Instructions to use hf-tiny-model-private/tiny-random-MPNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MPNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-MPNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MPNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a9d9658fb8ebeead98e744846ed7eb459f38064f8197f20d29dfa25759f2d21
- Size of remote file:
- 1.08 MB
- SHA256:
- d7a0046ebcfd3ae7c4bbbe9e377992abe5f648766fa1d62283c2b84294826caf
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