Instructions to use Drigoro/roberta_normal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/roberta_normal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Drigoro/roberta_normal")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Drigoro/roberta_normal") model = AutoModelForSequenceClassification.from_pretrained("Drigoro/roberta_normal", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Drigoro/roberta_normal: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/Drigoro/roberta_normal/resolve/main/training_args.bin
- Command line
-
hf download hf://Drigoro/roberta_normal/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Drigoro/roberta_normal/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 8f826b32bce499d1d274f69db216f19515964d5cc238b00fb7bf60d3f4594771
- Size of remote file:
- 5.11 kB
- SHA256:
- 7999447aa8da528457e63aab013d35fd1b4461922288fc3c004ee5f52678fc3a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.