Instructions to use hf-tiny-model-private/tiny-random-ProphetNetModel 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-ProphetNetModel 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-ProphetNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ProphetNetModel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "clean_up_tokenization_spaces": true, | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 30, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/runner/.cache/huggingface/hub/models--microsoft--prophetnet-large-uncased/snapshots/fd5b6f7e0cae2f7cd69f33b3da1d316c8f43645e/special_tokens_map.json", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "ProphetNetTokenizer", | |
| "unk_token": "[UNK]", | |
| "x_sep_token": "[X_SEP]" | |
| } | |