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 .gitignore from SetFit/distilbert-base-uncased__sst2__all-train: direct link, hf CLI and curl.
- Browser
- Download file 13 Bytes
-
https://huggingface.co/SetFit/distilbert-base-uncased__sst2__all-train/resolve/main/.gitignore
- Command line
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hf download hf://SetFit/distilbert-base-uncased__sst2__all-train/.gitignore
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curl -L -o .gitignore https://huggingface.co/SetFit/distilbert-base-uncased__sst2__all-train/resolve/main/.gitignore
13 Bytes
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