Instructions to use mrm8488/bert-medium-finetuned-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert-medium-finetuned-squadv2 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="mrm8488/bert-medium-finetuned-squadv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-medium-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-medium-finetuned-squadv2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mrm8488/bert-medium-finetuned-squadv2: direct link, hf CLI and curl.
- Browser
- Download file 164 MB
-
https://huggingface.co/mrm8488/bert-medium-finetuned-squadv2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mrm8488/bert-medium-finetuned-squadv2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mrm8488/bert-medium-finetuned-squadv2/resolve/main/flax_model.msgpack
164 MB
- Xet hash:
- e116654136c38165c2d4d0582b47cdd3911dd8bd82dc34cf9dad44f61b6ab2e6
- Size of remote file:
- 164 MB
- SHA256:
- e0d7f167f4b81fae889031062c3927a619679e3cb7143262e60874dde0091a87
路
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