Instructions to use moshew/bert-tiny-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/bert-tiny-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/bert-tiny-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/bert-tiny-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/bert-tiny-sst2-distilled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfef28577f572b5e4a3f92a760a74de06c029c9875281b9921b418933e01fa1b
- Size of remote file:
- 44.7 MB
- SHA256:
- d74a33655ee53e50859a5ce20bbae1b9f39b9653b7a3bc4ebbdf3cf4e801caa0
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