Instructions to use tkarr/distilbert-base-uncased-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkarr/distilbert-base-uncased-squad with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="tkarr/distilbert-base-uncased-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("tkarr/distilbert-base-uncased-squad") model = AutoModelForQuestionAnswering.from_pretrained("tkarr/distilbert-base-uncased-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from tkarr/distilbert-base-uncased-squad: direct link, hf CLI and curl.
- Browser
- Download file 531 MB
-
https://huggingface.co/tkarr/distilbert-base-uncased-squad/resolve/main/optimizer.pt
- Command line
-
hf download hf://tkarr/distilbert-base-uncased-squad/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/tkarr/distilbert-base-uncased-squad/resolve/main/optimizer.pt
531 MB
- Xet hash:
- 7cf09840e8b51f9c51bde8e68bb8944e5df72ed28e5c8966bf417da8d17e2ef7
- Size of remote file:
- 531 MB
- SHA256:
- 8fb3623b451efe037fe372266cd4c25a7a9eee0c13f2fa490744cc3d9eb14aec
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.