Instructions to use Jingya/tiny-random-bert-remote-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingya/tiny-random-bert-remote-code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jingya/tiny-random-bert-remote-code")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jingya/tiny-random-bert-remote-code") model = AutoModel.from_pretrained("Jingya/tiny-random-bert-remote-code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d3a2c7b10cf5f910845a473e417d28112a94f0536a88f75c484a639a48aa2c8f
- Size of remote file:
- 450 kB
- SHA256:
- 126992b1103fb31189f48c08978de9af4c82e1688d974413c6c9cf21f364d902
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