Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__sst2__train-16-0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__sst2__train-16-0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__sst2__train-16-0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-16-0") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-16-0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6863f2d526eb63d96416096e84ab4d2724b6a08ef553106235ab4757e20ba17c
- Size of remote file:
- 268 MB
- SHA256:
- e859097df3a4e505a9b1c4bbb256d95bfd61a6fdf411d1df44b0d92435633839
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