Instructions to use zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification") model = AutoModelForSequenceClassification.from_pretrained("zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/zhuqing/roberta-base-uncased-AutoModelWithLMHeadnetmums-classification/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- c8eb7812e00dd65fe5c705a1357aad654fbac219754b441494219f10d23bcf6d
- Size of remote file:
- 499 MB
- SHA256:
- 684231466193822b069d107117f7f2090545a149c9cf097a9126f1a142b025e6
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