Transformers
Safetensors
German
t5
text2text-generation
seq2seq
text-to-text
scientific-language-models
cross-lingual-transfer
wechsel
global-mmlu
text-generation-inference
Instructions to use rausch/de-t5-sci-transfer-init-spm32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rausch/de-t5-sci-transfer-init-spm32k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rausch/de-t5-sci-transfer-init-spm32k") model = AutoModelForSeq2SeqLM.from_pretrained("rausch/de-t5-sci-transfer-init-spm32k") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c896b82e5a6533a0fe2cc6dfe86669fecff31dee3fd5c5e517a601c4d81eb77
- Size of remote file:
- 769 kB
- SHA256:
- 5253e8d6227bb1f9d07d653985dd195d3a15d1e01b61f99fbb6b20fd2984d058
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