Instructions to use B-K/song2midi-processor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use B-K/song2midi-processor with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("B-K/song2midi-processor", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,175 Bytes
bf18ffb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | {
"added_tokens_decoder": {
"0": {
"content": "PAD_None",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "BOS_None",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "EOS_None",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "UNK_None",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"auto_map": {
"AutoProcessor": "processing_song2midi.Song2MIDIProcessor",
"AutoTokenizer": [
"tokenization_song2midi.Song2MIDIPerTokTokenizer",
null
]
},
"backend": "custom",
"bos_token": "BOS_None",
"eos_token": "EOS_None",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "PAD_None",
"processor_class": "Song2MIDIProcessor",
"tokenizer_class": "Song2MIDIPerTokTokenizer",
"unk_token": "UNK_None"
}
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