End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: jonatasgrosman/wav2vec2-large-xlsr-53-english
+tags:
+- generated_from_trainer
+datasets:
+- audiofolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: wav2vec2-large-xlsr-53-english-finetuned-babycry-v3
+  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: 0.8152173913043478
+    - name: F1
+      type: f1
+      value: 0.7322311897943244
+    - name: Precision
+      type: precision
+      value: 0.6645793950850661
+    - name: Recall
+      type: recall
+      value: 0.8152173913043478
+---
+
+<!-- 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. -->
+
+# wav2vec2-large-xlsr-53-english-finetuned-babycry-v3
+
+This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-english](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english) on the audiofolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7337
+- Accuracy: {'accuracy': 0.8152173913043478}
+- F1: 0.7322
+- Precision: 0.6646
+- Recall: 0.8152
+
+## 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: 4
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 5
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
+| 0.949         | 0.5435 | 25   | 0.7351          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.7488        | 1.0870 | 50   | 0.7795          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.6911        | 1.6304 | 75   | 0.7066          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.8113        | 2.1739 | 100  | 0.8012          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.634         | 2.7174 | 125  | 0.7801          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.6503        | 3.2609 | 150  | 0.7712          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.7523        | 3.8043 | 175  | 0.7078          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.5493        | 4.3478 | 200  | 0.7484          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+| 0.7253        | 4.8913 | 225  | 0.7341          | {'accuracy': 0.8152173913043478} | 0.7322 | 0.6646    | 0.8152 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.19.1

runs/Oct01_15-49-58_c9432f693ceb/events.out.tfevents.1727798329.c9432f693ceb.266.8

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