End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: miosipof/whisper-small-ft-balbus-sep28k-multiclass_v3a
+tags:
+- generated_from_trainer
+datasets:
+- balbus-classifier
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: whisper-small-ft-balbus-sep28k-multiclass_v3a-ft-balbus-sep28k-multiclass_v4_IT
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: Apple dataset
+      type: balbus-classifier
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.85924617196702
+    - name: Precision
+      type: precision
+      value: 0.8795132394309227
+    - name: Recall
+      type: recall
+      value: 0.8594066773532715
+    - name: F1
+      type: f1
+      value: 0.8603528122668381
+---
+
+<!-- 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. -->
+
+# whisper-small-ft-balbus-sep28k-multiclass_v3a-ft-balbus-sep28k-multiclass_v4_IT
+
+This model is a fine-tuned version of [miosipof/whisper-small-ft-balbus-sep28k-multiclass_v3a](https://huggingface.co/miosipof/whisper-small-ft-balbus-sep28k-multiclass_v3a) on the Apple dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2301
+- Accuracy: 0.8592
+- Precision: 0.8795
+- Recall: 0.8594
+- F1: 0.8604
+- Roc-auc: None
+
+## 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: 3e-06
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.5
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc-auc |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
+| 0.6519        | 0.4188 | 100  | 0.6127          | 0.4741   | 0.5574    | 0.4759 | 0.4098 | None    |
+| 0.4774        | 0.8377 | 200  | 0.4080          | 0.7468   | 0.7651    | 0.7470 | 0.7497 | None    |
+| 0.2425        | 1.2565 | 300  | 0.2518          | 0.8439   | 0.8566    | 0.8443 | 0.8445 | None    |
+| 0.2275        | 1.6754 | 400  | 0.2301          | 0.8592   | 0.8795    | 0.8594 | 0.8604 | None    |
+
+
+### Framework versions
+
+- Transformers 4.45.2
+- Pytorch 2.2.0
+- Datasets 3.6.0
+- Tokenizers 0.20.3