Instructions to use NOVA-vision-language/polite_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NOVA-vision-language/polite_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NOVA-vision-language/polite_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NOVA-vision-language/polite_bert") model = AutoModelForSequenceClassification.from_pretrained("NOVA-vision-language/polite_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "name_or_path": "experiments/polite_bert/polite_bert_v1/train/final", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "padding": true, | |
| "return_attention_mask": true, | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": null, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation": false, | |
| "unk_token": "[UNK]", | |
| "use_fast": false | |
| } | |