Instructions to use dfafdsaf/deberta_sentiment_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dfafdsaf/deberta_sentiment_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dfafdsaf/deberta_sentiment_5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dfafdsaf/deberta_sentiment_5000") model = AutoModelForSequenceClassification.from_pretrained("dfafdsaf/deberta_sentiment_5000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0e42b3bc1ad72002e17228782ce05523983c8fea99aa9a9ad4d621121fe1be0
- Size of remote file:
- 568 MB
- SHA256:
- caa72240d43c9443e34be0699e45383ef8116983017943a4f1602cb92ef98bd0
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