Instructions to use hf-tiny-model-private/tiny-random-SplinterModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SplinterModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-SplinterModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SplinterModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SplinterModel") - Notebooks
- Google Colab
- Kaggle
File size: 596 Bytes
acf31e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"additional_special_tokens": [
"[QUESTION]"
],
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/runner/.cache/huggingface/hub/models--tau--splinter-base/snapshots/d6bc929405a27b7502bbab767f615c89b0e52373/special_tokens_map.json",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "SplinterTokenizer",
"unk_token": "[UNK]"
}
|