Instructions to use joseph10/tiny-bert-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joseph10/tiny-bert-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="joseph10/tiny-bert-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("joseph10/tiny-bert-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("joseph10/tiny-bert-sst2-distilled") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82d8b92e324bbbeaaeefdba4cae0d6e0a80792367e4b40fe8ff8d3280753556c
- Size of remote file:
- 1.06 kB
- SHA256:
- 8ff5a8cb8c3895397aaa2e4c7983aad7239f98779586db87c227b3d954ca69d6
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