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README.md
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#
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{'per_device_train_batch_size': 16,
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'warmup_ratio': 0.1,
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'num_train_epochs': 1}
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{'lr': 0.001, 'eps': (1e-30, 0.001), 'clip_threshold': 1.0, 'decay_rate': 0.0, 'beta1': None, 'weight_decay': 0.0, 'scale_parameter': False, 'relative_step': False, 'warmup_init': False, 'differentiable': False}
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datasets:
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- DEplain/DEplain-APA-sent
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language:
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- de
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metrics:
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- sari
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- bleu
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- bertscore
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library_name: transformers
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base_model: google/mT5-large
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pipeline_tag: text2text-generation
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# Model Card for mT5-large-trimmed_deplain-apa
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Finetuned mT5-Model for German sentence-level text-simplification.
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## Model Details
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### Model Description
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- **Model type:** Encoder-Decoder-Transformer
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- **Language(s) (NLP):** German
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- **Finetuned from model:** google/mT5-small
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- **Task**: Text-Simplification
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## Training Details
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### Training Data
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[DEplain/DEplain-APA-sent](https://huggingface.co/datasets/DEplain/DEplain-APA-sent)
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### Training Procedure
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Parameter-efficient Fine-Tuning with LoRA
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#### Training Hyperparameters
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* Batch Size: 16
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* Epochs: 1
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* Learning Rate: 0,001
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* Optimizer: Adafactor
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#### LoRA Hyperparameters
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* R: 32
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* Alpha: 64
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* Dropout:
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* Target modules: all linear layers
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