Instructions to use YakovElm/MariaDB_5_RoBERTa_Over_Sampling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/MariaDB_5_RoBERTa_Over_Sampling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/MariaDB_5_RoBERTa_Over_Sampling")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/MariaDB_5_RoBERTa_Over_Sampling") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/MariaDB_5_RoBERTa_Over_Sampling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/MariaDB_5_RoBERTa_Over_Sampling: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/YakovElm/MariaDB_5_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/MariaDB_5_RoBERTa_Over_Sampling/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/MariaDB_5_RoBERTa_Over_Sampling/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 228973e86c35fb411915f6f0c22b7fde445729176ea405f8485a3f8fb0c82ca7
- Size of remote file:
- 499 MB
- SHA256:
- b768bfa7d2d687695f4ca86c6f8afd8a2ef178421e13185e146577f48f2a6b76
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