Instructions to use Jingya/tiny-random-DistilBertModel-for-sentence-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jingya/tiny-random-DistilBertModel-for-sentence-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jingya/tiny-random-DistilBertModel-for-sentence-transformers")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Jingya/tiny-random-DistilBertModel-for-sentence-transformers") model = AutoModel.from_pretrained("Jingya/tiny-random-DistilBertModel-for-sentence-transformers") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b878dcd956906c72d037fb3cdf7a451e037c2218007e7e939f2daa96c603fb3
- Size of remote file:
- 372 kB
- SHA256:
- ab3a703ef01c3ec964e6d241a0d166e6be15df8f7e30ac57cda4423fe280b04e
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