Model save

README.md

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+---
+license: bsd-3-clause
+base_model: MIT/ast-finetuned-audioset-10-10-0.4593
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
+  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. -->
+
+# ast-finetuned-audioset-10-10-0.4593-finetuned-common_voice
+
+This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4652
+- Accuracy: 0.905
+- F1: 0.9049
+- Recall: 0.905
+- Precision: 0.9057
+- Mcc: 0.8814
+- Auc: 0.9874
+
+## 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-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- 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: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision | Mcc    | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
+| 1.0765        | 1.0   | 200  | 0.9662          | 0.5925   | 0.5411 | 0.5925 | 0.6493    | 0.5098 | 0.9152 |
+| 0.5977        | 2.0   | 400  | 0.5536          | 0.8      | 0.7999 | 0.8    | 0.8146    | 0.7532 | 0.9670 |
+| 0.1826        | 3.0   | 600  | 0.5388          | 0.8375   | 0.8385 | 0.8375 | 0.8491    | 0.7994 | 0.9759 |
+| 0.1125        | 4.0   | 800  | 0.6617          | 0.85     | 0.8486 | 0.85   | 0.8636    | 0.8161 | 0.9798 |
+| 0.0025        | 5.0   | 1000 | 0.5859          | 0.865    | 0.8653 | 0.865  | 0.8733    | 0.8333 | 0.984  |
+| 0.0058        | 6.0   | 1200 | 0.5043          | 0.8975   | 0.8968 | 0.8975 | 0.9001    | 0.8728 | 0.9882 |
+| 0.0           | 7.0   | 1400 | 0.4883          | 0.8925   | 0.8932 | 0.8925 | 0.8957    | 0.8660 | 0.9859 |
+| 0.0           | 8.0   | 1600 | 0.4652          | 0.905    | 0.9050 | 0.905  | 0.9055    | 0.8814 | 0.9871 |
+| 0.0           | 9.0   | 1800 | 0.4655          | 0.905    | 0.9049 | 0.905  | 0.9057    | 0.8814 | 0.9873 |
+| 0.0           | 10.0  | 2000 | 0.4652          | 0.905    | 0.9049 | 0.905  | 0.9057    | 0.8814 | 0.9874 |
+
+
+### Framework versions
+
+- Transformers 4.41.0
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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+oid sha256:17356e7d2f3b9e9ec26433b3dcfcbf6a06855d96cdb2dbcd1abef3894688c2f1
 size 344799268