Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-base-Pubmed_PMC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-base-Pubmed_PMC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-base-Pubmed_PMC")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-Pubmed_PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-Pubmed_PMC", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from razent/SciFive-base-Pubmed_PMC: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/razent/SciFive-base-Pubmed_PMC/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://razent/SciFive-base-Pubmed_PMC/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/razent/SciFive-base-Pubmed_PMC/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 839e1adac5587ebb404ff0014be79a5bfa30e1b814d34952e2fa95b22daaa733
- Size of remote file:
- 892 MB
- SHA256:
- f9057f257b3e232e5abf26f86da51afa9aacc416945d7def2ab128766e355060
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