End of training

README.md

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+---
+base_model: microsoft/dit-base-finetuned-rvlcdip
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- recall
+- f1
+- precision
+model-index:
+- name: dit-base-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8458918688803746
+    - name: Recall
+      type: recall
+      value: 0.8458918688803746
+    - name: F1
+      type: f1
+      value: 0.8445087759723635
+    - name: Precision
+      type: precision
+      value: 0.8462519380607423
+---
+
+<!-- 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-finetuned-rvlcdip-finetuned-ind-17-imbalanced-aadhaarmask
+
+This model is a fine-tuned version of [microsoft/dit-base-finetuned-rvlcdip](https://huggingface.co/microsoft/dit-base-finetuned-rvlcdip) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3727
+- Accuracy: 0.8459
+- Recall: 0.8459
+- F1: 0.8445
+- Precision: 0.8463
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
+| 0.9625        | 0.9974 | 293  | 0.8121          | 0.7812   | 0.7812 | 0.7600 | 0.7620    |
+| 0.7711        | 1.9983 | 587  | 0.5780          | 0.8135   | 0.8135 | 0.7960 | 0.7843    |
+| 0.555         | 2.9991 | 881  | 0.4868          | 0.8255   | 0.8255 | 0.8133 | 0.8133    |
+| 0.6008        | 4.0    | 1175 | 0.4475          | 0.8357   | 0.8357 | 0.8281 | 0.8253    |
+| 0.5318        | 4.9974 | 1468 | 0.4478          | 0.8267   | 0.8267 | 0.8221 | 0.8254    |
+| 0.3382        | 5.9983 | 1762 | 0.3946          | 0.8463   | 0.8463 | 0.8412 | 0.8427    |
+| 0.4307        | 6.9991 | 2056 | 0.4083          | 0.8344   | 0.8344 | 0.8317 | 0.8362    |
+| 0.4613        | 8.0    | 2350 | 0.3915          | 0.8442   | 0.8442 | 0.8429 | 0.8481    |
+| 0.3247        | 8.9974 | 2643 | 0.3758          | 0.8421   | 0.8421 | 0.8402 | 0.8395    |
+| 0.3965        | 9.9745 | 2930 | 0.3637          | 0.8484   | 0.8484 | 0.8466 | 0.8470    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.2.0a0+81ea7a4
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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