update model card README.md

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: dit-base-Document_Classification-Desafio_1
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: validation
+      split: train
+      args: validation
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9865
+---
+
+<!-- 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. -->
+
+# dit-base-Document_Classification-Desafio_1
+
+This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0436
+- Accuracy: 0.9865
+- Weighted f1: 0.9865
+- Micro f1: 0.9865
+- Macro f1: 0.9863
+- Weighted recall: 0.9865
+- Micro recall: 0.9865
+- Macro recall: 0.9861
+- Weighted precision: 0.9869
+- Micro precision: 0.9865
+- Macro precision: 0.9870
+
+## 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-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
+| 0.8316        | 0.99  | 62   | 0.7519          | 0.743    | 0.7020      | 0.743    | 0.7015   | 0.743           | 0.743        | 0.7430       | 0.6827             | 0.743           | 0.6819          |
+| 0.3561        | 2.0   | 125  | 0.2302          | 0.9395   | 0.9401      | 0.9395   | 0.9400   | 0.9395          | 0.9395       | 0.9394       | 0.9482             | 0.9395          | 0.9480          |
+| 0.2222        | 2.99  | 187  | 0.1350          | 0.956    | 0.9564      | 0.956    | 0.9561   | 0.956           | 0.956        | 0.9551       | 0.9598             | 0.956           | 0.9600          |
+| 0.1705        | 4.0   | 250  | 0.0873          | 0.9725   | 0.9727      | 0.9725   | 0.9725   | 0.9725          | 0.9725       | 0.9721       | 0.9740             | 0.9725          | 0.9740          |
+| 0.1541        | 4.99  | 312  | 0.0642          | 0.9825   | 0.9825      | 0.9825   | 0.9824   | 0.9825          | 0.9825       | 0.9822       | 0.9830             | 0.9825          | 0.9830          |
+| 0.1253        | 6.0   | 375  | 0.0330          | 0.9915   | 0.9915      | 0.9915   | 0.9914   | 0.9915          | 0.9915       | 0.9913       | 0.9916             | 0.9915          | 0.9916          |
+| 0.1196        | 6.99  | 437  | 0.0524          | 0.982    | 0.9822      | 0.982    | 0.9820   | 0.982           | 0.982        | 0.9817       | 0.9832             | 0.982           | 0.9832          |
+| 0.0896        | 7.94  | 496  | 0.0436          | 0.9865   | 0.9865      | 0.9865   | 0.9863   | 0.9865          | 0.9865       | 0.9861       | 0.9869             | 0.9865          | 0.9870          |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- Pytorch 2.0.0
+- Datasets 2.11.0
+- Tokenizers 0.13.3