Instructions to use daniazie/mdeberta-v3_translationese_pairwise_combined_loss_v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniazie/mdeberta-v3_translationese_pairwise_combined_loss_v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="daniazie/mdeberta-v3_translationese_pairwise_combined_loss_v0.1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("daniazie/mdeberta-v3_translationese_pairwise_combined_loss_v0.1") model = AutoModelForSequenceClassification.from_pretrained("daniazie/mdeberta-v3_translationese_pairwise_combined_loss_v0.1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbedbc3da3674ee4e7041d9fe4b86d40544e8d6cde8f42c9aef7baadee7d9205
- Size of remote file:
- 406 MB
- SHA256:
- 9b07449151f2c0c3e0652fde305639375a8e4b1ded8b41443773b3cc4c950058
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