Text Classification
Transformers
Safetensors
camembert
Generated from Trainer
text-embeddings-inference
Instructions to use grexit-d/multipride_umberto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grexit-d/multipride_umberto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="grexit-d/multipride_umberto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("grexit-d/multipride_umberto") model = AutoModelForSequenceClassification.from_pretrained("grexit-d/multipride_umberto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 38f8e95b9ca0ae828f70799adb1f739b8e7679fc34e8d00ec0d227139a58045c
- Size of remote file:
- 5.84 kB
- SHA256:
- f1b545901f780e9402731fc9e060845d6aa62ce0b4cbcaad543824cc8a88daa2
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