Instructions to use YakovElm/IntelDAOS_RoBERTa_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS_RoBERTa_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS_RoBERTa_5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS_RoBERTa_5") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS_RoBERTa_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/IntelDAOS_RoBERTa_5: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/IntelDAOS_RoBERTa_5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/IntelDAOS_RoBERTa_5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/IntelDAOS_RoBERTa_5/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 010a54b627d30de2defda8433b4c47e155e648d31a9585f54af1c2b88e3dc33a
- Size of remote file:
- 499 MB
- SHA256:
- c27ede996fa3589e2e8f981c50bd988d0bd7e8a1f0156b59fb39459d0c6b6267
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