Instructions to use SetFit/deberta-v3-base__sst2__all-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SetFit/deberta-v3-base__sst2__all-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SetFit/deberta-v3-base__sst2__all-train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SetFit/deberta-v3-base__sst2__all-train") model = AutoModelForSequenceClassification.from_pretrained("SetFit/deberta-v3-base__sst2__all-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Evaluation results for SetFit/deberta-v3-base__sst2__all-train model as a base model for other tasks
As part of a research effort to identify high quality models in Huggingface that can serve as base models for further finetuning, we evaluated this by finetuning on 36 datasets. The model ranks 3rd among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023. To share this information with others in your model card, please add the following evaluation results to your README.md page. For more information please see https://ibm.github.io/model-recycling/ or contact me. Best regards, Elad Venezian eladv@il.ibm.com IBM Research AI09f02c8
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update model card README.md
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Training in progress, epoch 1
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initial commit
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