Instructions to use hf-internal-testing/tiny-random-MPNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MPNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-MPNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MPNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-MPNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be9902999d182a482c7aeb351740436315a3f8d0f54119b2823a8c1f615927f4
- Size of remote file:
- 974 kB
- SHA256:
- d344fd3380e27307ff352800c664ce13957d54f09e29ea1d051096aaa865b612
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