Instructions to use mrm8488/bert-tiny-4-finetuned-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bert-tiny-4-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-tiny-4-finetuned-squadv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-tiny-4-finetuned-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-tiny-4-finetuned-squadv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mrm8488/bert-tiny-4-finetuned-squadv2: direct link, hf CLI and curl.
- Browser
- Download file 23.8 MB
-
https://huggingface.co/mrm8488/bert-tiny-4-finetuned-squadv2/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mrm8488/bert-tiny-4-finetuned-squadv2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mrm8488/bert-tiny-4-finetuned-squadv2/resolve/main/flax_model.msgpack
23.8 MB
- Xet hash:
- c63556fdebdf8681ecad49a4bdba384d5d52987b9d1d3b21e503385fa4263bf2
- Size of remote file:
- 23.8 MB
- SHA256:
- 5ec2d5788e4606844f6c0b5be230d675dc59a668d7048edd8a8d8cddfb3f9e73
路
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