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:
- 4ba9a08dbae45f44b147aaa7e1d028c01a0db9c1fb756ad2b8dee0570be30183
- Size of remote file:
- 438 MB
- SHA256:
- 299c7e2a5549311fc76e3b6bb5d545c7e085427e490b16084c48dc7a98cdaa93
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