Instructions to use driftbench/sst2_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/sst2_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/sst2_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/sst2_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/sst2_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fe43095a559e8332ad8e2f1a682392ccf1a69d1c2a2cee2949b261f7323d7ee
- Size of remote file:
- 499 MB
- SHA256:
- 1303fb4b48dbc363b74d37e4a8acfe8f38802ab10281c2a4e8bde1403b7303a7
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