Instructions to use hf-internal-testing/tiny-random-StableLmForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-StableLmForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-StableLmForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-StableLmForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-StableLmForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3506145694815ecdf1755a593f8f405c9fca06afc6134293e82dbc8a2df32d35
- Size of remote file:
- 456 kB
- SHA256:
- 0f8f4dc08ce6a4422987ddb8171d84f4ec1208c1f3d3ba368c54cdd7f6037016
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