Instructions to use ji-xin/roberta_base-MNLI-two_stage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ji-xin/roberta_base-MNLI-two_stage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ji-xin/roberta_base-MNLI-two_stage")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("ji-xin/roberta_base-MNLI-two_stage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ji-xin/roberta_base-MNLI-two_stage: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
-
https://huggingface.co/ji-xin/roberta_base-MNLI-two_stage/resolve/main/training_args.bin
- Command line
-
hf download hf://ji-xin/roberta_base-MNLI-two_stage/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/ji-xin/roberta_base-MNLI-two_stage/resolve/main/training_args.bin
1.43 kB
- Xet hash:
- 1d0fe22097efd6170694b9a0072e1911d0608a3a9cff2790fb081cd51b1f1077
- Size of remote file:
- 1.43 kB
- SHA256:
- d6ef8d69070041e7d4011b6dfa76751fd7ce9caa32033b3bf06c3fe9c2f90f1b
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