Instructions to use A-Funakoshi/sample-text-classification-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use A-Funakoshi/sample-text-classification-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="A-Funakoshi/sample-text-classification-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("A-Funakoshi/sample-text-classification-bert") model = AutoModelForSequenceClassification.from_pretrained("A-Funakoshi/sample-text-classification-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 497 Bytes
5e1810a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": false,
"do_subword_tokenize": true,
"do_word_tokenize": true,
"jumanpp_kwargs": null,
"mask_token": "[MASK]",
"mecab_kwargs": null,
"model_max_length": 512,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"subword_tokenizer_type": "wordpiece",
"sudachi_kwargs": null,
"tokenizer_class": "BertJapaneseTokenizer",
"unk_token": "[UNK]",
"word_tokenizer_type": "mecab"
}
|