Saving best model to hub

README.md

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+---
+license: apache-2.0
+base_model: jordyvl/vit-base_rvl-cdip
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: vit-base_rvl_cdip-N1K_aAURC_32
+  results: []
+---
+
+<!-- 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. -->
+
+# vit-base_rvl_cdip-N1K_aAURC_32
+
+This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5215
+- Accuracy: 0.888
+- Brier Loss: 0.1918
+- Nll: 0.9026
+- F1 Micro: 0.888
+- F1 Macro: 0.8883
+- Ece: 0.0880
+- Aurc: 0.0205
+
+## 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: 2e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- 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 | Brier Loss | Nll    | F1 Micro | F1 Macro | Ece    | Aurc   |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
+| 0.1629        | 1.0   | 500  | 0.3779          | 0.8875   | 0.1721     | 1.1899 | 0.8875   | 0.8877   | 0.0531 | 0.0201 |
+| 0.1234        | 2.0   | 1000 | 0.4074          | 0.8868   | 0.1790     | 1.1333 | 0.8868   | 0.8874   | 0.0647 | 0.0213 |
+| 0.0616        | 3.0   | 1500 | 0.4257          | 0.888    | 0.1813     | 1.0677 | 0.888    | 0.8879   | 0.0695 | 0.0201 |
+| 0.0303        | 4.0   | 2000 | 0.4595          | 0.885    | 0.1869     | 1.0256 | 0.885    | 0.8856   | 0.0776 | 0.0222 |
+| 0.0133        | 5.0   | 2500 | 0.4902          | 0.8848   | 0.1922     | 0.9983 | 0.8848   | 0.8849   | 0.0831 | 0.0228 |
+| 0.0083        | 6.0   | 3000 | 0.4941          | 0.8862   | 0.1903     | 0.9464 | 0.8862   | 0.8868   | 0.0850 | 0.0211 |
+| 0.0051        | 7.0   | 3500 | 0.5116          | 0.8875   | 0.1928     | 0.9118 | 0.8875   | 0.8873   | 0.0875 | 0.0207 |
+| 0.0043        | 8.0   | 4000 | 0.5154          | 0.8882   | 0.1910     | 0.9138 | 0.8882   | 0.8887   | 0.0864 | 0.0205 |
+| 0.0041        | 9.0   | 4500 | 0.5221          | 0.8865   | 0.1924     | 0.9101 | 0.8865   | 0.8868   | 0.0896 | 0.0206 |
+| 0.0037        | 10.0  | 5000 | 0.5215          | 0.888    | 0.1918     | 0.9026 | 0.888    | 0.8883   | 0.0880 | 0.0205 |
+
+
+### Framework versions
+
+- Transformers 4.33.3
+- Pytorch 2.2.0.dev20231002
+- Datasets 2.7.1
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "jordyvl/vit-base_rvl-cdip",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "letter",
+    "1": "form",
+    "2": "email",
+    "3": "handwritten",
+    "4": "advertisement",
+    "5": "scientific_report",
+    "6": "scientific_publication",
+    "7": "specification",
+    "8": "file_folder",
+    "9": "news_article",
+    "10": "budget",
+    "11": "invoice",
+    "12": "presentation",
+    "13": "questionnaire",
+    "14": "resume",
+    "15": "memo"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "advertisement": 4,
+    "budget": 10,
+    "email": 2,
+    "file_folder": 8,
+    "form": 1,
+    "handwritten": 3,
+    "invoice": 11,
+    "letter": 0,
+    "memo": 15,
+    "news_article": 9,
+    "presentation": 12,
+    "questionnaire": 13,
+    "resume": 14,
+    "scientific_publication": 6,
+    "scientific_report": 5,
+    "specification": 7
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.33.3"
+}

pytorch_model.bin

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training_args.bin

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