Text Classification
Transformers
PyTorch
TensorFlow
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-large-Pubmed_PMC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-large-Pubmed_PMC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-large-Pubmed_PMC")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-Pubmed_PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3cae6420311c6a88219da08ec0912c7fc6e5d6c61e28d68e548c5eb89a24ded3
- Size of remote file:
- 2.95 GB
- SHA256:
- 3d133c77de6182dd8b741c434197acc738c5ce0079ce4c4eb583aae415e07d26
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