feat: new toknizer

.gitattributes

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 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+checkpoint-*/

README.md

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 ---
-license: mit
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+model-index:
+- name: resnet_weather_model
+  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: 0.6735537190082644
+    - name: F1
+      type: f1
+      value: 0.6654635943888922
 ---
+
+<!-- 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_weather_model
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7452
+- Accuracy: 0.6736
+- F1: 0.6655
+
+## 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: 0.0002
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 2.3598        | 1.0   | 91   | 2.1983          | 0.5165   | 0.5146 |
+| 2.0319        | 2.0   | 182  | 1.8708          | 0.6446   | 0.6433 |
+| 1.7971        | 3.0   | 273  | 1.7452          | 0.6736   | 0.6655 |
+
+
+### Framework versions
+
+- Transformers 4.25.1
+- Pytorch 1.13.0+cu116
+- Datasets 2.8.0
+- Tokenizers 0.13.2

config.json

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+{
+  "_name_or_path": "/content/gdrive/MyDrive/resnet-50",
+  "architectures": [
+    "ResNetForImageClassification"
+  ],
+  "depths": [
+    3,
+    4,
+    6,
+    3
+  ],
+  "downsample_in_first_stage": false,
+  "embedding_size": 64,
+  "hidden_act": "relu",
+  "hidden_sizes": [
+    256,
+    512,
+    1024,
+    2048
+  ],
+  "id2label": {
+    "0": "dew",
+    "1": "fogsmog",
+    "2": "frost",
+    "3": "glaze",
+    "4": "hail",
+    "5": "lightening",
+    "6": "lightning",
+    "7": "rain",
+    "8": "rainbow",
+    "9": "rime",
+    "10": "sandstorm",
+    "11": "snow"
+  },
+  "label2id": {
+    "dew": 0,
+    "fogsmog": 1,
+    "frost": 2,
+    "glaze": 3,
+    "hail": 4,
+    "lightening": 5,
+    "lightning": 6,
+    "rain": 7,
+    "rainbow": 8,
+    "rime": 9,
+    "sandstorm": 10,
+    "snow": 11
+  },
+  "layer_type": "bottleneck",
+  "model_type": "resnet",
+  "num_channels": 3,
+  "out_features": null,
+  "output_hidden_states": true,
+  "problem_type": "single_label_classification",
+  "stage_names": [
+    "stem",
+    "stage1",
+    "stage2",
+    "stage3",
+    "stage4"
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1"
+}

preprocessor_config.json

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+{
+  "crop_pct": 0.875,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "ConvNextFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_processor_type": "ConvNextImageProcessor",
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}

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