Instructions to use WaiLwin/roberta-risk-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WaiLwin/roberta-risk-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="WaiLwin/roberta-risk-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("WaiLwin/roberta-risk-model") model = AutoModelForSequenceClassification.from_pretrained("WaiLwin/roberta-risk-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from WaiLwin/roberta-risk-model: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/WaiLwin/roberta-risk-model/resolve/aeef2d6a0c931b3e5ed3b203a1d8fb09527a09cd/model.safetensors
- Command line
-
hf download hf://WaiLwin/roberta-risk-model@aeef2d6a0c931b3e5ed3b203a1d8fb09527a09cd/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/WaiLwin/roberta-risk-model/resolve/aeef2d6a0c931b3e5ed3b203a1d8fb09527a09cd/model.safetensors
499 MB
- Xet hash:
- 9406961f654f7709aa8117d43d4a215623fe7e7f94d91d71750e7a9267b29dd2
- Size of remote file:
- 499 MB
- SHA256:
- 4f24606f53f87af845e25e0a7c3facac40bbaef946bd0c506718e68058c868d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.