Instructions to use uzaaft/all_datasets_v3_mpnet-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uzaaft/all_datasets_v3_mpnet-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uzaaft/all_datasets_v3_mpnet-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uzaaft/all_datasets_v3_mpnet-base") model = AutoModelForMaskedLM.from_pretrained("uzaaft/all_datasets_v3_mpnet-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 548 Bytes
408ba21 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"bos_token": "<s>",
"clean_up_tokenization_spaces": true,
"cls_token": "<s>",
"do_lower_case": true,
"eos_token": "</s>",
"mask_token": "<mask>",
"max_length": 128,
"model_max_length": 512,
"pad_to_multiple_of": null,
"pad_token": "<pad>",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "</s>",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "MPNetTokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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