Instructions to use clagator/biobert_squad2_cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clagator/biobert_squad2_cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="clagator/biobert_squad2_cased")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("clagator/biobert_squad2_cased") model = AutoModelForQuestionAnswering.from_pretrained("clagator/biobert_squad2_cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd4b4f5c24bf93fb9a3ab2124cb9c127ba648153fc75031a52fb2ae06e64f46f
- Size of remote file:
- 431 MB
- SHA256:
- 0f7f63d4d7a1c558d4194e79c814dfacf9095994a882b1c90efc424d054dec67
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.