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
mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-base-fine-tuned-text-classificarion-ds-ss / special_tokens_map.json
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |