update model card README.md

README.md

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+---
+license: apache-2.0
+base_model: microsoft/resnet-50
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+model-index:
+- name: resnet-50-finetuned-masked-hateful-meme-restructured
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.5
+---
+
+<!-- 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. -->
+
+# resnet-50-finetuned-masked-hateful-meme-restructured
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7093
+- Accuracy: 0.5
+
+## 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.6639        | 0.99  | 66   | 0.7093          | 0.5      |
+| 0.6569        | 2.0   | 133  | 0.7295          | 0.5      |
+| 0.6489        | 2.99  | 199  | 0.7257          | 0.5      |
+| 0.6553        | 4.0   | 266  | 0.7274          | 0.5      |
+| 0.6334        | 4.99  | 332  | 0.7311          | 0.5      |
+| 0.627         | 6.0   | 399  | 0.7371          | 0.5      |
+| 0.6561        | 6.99  | 465  | 0.7386          | 0.5      |
+| 0.6552        | 8.0   | 532  | 0.7354          | 0.5      |
+| 0.6427        | 8.99  | 598  | 0.7346          | 0.5      |
+| 0.6451        | 9.92  | 660  | 0.7377          | 0.498    |
+
+
+### Framework versions
+
+- Transformers 4.31.0
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.13.3