Instructions to use YakovElm/Jira15_ElectraModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Jira15_ElectraModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Jira15_ElectraModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Jira15_ElectraModel") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Jira15_ElectraModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from YakovElm/Jira15_ElectraModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/Jira15_ElectraModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://YakovElm/Jira15_ElectraModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/YakovElm/Jira15_ElectraModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 6b42d62ccd4a60c8ffcd4a9261c335bddf09f5f70eee6ef3d1519396fe3ac4e0
- Size of remote file:
- 438 MB
- SHA256:
- b4888ba7cd0353642831d8ef9463baaaa1a637be3cad63c2bd2136cd91293b89
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.