End of training

README.md

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+---
+license: apache-2.0
+base_model: facebook/hubert-base-ls960
+tags:
+- generated_from_trainer
+datasets:
+- Emo-Codec/CREMA-D_synth
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: hubert-base-ls960-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: 0.8016085790884718
+    - name: Precision
+      type: precision
+      value: 0.8014677098753149
+    - name: Recall
+      type: recall
+      value: 0.8016085790884718
+    - name: F1
+      type: f1
+      value: 0.7989608760238184
+---
+
+<!-- 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. -->
+
+# hubert-base-ls960-tone-classification
+
+This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the CREMA-D dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7499
+- Accuracy: 0.8016
+- Precision: 0.8015
+- Recall: 0.8016
+- F1: 0.7990
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.4326        | 1.0   | 442  | 1.2934          | 0.5147   | 0.5889    | 0.5147 | 0.4878 |
+| 1.0447        | 2.0   | 884  | 0.8590          | 0.7051   | 0.7570    | 0.7051 | 0.7125 |
+| 0.775         | 3.0   | 1326 | 0.7668          | 0.7426   | 0.7589    | 0.7426 | 0.7404 |
+| 0.6593        | 4.0   | 1768 | 0.8127          | 0.7265   | 0.7564    | 0.7265 | 0.7245 |
+| 0.5014        | 5.0   | 2210 | 0.8670          | 0.7507   | 0.7631    | 0.7507 | 0.7436 |
+| 0.48          | 6.0   | 2652 | 0.7473          | 0.7694   | 0.7739    | 0.7694 | 0.7623 |
+| 0.3505        | 7.0   | 3094 | 0.7647          | 0.8016   | 0.8039    | 0.8016 | 0.7991 |
+| 0.3223        | 8.0   | 3536 | 0.7499          | 0.8016   | 0.8015    | 0.8016 | 0.7990 |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1

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