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Marcos12886
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distilhubert-finetuned-donateacry

Audio Classification
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hubert
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End of training

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Files changed (7) hide show
  1. README.md +19 -20
  2. model.safetensors +1 -1
  3. runs/Aug27_21-17-02_DesMar/events.out.tfevents.1724786225.DesMar +3 -0
  4. runs/Aug27_21-19-43_DesMar/events.out.tfevents.1724786386.DesMar +3 -0
  5. runs/Aug27_21-21-38_DesMar/events.out.tfevents.1724786501.DesMar +3 -0
  6. runs/Aug27_21-24-56_DesMar/events.out.tfevents.1724786699.DesMar +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8048780487804879
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  - name: F1
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  type: f1
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- value: 0.7226459261496639
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  - name: Precision
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  type: precision
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- value: 0.658964484381686
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  - name: Recall
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  type: recall
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- value: 0.8048780487804879
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.7831
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- - Accuracy: 0.8049
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- - F1: 0.7226
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- - Precision: 0.6590
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- - Recall: 0.8049
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  ## Model description
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@@ -76,23 +76,22 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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- - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | No log | 0.9032 | 7 | 1.1575 | 0.7317 | 0.6183 | 0.5354 | 0.7317 |
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- | No log | 1.9355 | 15 | 2.4664 | 0.1382 | 0.2163 | 0.4976 | 0.1382 |
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- | No log | 2.9677 | 23 | 0.8516 | 0.7967 | 0.7145 | 0.6480 | 0.7967 |
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- | No log | 4.0 | 31 | 0.8733 | 0.7967 | 0.7147 | 0.6539 | 0.7967 |
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- | No log | 4.9032 | 38 | 0.9544 | 0.7724 | 0.6931 | 0.6287 | 0.7724 |
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- | No log | 5.9355 | 46 | 0.8408 | 0.7886 | 0.7072 | 0.6411 | 0.7886 |
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- | No log | 6.9677 | 54 | 0.8002 | 0.8049 | 0.7226 | 0.6590 | 0.8049 |
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- | No log | 8.0 | 62 | 0.7852 | 0.8049 | 0.7226 | 0.6590 | 0.8049 |
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- | No log | 8.9032 | 69 | 0.7831 | 0.8049 | 0.7226 | 0.6590 | 0.8049 |
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- | No log | 9.0323 | 70 | 0.7831 | 0.8049 | 0.7226 | 0.6590 | 0.8049 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8130081300813008
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  - name: F1
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  type: f1
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+ value: 0.7606844060819746
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  - name: Precision
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  type: precision
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+ value: 0.7167376435669118
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  - name: Recall
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  type: recall
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+ value: 0.8130081300813008
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the audiofolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.7024
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+ - Accuracy: 0.8130
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+ - F1: 0.7607
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+ - Precision: 0.7167
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+ - Recall: 0.8130
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  ## Model description
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.03
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  - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 0.9032 | 7 | 1.0334 | 0.7317 | 0.6183 | 0.5354 | 0.7317 |
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+ | No log | 1.9355 | 15 | 0.9193 | 0.7642 | 0.6866 | 0.6238 | 0.7642 |
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+ | No log | 2.9677 | 23 | 0.7766 | 0.8049 | 0.7460 | 0.7005 | 0.8049 |
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+ | No log | 4.0 | 31 | 0.8394 | 0.7724 | 0.7275 | 0.6889 | 0.7724 |
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+ | No log | 4.9032 | 38 | 0.7391 | 0.7805 | 0.7351 | 0.6962 | 0.7805 |
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+ | No log | 5.9355 | 46 | 0.7578 | 0.8130 | 0.7607 | 0.7167 | 0.8130 |
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+ | No log | 6.9677 | 54 | 0.6822 | 0.8049 | 0.7558 | 0.7147 | 0.8049 |
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+ | No log | 8.0 | 62 | 0.6980 | 0.8049 | 0.7543 | 0.7119 | 0.8049 |
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+ | No log | 8.9032 | 69 | 0.7024 | 0.8130 | 0.7607 | 0.7167 | 0.8130 |
 
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  ### Framework versions
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