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:
- 073505f7c82735cb486efab98cd14e4f8f5f40c3bc68f17d80f4d5aeaa3e33be
- Size of remote file:
- 456 kB
- SHA256:
- 5ee5c8db3fc8898a361822dd2dfdb8bc1701cb88fbc2b82d9e8d3a0a54cf6b1c
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