Text Classification
Transformers
PyTorch
TensorFlow
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-large-PMC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-large-PMC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-large-PMC")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-PMC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 038f13a65cabdc430d471fe14d67a19a07cea2ad701099dbf44110ea977012b6
- Size of remote file:
- 2.95 GB
- SHA256:
- e831da6f28f84df23e50386aee0d943d0aede385cb3295e772745afa68d57a5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.