Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use Sleoruiz/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sleoruiz/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sleoruiz/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sleoruiz/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss") model = AutoModelForSequenceClassification.from_pretrained("Sleoruiz/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2897701df531b02cfad16d6b17ed80b44cd552c8cbbcf726c4df93b43a05b421
- Size of remote file:
- 16.3 MB
- SHA256:
- 9b7176ac32cc1a8fdd65c26bbd7786cbb0308af2592ab5cebd333fdda4b63dd6
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