Instructions to use mrm8488/spanbert-finetuned-squadv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/spanbert-finetuned-squadv1 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/spanbert-finetuned-squadv1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/spanbert-finetuned-squadv1") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/spanbert-finetuned-squadv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from mrm8488/spanbert-finetuned-squadv1: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/mrm8488/spanbert-finetuned-squadv1/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://mrm8488/spanbert-finetuned-squadv1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/mrm8488/spanbert-finetuned-squadv1/resolve/main/flax_model.msgpack
431 MB
- Xet hash:
- b164ee13dcc5bea35554b7c8154b79b24bb78a7b287d55233f7761204d390021
- Size of remote file:
- 431 MB
- SHA256:
- 6e6d1e9bb73b00c9238a9eabdb5f094450cf47ff980691696b3dc9c94780b7f8
路
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