Instructions to use TeamResearch/sentiment-model-saagie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TeamResearch/sentiment-model-saagie with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TeamResearch/sentiment-model-saagie")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TeamResearch/sentiment-model-saagie") model = AutoModelForSequenceClassification.from_pretrained("TeamResearch/sentiment-model-saagie") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b97981ec274b3f544ec76078073caa4c88b589a86e3bcfccf7e3fd10bff562ca
- Size of remote file:
- 3.06 kB
- SHA256:
- e0155b1285a2de22af08a14afedbfaf47a8923e04e7d52b43f6f5b82d5632017
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