Instructions to use hz53/roberta-base-boolq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hz53/roberta-base-boolq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hz53/roberta-base-boolq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hz53/roberta-base-boolq") model = AutoModelForSequenceClassification.from_pretrained("hz53/roberta-base-boolq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hz53/roberta-base-boolq: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/hz53/roberta-base-boolq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hz53/roberta-base-boolq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hz53/roberta-base-boolq/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 4b9dc409410c019b889200acd291399231e5a8192a2c26863bf438da5e12f192
- Size of remote file:
- 499 MB
- SHA256:
- c23efde48bbc5e0d7269b13d6ae08fe4dbbb92c0aff8680e0b05e828ee38927c
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