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")# pip install -U transformers accelerate # 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
Commit History
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[Awaiting approval] Upload ONNX weights
Verified signature[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).a814b9a
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Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (0200e0c05d867a8a6c44821f0e19f55342c7c858) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>737096d
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Upload tiny models for RobertaModel
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initial commit
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