Instructions to use ncsu-dk-lab/AutoDisProxyT-RTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncsu-dk-lab/AutoDisProxyT-RTE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncsu-dk-lab/AutoDisProxyT-RTE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncsu-dk-lab/AutoDisProxyT-RTE") model = AutoModelForSequenceClassification.from_pretrained("ncsu-dk-lab/AutoDisProxyT-RTE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ncsu-dk-lab/AutoDisProxyT-RTE: direct link, hf CLI and curl.
- Browser
- Download file 637 Bytes
-
https://huggingface.co/ncsu-dk-lab/AutoDisProxyT-RTE/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ncsu-dk-lab/AutoDisProxyT-RTE/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ncsu-dk-lab/AutoDisProxyT-RTE/resolve/main/tokenizer_config.json
637 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "model_max_length": 512, "name_or_path": "/home.local/jianwei/workspace/archive/SparseOptimizer/output/Layer_7_12_Hid_160_768_Head_10_12_IMRatio_3.5", "never_split": null, "special_tokens_map_file": "/home.local/jianwei/.cache/huggingface/transformers/b680d52711d2451bbd6c6b1700365d6d731977c1357ae86bd7227f61145d3be2.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "tokenizer_class": "BertTokenizer"} |