Transformers
PyTorch
Safetensors
English
t5
text2text-generation
text2text generation
text-generation-inference
Instructions to use haining/scientific_abstract_simplification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haining/scientific_abstract_simplification with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("haining/scientific_abstract_simplification") model = AutoModelForSeq2SeqLM.from_pretrained("haining/scientific_abstract_simplification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a40feedab4a9ac6a7e23aa4464220c05b53481c420d95e14ecc24ea28bd882a
- Size of remote file:
- 3.13 GB
- SHA256:
- a7b0673cdde01c1fcffa9166399ee985828c7553efad5a7fc27c80fcf7997df1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.