Instructions to use YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS_20_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- c8e4c4c369ac4c2948bc040a39064c94e46d6fb6939f2a34cffe292b969e95bb
- Size of remote file:
- 499 MB
- SHA256:
- 3fef8989f33e88ebe8a2b150ea4bc8c2ecbad987cfd6e46b1998c15d42ef6b86
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