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:
- 6e04073623701ce5b8f6de1ea9c41964dad02312ab84d494ae69a8d0cddc8afb
- Size of remote file:
- 1.08 MB
- SHA256:
- 955bed7df8f5fc47bffcaf37d1fdf75acc7d23a5e761d9e1879a6a84c78a7baa
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