Instructions to use Srjnnnn/distilbert-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Srjnnnn/distilbert-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Srjnnnn/distilbert-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Srjnnnn/distilbert-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Srjnnnn/distilbert-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be433fd23e5d769164e090044189e6e5f18395f5b0d347c98dfef67aa5e208d1
- Size of remote file:
- 268 MB
- SHA256:
- 9df399436adfb8510e30ec0defb53b5e3fc4a59015200e39b1432caae8807ffd
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