Instructions to use eskayML/interview_electra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eskayML/interview_electra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eskayML/interview_electra")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eskayML/interview_electra") model = AutoModelForSequenceClassification.from_pretrained("eskayML/interview_electra", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e28f4cfd1e2469ea5df395ce27f96e506d9e3d078bc3f65452fd377184edf01
- Size of remote file:
- 5.3 kB
- SHA256:
- 1f90cb18423480090e5952f92b4845315772b669a221438661b8ddad1ce0062b
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