Instructions to use edmihranyan/roberta_large_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edmihranyan/roberta_large_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edmihranyan/roberta_large_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edmihranyan/roberta_large_classifier") model = AutoModelForSequenceClassification.from_pretrained("edmihranyan/roberta_large_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from edmihranyan/roberta_large_classifier: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/edmihranyan/roberta_large_classifier/resolve/main/training_args.bin
- Command line
-
hf download hf://edmihranyan/roberta_large_classifier/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/edmihranyan/roberta_large_classifier/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- 901bd0fac77ea70d54a4b5bc9ad07d7c93d2c900593b315bf5941f7a7f539cfa
- Size of remote file:
- 2.93 kB
- SHA256:
- 1b9ba8c536963d6413dd7420a8fda692af28877f999850a54d222ada60e759c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.