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:
- 6a0c5539acfb8800573ee3fe6e867d04389f99113382997acc4519dcb5dc39e0
- Size of remote file:
- 17.6 MB
- SHA256:
- 1bc47acb15892c9c0885c1ea4b696ed70e80e468f772578f3b9f7533b4356d36
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