Instructions to use shashank1303/bert-finetuned-squad-accelerate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shashank1303/bert-finetuned-squad-accelerate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="shashank1303/bert-finetuned-squad-accelerate")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("shashank1303/bert-finetuned-squad-accelerate") model = AutoModelForQuestionAnswering.from_pretrained("shashank1303/bert-finetuned-squad-accelerate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shashank1303/bert-finetuned-squad-accelerate: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/shashank1303/bert-finetuned-squad-accelerate/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shashank1303/bert-finetuned-squad-accelerate/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shashank1303/bert-finetuned-squad-accelerate/resolve/main/pytorch_model.bin
436 MB
- Xet hash:
- 46e4372857ae9838094b08d9984756d34502b4ee459adf7d0e5d6a55611591b0
- Size of remote file:
- 436 MB
- SHA256:
- 91c82eb4d890d4b2b1f251fdfcc5e3dd03ccdd411651a7252536a74818b249c6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.