End of training

README.md

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+---
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: distilhubert-finetuned-gtzan
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# distilhubert-finetuned-gtzan
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5086
+- Accuracy: 0.89
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 4e-05
+- train_batch_size: 6
+- eval_batch_size: 6
+- seed: 42
+- gradient_accumulation_steps: 7
+- total_train_batch_size: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 25
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 2.2912        | 0.98  | 21   | 2.2667          | 0.19     |
+| 2.2263        | 1.96  | 42   | 2.1460          | 0.48     |
+| 1.9552        | 2.99  | 64   | 1.8067          | 0.44     |
+| 1.5982        | 3.97  | 85   | 1.5912          | 0.54     |
+| 1.5182        | 4.99  | 107  | 1.4077          | 0.61     |
+| 1.2855        | 5.97  | 128  | 1.2654          | 0.69     |
+| 1.1649        | 7.0   | 150  | 1.1915          | 0.69     |
+| 1.0742        | 7.98  | 171  | 1.0769          | 0.75     |
+| 1.0495        | 8.96  | 192  | 1.0011          | 0.77     |
+| 0.8827        | 9.99  | 214  | 0.9062          | 0.79     |
+| 0.7886        | 10.97 | 235  | 0.8333          | 0.83     |
+| 0.7019        | 11.99 | 257  | 0.7801          | 0.83     |
+| 0.6642        | 12.97 | 278  | 0.7691          | 0.79     |
+| 0.5982        | 14.0  | 300  | 0.6984          | 0.82     |
+| 0.5002        | 14.98 | 321  | 0.6526          | 0.84     |
+| 0.4789        | 15.96 | 342  | 0.5980          | 0.88     |
+| 0.3908        | 16.99 | 364  | 0.5874          | 0.86     |
+| 0.3892        | 17.97 | 385  | 0.5570          | 0.86     |
+| 0.3675        | 18.99 | 407  | 0.5634          | 0.87     |
+| 0.303         | 19.97 | 428  | 0.5387          | 0.87     |
+| 0.3017        | 21.0  | 450  | 0.5086          | 0.89     |
+| 0.2469        | 21.98 | 471  | 0.4969          | 0.89     |
+| 0.2542        | 22.96 | 492  | 0.4972          | 0.88     |
+| 0.2651        | 23.99 | 514  | 0.4947          | 0.89     |
+| 0.2591        | 24.5  | 525  | 0.4929          | 0.89     |
+
+
+### Framework versions
+
+- Transformers 4.35.0
+- Pytorch 2.1.0
+- Datasets 2.14.6
+- Tokenizers 0.14.1

config.json

@@ -0,0 +1,96 @@
+{
+  "_name_or_path": "ntu-spml/distilhubert",
+  "activation_dropout": 0.1,
+  "apply_spec_augment": false,
+  "architectures": [
+    "HubertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "sum",
+  "ctc_zero_infinity": false,
+  "do_stable_layer_norm": false,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_norm": "group",
+  "feat_proj_dropout": 0.0,
+  "feat_proj_layer_norm": false,
+  "final_dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "blues",
+    "1": "classical",
+    "2": "country",
+    "3": "disco",
+    "4": "hiphop",
+    "5": "jazz",
+    "6": "metal",
+    "7": "pop",
+    "8": "reggae",
+    "9": "rock"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "blues": "0",
+    "classical": "1",
+    "country": "2",
+    "disco": "3",
+    "hiphop": "4",
+    "jazz": "5",
+    "metal": "6",
+    "pop": "7",
+    "reggae": "8",
+    "rock": "9"
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_feature_length": 10,
+  "mask_feature_min_masks": 0,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_masks": 2,
+  "mask_time_prob": 0.05,
+  "model_type": "hubert",
+  "num_attention_heads": 12,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32
+}

model.safetensors

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preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

training_args.bin

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