
+### Framework versions
+
+- Transformers 4.29.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3