Update model and model card

README.md

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+---
+license: mit
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+widget:
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+    - -151.86175537109375
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+    - 51.011051177978516
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+    - 126.8833236694336
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+    x99:
+    - 209.15476989746094
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+    - -47.24432373046875
+---
+
+# Model description
+
+This is a LogisticRegressionCV model trained on averages of patch embeddings from the Imagenette dataset. This forms the GAM of an [Emb-GAM](https://arxiv.org/abs/2209.11799) extended to images. Patch embeddings are meant to be extracted with the [`google/vit-base-patch16-224` DINO checkpoint](https://huggingface.co/google/vit-base-patch16-224).
+
+## Intended uses & limitations
+
+This model is not intended to be used in production.
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter    | Value                                                     |
+|-------------------|-----------------------------------------------------------|
+| Cs                | 10                                                        |
+| class_weight      |                                                           |
+| cv                | StratifiedKFold(n_splits=5, random_state=1, shuffle=True) |
+| dual              | False                                                     |
+| fit_intercept     | True                                                      |
+| intercept_scaling | 1.0                                                       |
+| l1_ratios         |                                                           |
+| max_iter          | 100                                                       |
+| multi_class       | auto                                                      |
+| n_jobs            |                                                           |
+| penalty           | l2                                                        |
+| random_state      | 1                                                         |
+| refit             | False                                                     |
+| scoring           |                                                           |
+| solver            | lbfgs                                                     |
+| tol               | 0.0001                                                    |
+| verbose           | 0                                                         |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e {color: black;background-color: white;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e pre{padding: 0;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-toggleable {background-color: white;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-estimator:hover {background-color: #d4ebff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-item {z-index: 1;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-parallel-item:only-child::after {width: 0;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-57980b4f-6828-4a54-ae50-b50e1f9f097e div.sk-text-repr-fallback {display: none;}</style><div id="sk-57980b4f-6828-4a54-ae50-b50e1f9f097e" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="51ec5e46-9aaa-4487-adda-6718142c9f85" type="checkbox" checked><label for="51ec5e46-9aaa-4487-adda-6718142c9f85" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegressionCV</label><div class="sk-toggleable__content"><pre>LogisticRegressionCV(cv=StratifiedKFold(n_splits=5, random_state=1, shuffle=True),random_state=1, refit=False)</pre></div></div></div></div></div>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric   |   Value |
+|----------|---------|
+| accuracy | 0.99465 |
+| f1 score | 0.99465 |
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+
+from PIL import Image
+from skops import hub_utils
+import torch
+from transformers import AutoFeatureExtractor, AutoModel
+import pickle
+import os
+
+# load embedding model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModel.from_pretrained("google/vit-base-patch16-224").eval().to(device)
+
+# load logistic regression
+os.mkdir("emb-gam-vit")
+hub_utils.download(repo_id="Ramos-Ramos/emb-gam-vit", dst="emb-gam-vit")
+
+with open("emb-gam-vit/model.pkl", "rb") as file: 
+  logistic_regression = pickle.load(file)
+    
+# load image
+img = Image.open("examples/english_springer.png")
+
+# preprocess image
+inputs = {k: v.to(device) for k, v in feature_extractor(img, return_tensors='pt').items()}
+
+# extract patch embeddings
+with torch.no_grad():
+  patch_embeddings = model(**inputs).last_hidden_state[0, 1:].cpu()
+
+# classify
+pred = logistic_regression.predict(patch_embeddings.sum(dim=0, keepdim=True))
+
+# get patch contributions
+patch_contributions = logistic_regression.coef_ @ patch_embeddings.T.numpy()
+```
+
+</details>
+
+
+
+
+# Model Card Authors
+
+This model card is written by following authors:
+
+Patrick Ramos and Ryan Ramos
+
+# Model Card Contact
+
+You can contact the model card authors through following channels:
+[More Information Needed]
+
+# Citation
+
+Below you can find information related to citation.
+
+**BibTeX:**
+```
+@article{singh2022emb,
+  title={Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models},
+  author={Singh, Chandan and Gao, Jianfeng},
+  journal={arXiv preprint arXiv:2209.11799},
+  year={2022}
+}
+```
+
+
+# Additional Content
+
+## confusion_matrix
+
+![confusion_matrix](confusion_matrix.png)

model.pkl

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