Instructions to use Nehc/W10_27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nehc/W10_27 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nehc/W10_27")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nehc/W10_27") model = AutoModelForSequenceClassification.from_pretrained("Nehc/W10_27", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer_config.json
Browse files- tokenizer_config.json +1 -0
tokenizer_config.json
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{"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "E:\\HF_HOME\\transformers\\857db185d48b92f3e6141ef5092d8d5dbebab7eef1bacc6c9eaf85cf23807641.73ad1f9fd9f94089672128003fb4a687b64b73b2bfb8d08766bbc71feec8cd96", "name_or_path": "bert-base-multilingual-uncased", "tokenizer_class": "BertTokenizer"}
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