Instructions to use zhuqing/roberta-base-uncased-netmums-classification-intersection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhuqing/roberta-base-uncased-netmums-classification-intersection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zhuqing/roberta-base-uncased-netmums-classification-intersection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zhuqing/roberta-base-uncased-netmums-classification-intersection") model = AutoModelForSequenceClassification.from_pretrained("zhuqing/roberta-base-uncased-netmums-classification-intersection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zhuqing/roberta-base-uncased-netmums-classification-intersection: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/zhuqing/roberta-base-uncased-netmums-classification-intersection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zhuqing/roberta-base-uncased-netmums-classification-intersection/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/zhuqing/roberta-base-uncased-netmums-classification-intersection/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 1f474e509d91311984b0a09953f4b1f807458ef214a0bd80bddcf00949e8d8ee
- Size of remote file:
- 499 MB
- SHA256:
- 1308d4d7a661e6b3a7893b09343680d074f9c96b84b16621fbcdb7f38cff1af9
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