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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a176e0d703e411c76dff6c899d832ca550b178fe45c455264407060b8af418d
- Size of remote file:
- 439 kB
- SHA256:
- 205fccd6682d7b848f2459d9b95a401bc91a432f2e2f71d890b5ca2416ca1dcf
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