Text Classification
Transformers
Safetensors
bert
classification
Generated from Trainer
text-embeddings-inference
Instructions to use MiVaCod/rotten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiVaCod/rotten with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MiVaCod/rotten")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MiVaCod/rotten") model = AutoModelForSequenceClassification.from_pretrained("MiVaCod/rotten") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea82603431bf9749796e4bdf398a6bff363177ddc6ff9f42f3f1c55b6045d122
- Size of remote file:
- 438 MB
- SHA256:
- 36f0db7f6da9a367f744249356619a897444ac5ab556b46e12d5876b245fa192
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