Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+base_model: google/vit-base-patch16-224-in21k
+license: apache-2.0
+model-index:
+- name: vit-trash
+  results: []
+datasets:
+- garythung/trashnet
+metrics:
+- recall
+- precision
+- accuracy
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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+# vit-trash
+
+This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an [garythung/trashnet](https://huggingface.co/datasets/garythung/trashnet) dataset.
+
+## Training and evaluation data
+
+```json
+DatasetDict({
+    train: Dataset({
+        features: ['image', 'label'],
+        num_rows: 3537
+    })
+    validation: Dataset({
+        features: ['image', 'label'],
+        num_rows: 758
+    })
+    test: Dataset({
+        features: ['image', 'label'],
+        num_rows: 759
+    })
+})
+```
+### Training hyperparameters
+```json
+TrainingArguments(
+    output_dir="output-models",
+    save_total_limit=2,
+    report_to="wandb",
+    save_strategy="epoch",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=15,
+    per_device_eval_batch_size=10,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    load_best_model_at_end=True,
+    logging_dir="logs",
+    remove_unused_columns=False,
+)
+```
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.41.0
+- TensorFlow 2.15.0
+- Datasets 2.19.1
+- Tokenizers 0.19.1