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:
- 6f1f2c3dd01d75cac8562398b21a8f1c24806c1428bbe07733237e281f9c6890
- Size of remote file:
- 533 kB
- SHA256:
- 41b67d400d12ed556232eef03bcf9b49b60859c27ff206c043225e141305fdb6
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