Instructions to use deepset/minilm-uncased-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/minilm-uncased-squad2 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="deepset/minilm-uncased-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/minilm-uncased-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/minilm-uncased-squad2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from deepset/minilm-uncased-squad2: direct link, hf CLI and curl.
- Browser
- Download file 133 MB
-
https://huggingface.co/deepset/minilm-uncased-squad2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://deepset/minilm-uncased-squad2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/deepset/minilm-uncased-squad2/resolve/main/flax_model.msgpack
133 MB
- Xet hash:
- ad077e80afef4cf1c94d8b29b3bf483d09277baaf7a8781533ae5352bd5839f5
- Size of remote file:
- 133 MB
- SHA256:
- bcd4d6e16c9e843b51a977870a5e2c2dd038da46f686e005804e45c4999165f2
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.