Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
English
t5
text2text-generation
token-classification
question-answering
text-generation
Instructions to use razent/SciFive-base-PMC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-base-PMC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="razent/SciFive-base-PMC")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-base-PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-base-PMC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 008fd4b5b81b47d7de9353a80d0f426fa294692a89d2d085bb660c8c4a397ef5
- Size of remote file:
- 892 MB
- SHA256:
- b116e83d36db3fc6ab1f3e55fcf076ed95ca374084e7595301ad4a2e6cfeb0bd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.