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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-RobertaModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 801840eb877237525d19876cc56141752ab77f8e3fe2a74bc4af363038d31b51
- Size of remote file:
- 460 kB
- SHA256:
- dfe2212dd6e44a85bbbc42f1a22a321f8ef999c22b41b2f2d83b5b56d558856e
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