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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from SetFit/distilbert-base-uncased__sst2__all-train: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/SetFit/distilbert-base-uncased__sst2__all-train/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SetFit/distilbert-base-uncased__sst2__all-train/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/SetFit/distilbert-base-uncased__sst2__all-train/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- d5582f87140fd2e2588b0a02fb8fd9db1b57316db67c6d21a0b7c6e9d0ac6762
- Size of remote file:
- 268 MB
- SHA256:
- cd46a4a47f9b11f2ad9a40cb6b9fdf7905999c35f5e1172b04e778454877f89b
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