Instructions to use deutsche-welle/t5_large_peft_wnc_debiaser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use deutsche-welle/t5_large_peft_wnc_debiaser with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("t5-large") model = PeftModel.from_pretrained(base_model, "deutsche-welle/t5_large_peft_wnc_debiaser") - Notebooks
- Google Colab
- Kaggle
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
The model is trained on Wiki Neutrality Corpus (WNC). Big kudos to the authors of WNC.
We uploaded an example Python script (main.py), you can refer it if you want to host it via FastAPI.
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Model tree for deutsche-welle/t5_large_peft_wnc_debiaser
Base model
google-t5/t5-large