Instructions to use gavulsim/deberta_large_finetuned_claimdecomp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gavulsim/deberta_large_finetuned_claimdecomp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gavulsim/deberta_large_finetuned_claimdecomp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gavulsim/deberta_large_finetuned_claimdecomp") model = AutoModelForSequenceClassification.from_pretrained("gavulsim/deberta_large_finetuned_claimdecomp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80188e206cdb5b758a7b02604654ba23e43d2889036f8d695fd0fbe629b3e70d
- Size of remote file:
- 4.09 kB
- SHA256:
- 5a5947719f4180bc42b2c195f2cf7614e3727ffe498ce99037acb1dfb9f0005c
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