Text Classification
Transformers
PyTorch
TensorFlow
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-base-Pubmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-base-Pubmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-base-Pubmed")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-Pubmed") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-Pubmed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from razent/SciFive-base-Pubmed: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/razent/SciFive-base-Pubmed/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://razent/SciFive-base-Pubmed/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/razent/SciFive-base-Pubmed/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- a714ee567a4e0d3989298a6bffa0750132018ab8e36d146f818b05b987462b15
- Size of remote file:
- 892 MB
- SHA256:
- 9ed7461e225892b86b4e7e5db941dfcf784435cca1de947453b44e47662d84a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.