Instructions to use uhhlt/roberta-binary-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uhhlt/roberta-binary-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="uhhlt/roberta-binary-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("uhhlt/roberta-binary-classifier") model = AutoModelForSequenceClassification.from_pretrained("uhhlt/roberta-binary-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from uhhlt/roberta-binary-classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/uhhlt/roberta-binary-classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://uhhlt/roberta-binary-classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/uhhlt/roberta-binary-classifier/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- 73b65f6fb3557d0db15c9b4025bf03371d42a891faac857a9b9727c0604a3333
- Size of remote file:
- 1.42 GB
- SHA256:
- bdd34d1112b792cd97d28ec90b26932986f69ab331dfb2388c5b4e748bc18921
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