README.md

---
license: apache-2.0
datasets:
- openai/MMMLU
language:
- aa
- ae
- ak
- as
metrics:
- accuracy
base_model:
- openai/whisper-large-v3-turbo
new_version: meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-classification
library_name: adapter-transformers
tags:
- not-for-all-audiences
---
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

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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]

<!-- Provide the basic links for the model. -->

- **Repository:** [no]
- **Paper [optional]:** [no]
- **Demo [optional]:** [no]

## 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. -->

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

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[Learn german]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[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

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[no risk]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- 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. -->

[kniha nemčina pre samoukov]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

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

<!-- This section describes the evaluation protocols and provides the results. -->
 co to je 
### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[co to je]

#### Factors

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

[nerozumiem ti]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[ako na to]

### Results

[ idem skusit]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[neviem]

**APA:**

[neviem]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[neviem]

## More Information [optional]

[kolko este]

## Model Card Authors [optional]

[dobre ]

## Model Card Contact

[koniec]