Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use ppsingh/mpnet-adaptation_mitigation-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ppsingh/mpnet-adaptation_mitigation-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ppsingh/mpnet-adaptation_mitigation-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ppsingh/mpnet-adaptation_mitigation-classifier") model = AutoModelForSequenceClassification.from_pretrained("ppsingh/mpnet-adaptation_mitigation-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a945c94fa1833b8493d1e3b94b3dfb3931b38efa2917655f215e7d983efd3eb
- Size of remote file:
- 438 MB
- SHA256:
- 8f9609684c53f00f07d579a3017c65fa5f3468844433d85a3a255deb27faf772
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