Instructions to use YakovElm/Hyperledger_15_RoBERTa_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Hyperledger_15_RoBERTa_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Hyperledger_15_RoBERTa_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Hyperledger_15_RoBERTa_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Hyperledger_15_RoBERTa_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Hyperledger_15_RoBERTa_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/Hyperledger_15_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Hyperledger_15_RoBERTa_Over_Sampling/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Hyperledger_15_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- e9122404dd1c84056c98f7cf6c13b7dcd83f0a0822f6314b4cb5d9e770cb13b4
- Size of remote file:
- 499 MB
- SHA256:
- 34b58be48d457bf05a1ffcc9515cb99424b25f9dc27823fccea5ea8649e046d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.