German

from adapters import AutoAdapterModel

model_name = "dbmdz/bert-base-german-cased" model = AutoAdapterModel.from_pretrained(model_name) model.load_adapter("LukasKorvas/German", set_active=True)--- license: apache-2.0 datasets:

• openai/MMMLU language:
• af metrics:
• accuracy base_model:
• openai/whisper-large-v3-turbo new_version: meta-llama/Llama-3.1-8B-Instruct library_name: adapter-transformers

---

Model Card for Model ID

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This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

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Tento model je prispôsobený pre úlohy spracovania prirodzeného jazyka v nemčine, ako je klasifikácia textu a generovanie konverzačného obsahu.

• Developed by: [Lukas]
• Funded by [optional]: [Korvas]
• Shared by [optional]: [Nemčina pre Samoukov]
• Model type: [text a video]
• Language(s) (NLP): [Slovak, German]
• License: [no]
• Finetuned from model [optional]: [no]

Model Sources [optional]

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• Repository: [no]
• Paper [optional]: [no]
• Demo [optional]: [no]

Uses

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Tento model môže byť použitý na konverzačné AI aplikácie, učenie jazykov, automatizáciu zákazníckych služieb a podobne.

Direct Use

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[Learn german]

Downstream Use [optional]

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[try]

Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[no]

Bias, Risks, and Limitations

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[no risk]

Recommendations

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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.

How to Get Started with the Model

Use the code below to get started with the model.

[zaklady nemeckého jazyka]

Training Details

Training Data

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[kniha nemčina pre samoukov]

Training Procedure

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Skúška

Preprocessing [optional]

[book]

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[co to je]

Evaluation

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co to je

Testing Data, Factors & Metrics

Testing Data

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[co to je]

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[nerozumiem ti]

Metrics

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[ako na to]

Results

[ idem skusit]

Summary

Model Examination [optional]

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[uz to pusti]

Environmental Impact

<!-- 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 presented in Lacoste et al. (2019).

• Hardware Type: [neviem]
• Hours used: [dve]
• Cloud Provider: [ano]
• Compute Region: [neviem]
• Carbon Emitted: [asi]

Technical Specifications [optional]

Model Architecture and Objective

[neviem]

Compute Infrastructure

[neviem]

Hardware

[neviem]

Software

[neviem]

Citation [optional]

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BibTeX:

[neviem]

APA:

[neviem]

Glossary [optional]

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[neviem]

More Information [optional]

[kolko este]

Model Card Authors [optional]

[dobre ]

Model Card Contact

[koniec]