Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__sst2__all-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/distilbert-base-uncased__sst2__all-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/distilbert-base-uncased__sst2__all-train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__sst2__all-train") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__sst2__all-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51a106bcb2f58e624263fa5ccd2deccbc6fd4cdc5e658b7760263287c9f13bd1
- Size of remote file:
- 3.06 kB
- SHA256:
- 392dadabdb847b88e055f93b938bc60456a05caa77848363d9bc097dc5741ea7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.