Feature Extraction
Transformers
PyTorch
TensorFlow
ONNX
Safetensors
roberta
text-embeddings-inference
Instructions to use hf-internal-testing/tiny-random-RobertaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-RobertaModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RobertaModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3d96c0bfffc1a49d3c10aeb4c77f9873bcb6e4250db919d2910e3428e3a288f
- Size of remote file:
- 371 kB
- SHA256:
- b8ce160880352bca2cce64ab3b4022018c29d258ca1721df342e458ad5a4e6fa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.