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:
- 507a153b95b1181da6166d3eb9ca088ddd2abb1ff62dfdaf9c994e759f818fd9
- Size of remote file:
- 456 kB
- SHA256:
- c354c902560b5daf9544da42017a2bb8d569c6a6d2560e265ff65eb2e51c379c
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