Instructions to use eskayML/electra_interview_duplicated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eskayML/electra_interview_duplicated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eskayML/electra_interview_duplicated")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eskayML/electra_interview_duplicated") model = AutoModelForSequenceClassification.from_pretrained("eskayML/electra_interview_duplicated", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b49f466510148c196c539af61453fd2a21b51d120c2ac2f5799f713325914a60
- Size of remote file:
- 5.18 kB
- SHA256:
- 18338bf3ea3bc3266fedad86a6ed71864d3445c6ad89fbcf7585c5330b841661
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.