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
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: distilhubert-finetuned-babycry-v7
+  results: []
+---
+
+<!-- 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-v7
+
+This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.5860
+- Accuracy: {'accuracy': 0.8695652173913043}
+- F1: 0.8089
+- Precision: 0.7561
+- Recall: 0.8696
+
+## 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: 4
+- 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: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 8
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy                         | F1     | Precision | Recall |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:------:|:---------:|:------:|
+| 1.005         | 0.5435 | 25   | 0.6526          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.7736        | 1.0870 | 50   | 0.6396          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.5618        | 1.6304 | 75   | 0.6990          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.8623        | 2.1739 | 100  | 0.5802          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6532        | 2.7174 | 125  | 0.6205          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.8076        | 3.2609 | 150  | 0.6168          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.7581        | 3.8043 | 175  | 0.5917          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.7525        | 4.3478 | 200  | 0.5988          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6566        | 4.8913 | 225  | 0.5997          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6845        | 5.4348 | 250  | 0.5815          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6812        | 5.9783 | 275  | 0.5830          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6548        | 6.5217 | 300  | 0.5855          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.6555        | 7.0652 | 325  | 0.5859          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+| 0.7294        | 7.6087 | 350  | 0.5861          | {'accuracy': 0.8695652173913043} | 0.8089 | 0.7561    | 0.8696 |
+
+
+### Framework versions
+
+- Transformers 4.44.2
+- Pytorch 2.4.1+cu121
+- Tokenizers 0.19.1

runs/Oct02_17-13-51_315dc4c4c032/events.out.tfevents.1727889368.315dc4c4c032.673.1

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