Instructions to use hf-internal-testing/tiny-random-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-distilbert") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-distilbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d27f247d04536a6e1de78c8c7fd78b9bfc76c97fd36f5f084feea6b507c200e3
- Size of remote file:
- 14.5 MB
- SHA256:
- 7d5dbea3a59ae9f6fbc276b26cd9b91fb44c35416b677af725495f167dc0ac72
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