Instructions to use hf-internal-testing/tiny-random-RobertaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-RobertaForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-RobertaForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
Upload ONNX weights (#2)
Browse files- [Awaiting approval] Upload ONNX weights (6373daf0f5edf43316066a360fb29d6091d8f945)
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:e4261a029ef5738df95aa2b32b57f7d757f8527d9e19f643d4bcaffceaf6dfe9
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size 459437
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