End of training

README.md

@@ -1,61 +1,87 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: ntu-spml/distilhubert
-tags:
-- generated_from_trainer
-datasets:
-- marsyas/gtzan
-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 GTZAN dataset.
-It achieves the following results on the evaluation set:
-- eval_loss: 1.8559
-- eval_accuracy: 0.57
-- eval_runtime: 119.8761
-- eval_samples_per_second: 0.834
-- eval_steps_per_second: 0.108
-- epoch: 1.0
-- step: 113
-
-## 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: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 10
-- mixed_precision_training: Native AMP
-
-### Framework versions
-
-- Transformers 4.48.2
-- Pytorch 2.6.0+cu126
-- Datasets 3.2.0
-- Tokenizers 0.21.0
+---
+library_name: transformers
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- marsyas/gtzan
+metrics:
+- accuracy
+model-index:
+- name: distilhubert-finetuned-gtzan
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: GTZAN
+      type: marsyas/gtzan
+      config: all
+      split: train
+      args: all
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.82
+---
+
+<!-- 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 GTZAN dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7001
+- Accuracy: 0.82
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 1.5997        | 1.0   | 113  | 1.5628          | 0.56     |
+| 1.0615        | 2.0   | 226  | 1.1134          | 0.68     |
+| 0.9443        | 3.0   | 339  | 0.8761          | 0.78     |
+| 0.491         | 4.0   | 452  | 0.8227          | 0.78     |
+| 0.382         | 5.0   | 565  | 0.7287          | 0.79     |
+| 0.2423        | 6.0   | 678  | 0.6206          | 0.82     |
+| 0.1179        | 7.0   | 791  | 0.6201          | 0.82     |
+| 0.055         | 8.0   | 904  | 0.6419          | 0.83     |
+| 0.0533        | 9.0   | 1017 | 0.6680          | 0.84     |
+| 0.0329        | 10.0  | 1130 | 0.7001          | 0.82     |
+
+
+### Framework versions
+
+- Transformers 4.47.1
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

model.safetensors

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