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:
- 8844f132f6b41de956722c96f0e312f67e8732048eb39a86544e9ab1e93db08c
- Size of remote file:
- 974 kB
- SHA256:
- 8ddd339c5bb0f648a038144249a844705a6ecf4c938db55ebb8269f2140d76e3
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