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
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Model Description
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]
- Repository: [no]
- Paper [optional]: [no]
- Demo [optional]: [no]
Uses
Tento model môže byť použitý na konverzačné AI aplikácie, učenie jazykov, automatizáciu zákazníckych služieb a podobne.
Direct Use
[Learn german]
Downstream Use [optional]
[try]
Out-of-Scope Use
[no]
Bias, Risks, and Limitations
[no risk]
Recommendations
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
[kniha nemčina pre samoukov]
Training Procedure
Skúška
Preprocessing [optional]
[book]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[co to je]
Evaluation
co to je
Testing Data, Factors & Metrics
Testing Data
[co to je]
Factors
[nerozumiem ti]
Metrics
[ako na to]
Results
[ idem skusit]
Summary
Model Examination [optional]
[uz to pusti]
Environmental Impact
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]
BibTeX:
[neviem]
APA:
[neviem]
Glossary [optional]
[neviem]
More Information [optional]
[kolko este]
Model Card Authors [optional]
[dobre ]
Model Card Contact
[koniec]