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:
- 04cb3a4f86b04f16adfd4f0343b2c88e735d831cc6f3165a3ac630a4fe5d4365
- Size of remote file:
- 3.06 kB
- SHA256:
- 2386707b8b8cc77adcbab9d3894df26a273ade960dd6cc92303fc7ed6c2806fe
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