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_2
+  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_2
+
+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: 0.3354
+- Accuracy: 0.8788
+- Brier Loss: 0.2243
+- Nll: 1.0945
+- F1 Micro: 0.8788
+- F1 Macro: 0.8793
+- Ece: 0.1094
+- Aurc: 0.0303
+
+## 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: 2
+- eval_batch_size: 2
+- 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   |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
+| 0.1996        | 1.0   | 8000  | 0.2395          | 0.8413   | 0.2593     | 1.3668 | 0.8413   | 0.8446   | 0.1016 | 0.0414 |
+| 0.1357        | 2.0   | 16000 | 0.2446          | 0.8545   | 0.2435     | 1.2587 | 0.8545   | 0.8550   | 0.1066 | 0.0364 |
+| 0.1083        | 3.0   | 24000 | 0.2702          | 0.8515   | 0.2584     | 1.2968 | 0.8515   | 0.8528   | 0.1126 | 0.0379 |
+| 0.0578        | 4.0   | 32000 | 0.3397          | 0.8327   | 0.2931     | 1.3827 | 0.8327   | 0.8322   | 0.1391 | 0.0494 |
+| 0.0294        | 5.0   | 40000 | 0.3407          | 0.8538   | 0.2662     | 1.3685 | 0.8537   | 0.8536   | 0.1280 | 0.0378 |
+| 0.0099        | 6.0   | 48000 | 0.3489          | 0.8585   | 0.2602     | 1.2438 | 0.8585   | 0.8599   | 0.1254 | 0.0350 |
+| 0.0058        | 7.0   | 56000 | 0.3433          | 0.868    | 0.2418     | 1.2328 | 0.868    | 0.8666   | 0.1172 | 0.0362 |
+| 0.0026        | 8.0   | 64000 | 0.3414          | 0.87     | 0.2401     | 1.1910 | 0.87     | 0.8704   | 0.1162 | 0.0304 |
+| 0.0026        | 9.0   | 72000 | 0.3460          | 0.8728   | 0.2357     | 1.1567 | 0.8728   | 0.8729   | 0.1143 | 0.0308 |
+| 0.0024        | 10.0  | 80000 | 0.3354          | 0.8788   | 0.2243     | 1.0945 | 0.8788   | 0.8793   | 0.1094 | 0.0303 |
+
+
+### 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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