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End of training

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: ntu-spml/distilhubert
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - marsyas/gtzan
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: distilhubert-finetuned-gtzan
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+ results:
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+ - task:
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+ name: Audio Classification
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+ type: audio-classification
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+ dataset:
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+ name: GTZAN
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+ type: marsyas/gtzan
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+ config: all
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+ split: train
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+ args: all
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.88
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # distilhubert-finetuned-gtzan
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+
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+ This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
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+ It achieves the following results on the evaluation set:
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+ - Accuracy: 0.88
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+ - Loss: 0.4331
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 32
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 20
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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+ |:-------------:|:-----:|:----:|:--------:|:---------------:|
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+ | 2.2693 | 0.99 | 28 | 0.31 | 2.2480 |
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+ | 1.9782 | 1.98 | 56 | 0.45 | 1.8990 |
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+ | 1.6438 | 2.97 | 84 | 0.62 | 1.5180 |
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+ | 1.3307 | 4.0 | 113 | 0.73 | 1.2206 |
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+ | 1.133 | 4.99 | 141 | 0.76 | 0.9961 |
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+ | 0.9384 | 5.98 | 169 | 0.78 | 0.8889 |
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+ | 0.8668 | 6.97 | 197 | 0.79 | 0.7543 |
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+ | 0.674 | 8.0 | 226 | 0.79 | 0.7433 |
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+ | 0.5997 | 8.99 | 254 | 0.83 | 0.6194 |
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+ | 0.5195 | 9.98 | 282 | 0.91 | 0.5685 |
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+ | 0.401 | 10.97 | 310 | 0.91 | 0.5144 |
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+ | 0.3151 | 12.0 | 339 | 0.87 | 0.4775 |
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+ | 0.2653 | 12.99 | 367 | 0.88 | 0.4984 |
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+ | 0.2182 | 13.98 | 395 | 0.88 | 0.4337 |
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+ | 0.2036 | 14.97 | 423 | 0.89 | 0.4657 |
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+ | 0.1925 | 16.0 | 452 | 0.89 | 0.4222 |
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+ | 0.1807 | 16.99 | 480 | 0.87 | 0.4512 |
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+ | 0.1626 | 17.98 | 508 | 0.88 | 0.4247 |
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+ | 0.1388 | 18.97 | 536 | 0.88 | 0.4324 |
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+ | 0.1718 | 19.82 | 560 | 0.88 | 0.4331 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.1+cu121
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ {
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+ "_name_or_path": "ntu-spml/distilhubert",
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+ "activation_dropout": 0.1,
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+ "apply_spec_augment": false,
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+ "architectures": [
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+ "HubertForSequenceClassification"
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+ ],
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+ "attention_dropout": 0.1,
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+ "bos_token_id": 1,
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+ "ctc_loss_reduction": "sum",
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+ "do_stable_layer_norm": false,
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+ "feat_extract_activation": "gelu",
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+ "final_dropout": 0.0,
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+ "hidden_act": "gelu",
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+ "hidden_dropout": 0.1,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "blues",
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+ "1": "classical",
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+ "2": "country",
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+ "3": "disco",
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+ "4": "hiphop",
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+ "5": "jazz",
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+ "6": "metal",
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+ "7": "pop",
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+ "8": "reggae",
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+ "9": "rock"
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+ "layer_norm_eps": 1e-05,
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+ "layerdrop": 0.0,
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+ "mask_feature_length": 10,
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+ "mask_feature_min_masks": 0,
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+ "mask_feature_prob": 0.0,
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+ "mask_time_length": 10,
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+ "mask_time_min_masks": 2,
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+ "mask_time_prob": 0.05,
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+ "model_type": "hubert",
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+ "num_attention_heads": 12,
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+ "num_conv_pos_embedding_groups": 16,
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+ "num_conv_pos_embeddings": 128,
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+ "num_feat_extract_layers": 7,
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+ "num_hidden_layers": 2,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.36.2",
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+ "use_weighted_layer_sum": false,
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+ "vocab_size": 32
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+ }
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