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BEST-RQ-ViT

Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
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audioset
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BEST-RQ-ViT
File size: 2,633 Bytes
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{
  "schema_version": 1,
  "model_name": "BEST-RQ-ViT",
  "mock": false,
  "model_target": "src.models.best_rq_module.BestRQModule",
  "source": {
    "wandb_run": {
      "path": "tuncay-ludovic/audio embeddings/81ptcoku",
      "name": "best_rq-audioset-200k-256x1bs",
      "checkpoint_name": "checkpoints/last.ckpt",
      "run_state": "finished",
      "summary_trainer_global_step": 200000
    },
    "training_config_sha256": "6b1d556408e1f1379f7368b0dfe0d51e21da8b933867eeaa1e507a49311be3c0",
    "configured_max_steps": 200000,
    "checkpoint_sha256": "66a99a0b98a463ed3509578b1b53ab45ddcd424e2f47a5305cdf35d7e6a7fb03",
    "global_step": 200000
  },
  "runtime_versions": {
    "torch": "2.9.1",
    "torchaudio": "2.9.1",
    "transformers": "5.17.0",
    "timm": "1.0.22",
    "einops": "0.8.1"
  },
  "validation": {
    "max_absolute_error": 0.0,
    "batch_size": 2
  },
  "files": {
    "CODE_LICENSE": "8fe9e8b749cd4abedabcb3100df445db899e72192394d14cc2dbf24a40811af6",
    "README.md": "662fd0b97f7dff054d662b4a0ff9b117b87c8a411275d4b715a4c19fdbd389a7",
    "adapters.py": "ca0f26826763c0e48b7508da732242bb83adfeb4814e389ed3fde93ad648c728",
    "config.json": "fe8227548617416bd879efe1be3e9e3fc04270709250f09f01806780fe548789",
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    "extraction.py": "8949389abc52278e24634518f72eae1270bb1a649ad016b71e0d6e8113acaa7c",
    "feature_extraction_audio.py": "5ed9004727be8b61bd6c0efbeb703b634c20c1b790d9fd59d6a9910438e89b39",
    "model.safetensors": "f30df9d0fbe9a69cc9ce691ac7fb5eb49027f7791975cb9ff8284bf1704c0b58",
    "modeling_audio.py": "ba394cd7f80929274090bc1b8986298247f61494db5ed99c0c5469f4e30bb874",
    "normalization.py": "918b0eb9d547e77ba1adc154deb33d29861532e2a0cd7663497bfc9df6155cef",
    "patch_embed.py": "825cf2ca0a9d8d10e12f40dc69d2293aabf04b627b926d42314a8b5193e231f7",
    "preprocessor_config.json": "e37df5cada8190b709335e4c7d45e714617cfcdd532d0e270562225b173b26af",
    "requirements.txt": "08c9ad278bc06a31d7c7ed3a2255dca48bd63faf29ae7c27635c284d082bb818",
    "rope.py": "d74bdc7a7dd4ba98414f93c4f1937b95614b57394984d265af5999327efc2d82",
    "spectrogram.py": "b1e6de8e8ec8220ad9369a765f80a1504b13a6a54aae2ab5bc18cd8dd1e0a894",
    "transformer.py": "03ea1cb7509669f91003f8cd94edd3660b0c6788e14335ca995c60834a2017d4",
    "vit.py": "cb6b05b5f617def68e05c40e6bf4d8a24a7b43e5acc4481649e3541bd172e727",
    "waveform_feature_encoder.py": "69c811624650441e7f46d4fc4e8177c9bac2f65970aef5c6fbd826522f109e22",
    "LICENSE": "8fe9e8b749cd4abedabcb3100df445db899e72192394d14cc2dbf24a40811af6"
  }
}