Instructions to use YakovElm/Qt15_ElectraModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Qt15_ElectraModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Qt15_ElectraModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Qt15_ElectraModel") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Qt15_ElectraModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Qt15_ElectraModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Qt15_ElectraModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Qt15_ElectraModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Qt15_ElectraModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 1a9087f8fcec0eeece9e650b09075816227dd05cf1dbe0b8bce3aafa51446e6d
- Size of remote file:
- 438 MB
- SHA256:
- ce02cf8b79a6b2ce980c1b3de42660036ad5ba7db4f779eade1f0f5a22b35419
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.