Instructions to use rushikeshwalode/MLM_rotten_tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rushikeshwalode/MLM_rotten_tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="rushikeshwalode/MLM_rotten_tomatoes")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("rushikeshwalode/MLM_rotten_tomatoes") model = AutoModelForMaskedLM.from_pretrained("rushikeshwalode/MLM_rotten_tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rushikeshwalode/MLM_rotten_tomatoes: direct link, hf CLI and curl.
- Browser
- Download file 5.71 kB
-
https://huggingface.co/rushikeshwalode/MLM_rotten_tomatoes/resolve/main/training_args.bin
- Command line
-
hf download hf://rushikeshwalode/MLM_rotten_tomatoes/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rushikeshwalode/MLM_rotten_tomatoes/resolve/main/training_args.bin
5.71 kB
- Xet hash:
- 44c0099ece7cb78b9ec457f707e4ec07b84c57b635453ee0b113c978ad3a781b
- Size of remote file:
- 5.71 kB
- SHA256:
- 9c4572429947c3fc2ffef9ae7927e902c3a42555fdf7f5c9f26b6ad553167f62
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