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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
add tokenizer
Browse files- tokenizer.json +0 -0
tokenizer.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
|
|