Instructions to use dbernsohn/roberta-javascript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbernsohn/roberta-javascript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbernsohn/roberta-javascript")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-javascript") model = AutoModelForMaskedLM.from_pretrained("dbernsohn/roberta-javascript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from dbernsohn/roberta-javascript: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/dbernsohn/roberta-javascript/resolve/refs%2Fpr%2F2/model.safetensors
- Command line
-
hf download hf://dbernsohn/roberta-javascript@refs/pr/2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dbernsohn/roberta-javascript/resolve/refs%2Fpr%2F2/model.safetensors
334 MB
- Xet hash:
- 49d5588110181ab4d1066fe1f949f9e0cdb134dda061cc7924163fa03e45740f
- Size of remote file:
- 334 MB
- SHA256:
- f1fdc7a00a938a155a6bebdd176be38d5aba15e31676f7fcfa49b597a4d9fc2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.