Instructions to use YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS20Classic_Balance_DATA_ratio_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72041f577bcddd27f5ac43f137daad2125a657606a90dc59e83b99605c3bb24a
- Size of remote file:
- 438 MB
- SHA256:
- aa16df7918ddff9acbce28a2ea05234d4220761ae838e5896b38ef8253edabbc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.