Instructions to use moshew/mpnet-base-sst2-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moshew/mpnet-base-sst2-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="moshew/mpnet-base-sst2-distilled")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("moshew/mpnet-base-sst2-distilled") model = AutoModelForSequenceClassification.from_pretrained("moshew/mpnet-base-sst2-distilled") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4485c5a0a6086483fa3449da07140347eb399b359cf109b4bc1568f991fdc42d
- Size of remote file:
- 3.06 kB
- SHA256:
- 66904111125d44c75a11b803b6969ecab1b153f6adc16a72d6f908efbf089dfa
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