Instructions to use hf-internal-testing/tiny-random-DistilBertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DistilBertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DistilBertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DistilBertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DistilBertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 390e5f1ae95c359797b935c88ce3211661355d5c7092d75d3342966859683549
- Size of remote file:
- 464 kB
- SHA256:
- f563bffe395eae2ffada6a609569a79d91d6f2ea0349dcc9844bc06230f490b2
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