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:
+- audiofolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: distilhubert-finetuned-babycry-v6
+  results:
+  - task:
+      name: Audio Classification
+      type: audio-classification
+    dataset:
+      name: audiofolder
+      type: audiofolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value:
+        accuracy: 0.8260869565217391
+    - name: F1
+      type: f1
+      value: 0.7474120082815735
+    - name: Precision
+      type: precision
+      value: 0.6824196597353497
+    - name: Recall
+      type: recall
+      value: 0.8260869565217391
+---
+
+<!-- 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-babycry-v6
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6676
+- Accuracy: {'accuracy': 0.8260869565217391}
+- F1: 0.7474
+- Precision: 0.6824
+- Recall: 0.8261
+
+## 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: 0.001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
+| 0.7816        | 2.1739 | 25   | 0.7361          | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824    | 0.8261 |
+| 0.7056        | 4.3478 | 50   | 0.6957          | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824    | 0.8261 |
+| 0.6654        | 6.5217 | 75   | 0.6683          | {'accuracy': 0.8260869565217391} | 0.7474 | 0.6824    | 0.8261 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1

runs/Oct02_16-31-49_ad7b983360ff/events.out.tfevents.1727886828.ad7b983360ff.829.1

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