Upload folder using huggingface_hub

README.md

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+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- Image Regression
+datasets:
+- "tonyassi/sales1"
+metrics:
+- accuracy
+model-index:
+- name: "sales-prediction13"
+  results: []
+---
+
+# sales-prediction13
+## Image Regression Model
+
+This model was trained with [Image Regression Model Trainer](https://github.com/TonyAssi/ImageRegression/tree/main). It takes an image as input and outputs a float value.
+
+```python
+from ImageRegression import predict
+predict(repo_id='tonyassi/sales-prediction13',image_path='image.jpg')
+```
+
+---
+
+## Dataset
+Dataset: tonyassi/sales1\
+Value Column: 'sales'\
+Train Test Split: 0.2
+
+---
+
+## Training
+Base Model: [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)\
+Epochs: 10\
+Learning Rate: 0.0001
+
+---
+
+## Usage
+
+### Download
+```bash
+git clone https://github.com/TonyAssi/ImageRegression.git
+cd ImageRegression
+```
+
+### Installation
+```bash
+pip install -r requirements.txt
+```
+
+### Import 
+```python
+from ImageRegression import train_model, upload_model, predict
+```
+
+### Inference (Prediction)
+- **repo_id** 🤗 repo id of the model
+- **image_path** path to image
+```python
+predict(repo_id='tonyassi/sales-prediction13',
+        image_path='image.jpg')
+```
+The first time this function is called it'll download the safetensor model. Subsequent function calls will run faster.
+
+### Train Model
+- **dataset_id** 🤗 dataset id
+- **value_column_name** column name of prediction values in dataset
+- **test_split** test split of the train/test split
+- **output_dir** the directory where the checkpoints will be saved
+- **num_train_epochs** training epochs
+- **learning_rate** learning rate
+```python
+train_model(dataset_id='tonyassi/sales1',
+            value_column_name='sales',
+            test_split=0.2,
+            output_dir='./results',
+            num_train_epochs=10,
+            learning_rate=0.0001)
+
+```
+The trainer will save the checkpoints in the output_dir location. The model.safetensors are the trained weights you'll use for inference (predicton).
+
+### Upload Model
+This function will upload your model to the 🤗 Hub.
+- **model_id** the name of the model id
+- **token** go [here](https://huggingface.co/settings/tokens) to create a new 🤗 token
+- **checkpoint_dir** checkpoint folder that will be uploaded
+```python
+upload_model(model_id='sales-prediction13',
+             token='YOUR_HF_TOKEN',
+             checkpoint_dir='./results/checkpoint-940')
+```

metadata.json

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+{
+    "dataset_id": "tonyassi/sales1",
+    "value_column_name": "sales",
+    "test_split": 0.2,
+    "num_train_epochs": 10,
+    "learning_rate": 0.0001,
+    "max_value": 100000
+}

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