Instructions to use MLap/bloom1.7-lora-sentiment-analysis-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLap/bloom1.7-lora-sentiment-analysis-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MLap/bloom1.7-lora-sentiment-analysis-classification")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MLap/bloom1.7-lora-sentiment-analysis-classification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d21eed7552a1813684443962880a8ce27dcde14ab4b8d1b42af794c132a39a6
- Size of remote file:
- 21.8 MB
- SHA256:
- b266bb5cbf91ba626a33e11b7a324ed8c0d2a9592327c8575081dd00e5c4a88f
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