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:
- fd6a5737faf2d31df3f84216c7edb1d5aad7c2cbb6d9b172aba042facd10d747
- Size of remote file:
- 456 kB
- SHA256:
- 20fd04409d69a6568130a4e08ffc526b1abb4f2d209670d8821bd311aadae214
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