Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use RazyDave/deberta-v3-base-finetuned-rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RazyDave/deberta-v3-base-finetuned-rte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RazyDave/deberta-v3-base-finetuned-rte")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RazyDave/deberta-v3-base-finetuned-rte") model = AutoModelForSequenceClassification.from_pretrained("RazyDave/deberta-v3-base-finetuned-rte") - Notebooks
- Google Colab
- Kaggle
File size: 454 Bytes
310516a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"name_or_path": "microsoft/deberta-v3-base",
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"special_tokens_map_file": null,
"split_by_punct": false,
"tokenizer_class": "DebertaV2Tokenizer",
"unk_token": "[UNK]",
"vocab_type": "spm"
}
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