LukasKorvas
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Update README.md
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README.md
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from adapters import AutoAdapterModel
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model_name = "dbmdz/bert-base-german-cased"
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model.load_adapter("LukasKorvas/German", set_active=True)---
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license: apache-2.0
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datasets:
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [
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- **Funded by [optional]:** [
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- **Shared by [optional]:** [
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- **Model type:** [
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- **Language(s) (NLP):** [
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- **License:** [
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- **Finetuned from model [optional]:** [
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [
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- **Paper [optional]:** [
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- **Demo [optional]:** [
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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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[
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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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[
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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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[
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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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[
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### Recommendations
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Use the code below to get started with the model.
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## Training Details
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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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[
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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 [optional]
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[
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[
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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 Dataset Card if possible. -->
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[
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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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[
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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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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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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:** [
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- **Hours used:** [
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- **Cloud Provider:** [
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- **Compute Region:** [
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- **Carbon Emitted:** [
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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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**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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from adapters import AutoAdapterModel
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model_name = "dbmdz/bert-base-german-cased"
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model = AutoAdapterModel.from_pretrained(model_name)
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model.load_adapter("LukasKorvas/German", set_active=True)---
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license: apache-2.0
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datasets:
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<!-- Provide a longer summary of what this model is. -->
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Tento model je prispôsobený pre úlohy spracovania prirodzeného jazyka v nemčine, ako je klasifikácia textu a generovanie konverzačného obsahu.
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- **Developed by:** [Lukas]
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- **Funded by [optional]:** [Korvas]
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- **Shared by [optional]:** [Nemčina pre Samoukov]
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- **Model type:** [text a video]
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- **Language(s) (NLP):** [Slovak, German]
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- **License:** [no]
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- **Finetuned from model [optional]:** [no]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [no]
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- **Paper [optional]:** [no]
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- **Demo [optional]:** [no]
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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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Tento model môže byť použitý na konverzačné AI aplikácie, učenie jazykov, automatizáciu zákazníckych služieb a podobne.
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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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[Learn german]
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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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[try]
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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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[no]
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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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[no risk]
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### Recommendations
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Use the code below to get started with the model.
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[zaklady nemeckého jazyka]
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## Training Details
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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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[kniha nemčina pre samoukov]
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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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Skúška
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#### Preprocessing [optional]
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[book]
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[co to je]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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co to je
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[co to je]
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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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[nerozumiem ti]
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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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[ako na to]
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### Results
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[ idem skusit]
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#### Summary
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<!-- Relevant interpretability work for the model goes here -->
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[uz to pusti]
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## Environmental Impact
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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:** [neviem]
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- **Hours used:** [dve]
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- **Cloud Provider:** [ano]
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- **Compute Region:** [neviem]
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- **Carbon Emitted:** [asi]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[neviem]
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### Compute Infrastructure
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[neviem]
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#### Hardware
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[neviem]
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#### Software
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[neviem]
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## Citation [optional]
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**BibTeX:**
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[neviem]
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**APA:**
|
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[neviem]
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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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[neviem]
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## More Information [optional]
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[kolko este]
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## Model Card Authors [optional]
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[dobre ]
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## Model Card Contact
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[koniec]
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