DEPLAIN: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification
Paper • 2305.18939 • Published
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card for mT5-small-VT_deplain-apa
Finetuned mT5-Model for German sentence-level text-simplification.
Model Details
Model Description
- Model type: Encoder-Decoder-Transformer
- Language(s) (NLP): German
- Finetuned from model: google/mT5-small
- Task: Text-Simplification
Training Details
Training Data
DEplain/DEplain-APA-sent Stodden et al. (2023):arXiv:2305.18939
Training Procedure
Parameter-efficient Fine-Tuning with LoRA.
Vocabulary adjusted through Vocabulary Transfer (Mosin et al. (2021): arXiv:2112.14569).
Training Hyperparameters
- Batch Size: 16
- Epochs: 1
- Learning Rate: 0.001
- Optimizer: Adafactor
LoRA Hyperparameters
- R: 32
- Alpha: 64
- Dropout: 0.1
- Target modules: all linear layers