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 pytorch_model.bin from YanJiangJerry/roberta-large-e2-noweight: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/YanJiangJerry/roberta-large-e2-noweight/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YanJiangJerry/roberta-large-e2-noweight/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YanJiangJerry/roberta-large-e2-noweight/resolve/main/pytorch_model.bin
1.42 GB
- Xet hash:
- be651959c3401001f2c03a9849e4e17fcd1837058a243bbd44daa68c2aed765a
- Size of remote file:
- 1.42 GB
- SHA256:
- e76da5e7d0355afd7caf89f1617b59c6eefbd013938a157ffb2a0660ef4f6cc4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.