Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/mpnet-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/mpnet-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/mpnet-classification-10ksamples")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/mpnet-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/mpnet-classification-10ksamples") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2cce2852a007a3b33de19374569f49ed051988e2c0a2925fcdea45ee9fa9a5e1
- Size of remote file:
- 4.03 kB
- SHA256:
- a6142e258605e01f8c72bf9b4f0fc255687447b72861ca1544824865166771d3
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