Instructions to use YakovElm/Apache_20_RoBERTa_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache_20_RoBERTa_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache_20_RoBERTa_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache_20_RoBERTa_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache_20_RoBERTa_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Apache_20_RoBERTa_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/Apache_20_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Apache_20_RoBERTa_Over_Sampling/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Apache_20_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 6d317ed339432e58c12f1539dea17b5ab731a5193f3f872ce8f0b42c78114607
- Size of remote file:
- 499 MB
- SHA256:
- cc108f231ae50bc953a4d568549b8582e9678b7af2af90c23949336ab35bf051
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