Instructions to use AshutoshAdhikari/rsclf-scibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AshutoshAdhikari/rsclf-scibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AshutoshAdhikari/rsclf-scibert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AshutoshAdhikari/rsclf-scibert") model = AutoModelForSequenceClassification.from_pretrained("AshutoshAdhikari/rsclf-scibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 196b35f44a0406aef5b33a14d50c525a894cd8b676a44251ff167499bf80b417
- Size of remote file:
- 440 MB
- SHA256:
- e5b2f8cfdf44a98127e9971b5271398f744082740a9b5823c212a1c2a7c113fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.