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  model-index:
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  - name: schlager-bot-004
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  results: []
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # schlager-bot-004
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- This model is a fine-tuned version of [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) on an unknown dataset.
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  ## Model description
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- More information needed
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  ## Intended uses & limitations
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- More information needed
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  ## Training and evaluation data
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- More information needed
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  ## Training procedure
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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  - lr_scheduler_warmup_ratio: 0.03
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  - num_epochs: 10
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- ### Training results
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-
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-
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  ### Framework versions
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  - Transformers 4.34.0
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  - Pytorch 2.1.0+cu118
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  - Datasets 2.13.0
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- - Tokenizers 0.14.1
 
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  model-index:
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  - name: schlager-bot-004
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  results: []
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+ license: llama2
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+ language:
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+ - de
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # schlager-bot-004
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+ This model is a fine-tuned version of [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b) on a dataset of 1048 schlager song lyrics. Schlager songs form a genre of German music and, therefore, the input should be in German as well to ensure best results.
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  ## Model description
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+ The model takes a verse (in German) and uses it to generate the text to a schlager song.
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  ## Intended uses & limitations
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+ This model is not intended for commercial use of any kind as the dataset used for fine-tuning contains propietary information.
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  ## Training and evaluation data
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+ The training and evaluation data was extracted from Spotify via an API.
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  ## Training procedure
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+ The model was fine-tuned using a Google colab notebook with a V100 GPU and High RAM. For details, please see the Github repository (https://github.com/NiclasFenton-Wiegleb/schlager-lyrics-bot/tree/main)
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
 
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  - lr_scheduler_warmup_ratio: 0.03
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  - num_epochs: 10
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  ### Framework versions
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  - Transformers 4.34.0
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  - Pytorch 2.1.0+cu118
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  - Datasets 2.13.0
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+ - Tokenizers 0.14.1