Instructions to use CPMs/cpm.in.roberta-base.approximate.seed77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CPMs/cpm.in.roberta-base.approximate.seed77 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForFactualCounterfactualSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CPMs/cpm.in.roberta-base.approximate.seed77") model = RobertaForFactualCounterfactualSequenceClassification.from_pretrained("CPMs/cpm.in.roberta-base.approximate.seed77", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CPMs/cpm.in.roberta-base.approximate.seed77: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/CPMs/cpm.in.roberta-base.approximate.seed77/resolve/main/training_args.bin
- Command line
-
hf download hf://CPMs/cpm.in.roberta-base.approximate.seed77/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/CPMs/cpm.in.roberta-base.approximate.seed77/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- ff63706a0d225c81628ca9fe31dbef9ed8d87076fd439af9bd0b568abdbdaa4a
- Size of remote file:
- 3.18 kB
- SHA256:
- 1673c25df66ab430cf5640069391befa925c7fce30623a5c929351ed4e140928
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