Instructions to use xuantsh/distilroberta-base-Mark_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xuantsh/distilroberta-base-Mark_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xuantsh/distilroberta-base-Mark_example")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xuantsh/distilroberta-base-Mark_example") model = AutoModelForMaskedLM.from_pretrained("xuantsh/distilroberta-base-Mark_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xuantsh/distilroberta-base-Mark_example: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/xuantsh/distilroberta-base-Mark_example/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xuantsh/distilroberta-base-Mark_example/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xuantsh/distilroberta-base-Mark_example/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- f9f3819b02b50c3c6eef99cac2f2ec8a8b7a1024f1dda1b7cfb26864c8dc65c9
- Size of remote file:
- 329 MB
- SHA256:
- 76cb31b54de6b7cbbe9168870ea0ab7a67c80a95c57160fb1be421dfd1441108
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.