Instructions to use YakovElm/IntelDAOS_RoBERTa_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_RoBERTa_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_RoBERTa_10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_RoBERTa_10") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_RoBERTa_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/IntelDAOS_RoBERTa_10: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/IntelDAOS_RoBERTa_10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS_RoBERTa_10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS_RoBERTa_10/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 7adca15d4777f0c974a536027420728a50536fb75bf4f00cfe013e86cacdf5a7
- Size of remote file:
- 499 MB
- SHA256:
- 63a2e6160d0fef2fe836581b3e2c0ecbd9d4379fe0d25d83b370d733c87c1cd7
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