Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-large-Pubmed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-large-Pubmed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-large-Pubmed")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-Pubmed") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from razent/SciFive-large-Pubmed: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/razent/SciFive-large-Pubmed/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://razent/SciFive-large-Pubmed/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/razent/SciFive-large-Pubmed/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- 41bf118fefad06f75662e64136d159866df07da30e7c805e26002644f94df2fe
- Size of remote file:
- 2.95 GB
- SHA256:
- 40ae46e7c96c956107b296af64ff9eccfc5752d1c77deb2d4c004601a1d4df12
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