Instructions to use hf-internal-testing/tiny-random-ElectraForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ElectraForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-ElectraForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-ElectraForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-ElectraForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f43743c5dad0ad0f5266fdacb1de59be1cc8208ec18c57433957297b754444a
- Size of remote file:
- 1.13 MB
- SHA256:
- e3fd837eb9d6820c299da3cab48ca92004d7b6b368c76b512e40fa646a82bee9
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