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
Commit History
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Librarian Bot: Add base_model information to model (#1)
- Librarian Bot: Add base_model information to model (f7e9a98c285034bede2aab8535ffce01de9ac2cf) Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>2b6ddad
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Checkpoint for WandB run: distilbert-all-train-f09c7705
7ae927f -
Training in progress, epoch 2
64ecb97 -
Training in progress, epoch 1
6ee91d5 -
update model card README.md
a9fb1e1 -
Training in progress, epoch 1
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initial commit
f4f150b