Text Classification
Transformers
PyTorch
English
distilbert
classification
sequence-classification
text-embeddings-inference
Instructions to use profoz/mlops-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use profoz/mlops-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="profoz/mlops-demo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("profoz/mlops-demo") model = AutoModelForSequenceClassification.from_pretrained("profoz/mlops-demo") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d81ab9870f44a30d0f009e457a427bed43120954a2155ae00c2c9e0e9bc3b685
- Size of remote file:
- 268 MB
- SHA256:
- 6e909ecaa770ed237bfa184966a038652b4664da2653f4dec46ac73e1744402a
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