Instructions to use breadlicker45/human-class-or-somthing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/human-class-or-somthing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/human-class-or-somthing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/human-class-or-somthing") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/human-class-or-somthing") - Notebooks
- Google Colab
- Kaggle
File size: 388 Bytes
906332b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"bos_token": "<|startoftext|>",
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "<|pad|>",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "DistilBertTokenizer",
"unk_token": "[UNK]"
}
|