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:
- 5177c6745df49c927bfabeddaf96e0726d6cc87072d24ab21dd86010fa1260d8
- Size of remote file:
- 4.66 kB
- SHA256:
- 423dcb6d007294effbfa2b5a2be727eef8af322e497d4dcde0c369f2975baf70
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