Text Classification
Transformers
PyTorch
Enawené-Nawé
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use tolgadev/rottentomato-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tolgadev/rottentomato-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tolgadev/rottentomato-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tolgadev/rottentomato-classifier") model = AutoModelForSequenceClassification.from_pretrained("tolgadev/rottentomato-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tolgadev/rottentomato-classifier: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
-
https://huggingface.co/tolgadev/rottentomato-classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://tolgadev/rottentomato-classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/tolgadev/rottentomato-classifier/resolve/main/tokenizer.json
712 kB
- Xet hash:
- 0d797430e457462904421f8fc11b4d31849c6fd68216ac3af8f5cdbbdb869868
- Size of remote file:
- 712 kB
- SHA256:
- c3bbeac7c7669ec478d44bac871031a47b39935533558d252d5b7699e7d4d457
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