Instructions to use ncsu-dk-lab/AutoDisProxyT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncsu-dk-lab/AutoDisProxyT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncsu-dk-lab/AutoDisProxyT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ncsu-dk-lab/AutoDisProxyT") model = AutoModelForMaskedLM.from_pretrained("ncsu-dk-lab/AutoDisProxyT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 595 Bytes
363d524 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"name_or_path": "textattack/bert-base-uncased-MRPC",
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home.local/jianwei/.cache/huggingface/transformers/b680d52711d2451bbd6c6b1700365d6d731977c1357ae86bd7227f61145d3be2.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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