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@@ -17,7 +17,6 @@ This modelcard aims to be a base template for new models. It has been generated
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  <!-- Provide a longer summary of what this model is. -->
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  <!-- - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
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  - **License:** [More Information Needed] -->
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  - **Finetuned from model :** google-bert/bert-base-uncased
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- <!-- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- <!-- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed] --> -->
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  ## Uses
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  <!-- 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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  This model classifies food recipe or ingredient into three disctinct categories: Vegan, Vegetarian and Non-vegetarian
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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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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- <!-- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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  ## Bias, Risks, and Limitations
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  <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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  1. Download model.
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  2. Run testing script.
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  ## Training Details
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  ### Training Data
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  1. Training on mlm and nsp dataset combined
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  2. Fine-tuning on synthetic dataset for recipe classification
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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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- <!-- This should link to a Dataset Card if possible. -->
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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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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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  ### Results
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  {'accuracy': 0.9166666666666666, 'precision': 0.9333333333333332, 'recall': 0.9166666666666666, 'f1_score': 0.9153439153439153}
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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 -->
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- <!-- 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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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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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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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- <!-- **BibTeX:** --> -->
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  <!-- Provide a longer summary of what this model is. -->
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  <!-- - **Developed by:** [More Information Needed]
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  - **Funded by [optional]:** [More Information Needed]
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  - **Shared by [optional]:** [More Information Needed]
 
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  - **License:** [More Information Needed] -->
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  - **Finetuned from model :** google-bert/bert-base-uncased
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  ## Uses
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  <!-- 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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  This model classifies food recipe or ingredient into three disctinct categories: Vegan, Vegetarian and Non-vegetarian
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  ## Bias, Risks, and Limitations
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  <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
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  1. Download model.
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  2. Run testing script.
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  ## Training Details
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  ### Training Data
 
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  1. Training on mlm and nsp dataset combined
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  2. Fine-tuning on synthetic dataset for recipe classification
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  ### Results
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  <!-- [More Information Needed] -->
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  {'accuracy': 0.9166666666666666, 'precision': 0.9333333333333332, 'recall': 0.9166666666666666, 'f1_score': 0.9153439153439153}