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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69d8ebe5b291d9d06a91faae7e5907dff0140f53eb9d66fd70b243cfb34f9817
- Size of remote file:
- 1.02 MB
- SHA256:
- dc08c2474eb9328dfc5a17c712984d9f19b897fac75c4fd0296d4b37e079dd59
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