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  Bert-base-german-cased model fine-tuned on a corpus of German-Swiss.
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- # Table of Contents
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- - [Model Card for Bert-base-german-cased-finetuned-swiss](#model-card-for--model_id-)
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- - [Table of Contents](#table-of-contents)
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- - [Table of Contents](#table-of-contents-1)
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- - [Model Details](#model-details)
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- - [Model Description](#model-description)
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- - [Uses](#uses)
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- - [Direct Use](#direct-use)
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- - [Downstream Use [Optional]](#downstream-use-optional)
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- - [Out-of-Scope Use](#out-of-scope-use)
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- - [Bias, Risks, and Limitations](#bias-risks-and-limitations)
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- - [Recommendations](#recommendations)
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- - [Training Details](#training-details)
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- - [Training Data](#training-data)
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- - [Training Procedure](#training-procedure)
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- - [Preprocessing](#preprocessing)
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- - [Speeds, Sizes, Times](#speeds-sizes-times)
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- - [Evaluation](#evaluation)
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- - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
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- - [Testing Data](#testing-data)
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- - [Factors](#factors)
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- - [Metrics](#metrics)
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- - [Results](#results)
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- - [Model Examination](#model-examination)
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- - [Environmental Impact](#environmental-impact)
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- - [Technical Specifications [optional]](#technical-specifications-optional)
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- - [Model Architecture and Objective](#model-architecture-and-objective)
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- - [Compute Infrastructure](#compute-infrastructure)
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- - [Hardware](#hardware)
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- - [Software](#software)
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- - [Citation](#citation)
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- - [Glossary [optional]](#glossary-optional)
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- - [More Information [optional]](#more-information-optional)
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- - [Model Card Authors [optional]](#model-card-authors-optional)
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- - [Model Card Contact](#model-card-contact)
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- - [How to Get Started with the Model](#how-to-get-started-with-the-model)
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  # Model Details
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  Bert-base-german-cased model fine-tuned on a corpus of German-Swiss.
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  Fine-tuning was done on the Swiss German data of the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/) and [SwissCrawl](https://icosys.ch/swisscrawl). For testing purposes, the model was evaluated on the [Swiss Dialect Classification dataset](https://huggingface.co/datasets/statworx/swiss-dialects) as down-stream task.
 
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- - **Developed by:** More information needed
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- - **Shared by [Optional]:** More information needed
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  - **Model type:** Language model
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  - **Language(s) (NLP):** de
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  - **License:** apache-2.0
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- - **Parent Model:** More information needed
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- - **Resources for more information:** 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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- ## 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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- <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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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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- <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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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- <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
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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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- Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
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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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- # Training Details
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- ## Training Data
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- <!-- This should link to a Data 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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- https://huggingface.co/datasets/statworx/leipzip-swiss,
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- https://icosys.ch/swisscrawl
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- ## Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- ### Preprocessing
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- More information needed
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- ### Speeds, Sizes, Times
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- More information needed
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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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- <!-- This should link to a Data Card if possible. -->
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- More information needed
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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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- More information needed
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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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- # Model Examination
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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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- More information needed
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- ## Compute Infrastructure
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- ### Hardware
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- ### Software
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- # Citation
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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 needed
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- # More Information [optional]
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- More information needed
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- # Model Card Authors [optional]
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- <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
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- F, a, b, i, a, n, , M, ü, l, l, e, r
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- # Model Card Contact
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- More information needed
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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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- <details>
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- <summary> Click to expand </summary>
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- # ⚠️ Type of model unknown
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- </details>
 
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  Bert-base-german-cased model fine-tuned on a corpus of German-Swiss.
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  # Model Details
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  Bert-base-german-cased model fine-tuned on a corpus of German-Swiss.
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  Fine-tuning was done on the Swiss German data of the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/) and [SwissCrawl](https://icosys.ch/swisscrawl). For testing purposes, the model was evaluated on the [Swiss Dialect Classification dataset](https://huggingface.co/datasets/statworx/swiss-dialects) as down-stream task.
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+ It outperformed its parent model ([bert-base-german-cased](https://huggingface.co/bert-base-german-cased)) by approx. 5% accuracy.
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+ - **Developed by:** Fabian Müller
 
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  - **Model type:** Language model
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  - **Language(s) (NLP):** de
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  - **License:** apache-2.0
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+ - **Parent Model:** [bert-base-german-cased](https://huggingface.co/bert-base-german-cased)