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
File size: 529 Bytes
b336e87 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"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
}
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