Text Classification
Transformers
PyTorch
TensorBoard
Safetensors
deberta-v2
text-embeddings-inference
Instructions to use mgfrantz/deberta_v3_finetuned_predicting_effective_arguments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgfrantz/deberta_v3_finetuned_predicting_effective_arguments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mgfrantz/deberta_v3_finetuned_predicting_effective_arguments")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mgfrantz/deberta_v3_finetuned_predicting_effective_arguments") model = AutoModelForSequenceClassification.from_pretrained("mgfrantz/deberta_v3_finetuned_predicting_effective_arguments", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (fa3c1892455c670971ce74443741b6059535e0d7) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>954bf08
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commit files to HF hub
18cc8c4
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ADD new training run
71dc3ac -
ADD new runs, was not able to outperform baseline
389f120
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commit files to HF hub
8fdedac
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initial commit
7265fb2