Instructions to use zharry29/goal_benchmark_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zharry29/goal_benchmark_roberta with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("zharry29/goal_benchmark_roberta") model = AutoModelForMultipleChoice.from_pretrained("zharry29/goal_benchmark_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from zharry29/goal_benchmark_roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/zharry29/goal_benchmark_roberta/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://zharry29/goal_benchmark_roberta/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/zharry29/goal_benchmark_roberta/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 4b79eb49ed4d4d0638b4fa0844b6b85cee488623ecec5cc569418888260f08b1
- Size of remote file:
- 499 MB
- SHA256:
- 470f9fdba0a0ad262a6b8f6038deec3e9d32d609e0c0f9c3ff109ae9e81f219e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.