Instructions to use diwank/dyda-deberta-pair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diwank/dyda-deberta-pair with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="diwank/dyda-deberta-pair")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("diwank/dyda-deberta-pair") model = AutoModelForSequenceClassification.from_pretrained("diwank/dyda-deberta-pair", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from diwank/dyda-deberta-pair: direct link, hf CLI and curl.
- Browser
- Download file 778 Bytes
-
https://huggingface.co/diwank/dyda-deberta-pair/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://diwank/dyda-deberta-pair/special_tokens_map.json
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curl -L -o special_tokens_map.json https://huggingface.co/diwank/dyda-deberta-pair/resolve/main/special_tokens_map.json
778 Bytes
| {"bos_token": {"content": "[CLS]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "eos_token": {"content": "[SEP]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "unk_token": {"content": "[UNK]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "sep_token": {"content": "[SEP]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "pad_token": {"content": "[PAD]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "cls_token": {"content": "[CLS]", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}} |