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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- feb735e5daeb98c1c5ba470f2faac0189b283ff152853bec979fa9e33f547197
- Size of remote file:
- 4.98 kB
- SHA256:
- aa266dda59ab05b6d8a3bd12796853d46bbd145b3ec9e83e0f1e590f973b5933
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.