End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: ntu-spml/distilhubert
+tags:
+- generated_from_trainer
+datasets:
+- Emo-Codec/CREMA-D_synth
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: distilhubert-tone-classification
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: CREMA-D
+      type: Emo-Codec/CREMA-D_synth
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7024128686327078
+    - name: Precision
+      type: precision
+      value: 0.7036509389001218
+    - name: Recall
+      type: recall
+      value: 0.7024128686327078
+    - name: F1
+      type: f1
+      value: 0.6970142752522046
+---
+
+<!-- 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-tone-classification
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the CREMA-D dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.1479
+- Accuracy: 0.7024
+- Precision: 0.7037
+- Recall: 0.7024
+- F1: 0.6970
+
+## 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: 16
+- eval_batch_size: 16
+- 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: 8
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.339         | 1.0   | 442  | 1.3491          | 0.4987   | 0.5533    | 0.4987 | 0.4664 |
+| 1.0008        | 2.0   | 884  | 1.0219          | 0.6408   | 0.6668    | 0.6408 | 0.6373 |
+| 0.7673        | 3.0   | 1326 | 0.9572          | 0.6676   | 0.6870    | 0.6676 | 0.6557 |
+| 0.5888        | 4.0   | 1768 | 0.8830          | 0.6890   | 0.6930    | 0.6890 | 0.6889 |
+| 0.4396        | 5.0   | 2210 | 1.0893          | 0.6810   | 0.7064    | 0.6810 | 0.6738 |
+| 0.2987        | 6.0   | 2652 | 1.0561          | 0.6810   | 0.6892    | 0.6810 | 0.6738 |
+| 0.2009        | 7.0   | 3094 | 1.1421          | 0.6836   | 0.6944    | 0.6836 | 0.6769 |
+| 0.1345        | 8.0   | 3536 | 1.1479          | 0.7024   | 0.7037    | 0.7024 | 0.6970 |
+
+
+### Framework versions
+
+- Transformers 4.50.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.0
+- Tokenizers 0.21.1