Instructions to use ModelTC/bart-base-sst2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-sst2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-sst2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-sst2") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-sst2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9b019d07e0272f39875369293e553d8c9a1bcbcc23c3bf8b1af953109959bead
- Size of remote file:
- 2.99 kB
- SHA256:
- 3710f21d166d379b2367b3df014954a82ba55fb2396cc31733d9c6634f5c7e5e
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