Instructions to use YanJiangJerry/roberta-large-e2-noweight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YanJiangJerry/roberta-large-e2-noweight with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YanJiangJerry/roberta-large-e2-noweight")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YanJiangJerry/roberta-large-e2-noweight") model = AutoModelForSequenceClassification.from_pretrained("YanJiangJerry/roberta-large-e2-noweight", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from YanJiangJerry/roberta-large-e2-noweight: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/YanJiangJerry/roberta-large-e2-noweight/resolve/main/training_args.bin
- Command line
-
hf download hf://YanJiangJerry/roberta-large-e2-noweight/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/YanJiangJerry/roberta-large-e2-noweight/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- c9eeb6b11d23782f428c14eb26eb96e0d209f24f069665639bbb92675965de16
- Size of remote file:
- 4.03 kB
- SHA256:
- 541eeb80d4783496d4bedfbd90669177a20bd572f601d1fd997fafe511475e91
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.