Instructions to use VityaVitalich/bert-tiny-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VityaVitalich/bert-tiny-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VityaVitalich/bert-tiny-sst2", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VityaVitalich/bert-tiny-sst2") model = AutoModelForSequenceClassification.from_pretrained("VityaVitalich/bert-tiny-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0347968c59975806f6ad9c5f43df626acb7329e7e22fbc99682659a695dbaf91
- Size of remote file:
- 8.79 MB
- SHA256:
- 5c7bf9547da061b1868afb1a2609c9f88913eca5c4b41dc90aa7e28c19167670
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