Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use SetFit/distilbert-base-uncased__sst2__train-8-1 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-8-1 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-8-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-8-1") model = AutoModelForSequenceClassification.from_pretrained("SetFit/distilbert-base-uncased__sst2__train-8-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SetFit/distilbert-base-uncased__sst2__train-8-1: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/SetFit/distilbert-base-uncased__sst2__train-8-1/resolve/main/training_args.bin
- Command line
-
hf download hf://SetFit/distilbert-base-uncased__sst2__train-8-1/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/SetFit/distilbert-base-uncased__sst2__train-8-1/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 49716f57ebf9d5949eea5e47910fe871caea7d0dfa476606f9260b76cd5a956b
- Size of remote file:
- 3.06 kB
- SHA256:
- 0dcd22f8c90a87ed4090f9710a40f71963bc7ce799667041a3d5d560ec4aef44
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