Model save

README.md

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+---
+base_model: microsoft/unispeech-sat-base
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: unispeech-sat-base-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. -->
+
+# unispeech-sat-base-finetuned-common_voice
+
+This model is a fine-tuned version of [microsoft/unispeech-sat-base](https://huggingface.co/microsoft/unispeech-sat-base) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0481
+- Accuracy: 0.9925
+- F1: 0.9925
+- Recall: 0.9925
+- Precision: 0.9928
+- Mcc: 0.9907
+- Auc: 0.9999
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
+| 1.5302        | 1.0   | 50   | 1.4495          | 0.56     | 0.5047 | 0.5600 | 0.6655    | 0.4723 | 0.8635 |
+| 1.1592        | 2.0   | 100  | 0.9831          | 0.7125   | 0.6783 | 0.7125 | 0.7985    | 0.6723 | 0.9633 |
+| 0.7313        | 3.0   | 150  | 0.5535          | 0.9425   | 0.9428 | 0.9425 | 0.9455    | 0.9287 | 0.9926 |
+| 0.4431        | 4.0   | 200  | 0.2633          | 0.965    | 0.9651 | 0.9650 | 0.9676    | 0.9569 | 0.9976 |
+| 0.2353        | 5.0   | 250  | 0.1310          | 0.985    | 0.9850 | 0.985  | 0.9856    | 0.9814 | 0.9998 |
+| 0.1846        | 6.0   | 300  | 0.1136          | 0.9775   | 0.9775 | 0.9775 | 0.9783    | 0.9721 | 0.9978 |
+| 0.1464        | 7.0   | 350  | 0.0714          | 0.9875   | 0.9875 | 0.9875 | 0.9878    | 0.9844 | 1.0000 |
+| 0.1016        | 8.0   | 400  | 0.0592          | 0.99     | 0.9900 | 0.99   | 0.9902    | 0.9876 | 0.9999 |
+| 0.057         | 9.0   | 450  | 0.0466          | 0.9925   | 0.9925 | 0.9925 | 0.9928    | 0.9907 | 0.9999 |
+| 0.068         | 10.0  | 500  | 0.0481          | 0.9925   | 0.9925 | 0.9925 | 0.9928    | 0.9907 | 0.9999 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

model.safetensors

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 size 378306500