Model save

README.md

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+---
+license: apache-2.0
+base_model: facebook/hubert-base-ls960
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- recall
+- precision
+model-index:
+- name: hubert-base-ls960-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. -->
+
+# hubert-base-ls960-finetuned-common_voice
+
+This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4616
+- Accuracy: 0.9375
+- F1: 0.9377
+- Recall: 0.9375
+- Precision: 0.9403
+- Mcc: 0.9225
+- Auc: 0.9925
+
+## 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: 16
+- total_train_batch_size: 128
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|:------:|:------:|
+| 1.601         | 0.96  | 12   | 1.5594          | 0.385    | 0.3217 | 0.3850 | 0.6064    | 0.2666 | 0.7895 |
+| 1.5467        | 2.0   | 25   | 1.3344          | 0.67     | 0.6516 | 0.6700 | 0.7185    | 0.6030 | 0.9009 |
+| 1.4062        | 2.96  | 37   | 1.0521          | 0.8      | 0.7964 | 0.8    | 0.8014    | 0.7521 | 0.9436 |
+| 1.0881        | 4.0   | 50   | 0.8340          | 0.8525   | 0.8502 | 0.8525 | 0.8677    | 0.8201 | 0.9759 |
+| 0.9348        | 4.96  | 62   | 0.7227          | 0.89     | 0.8894 | 0.89   | 0.8939    | 0.8639 | 0.9801 |
+| 0.8596        | 6.0   | 75   | 0.5873          | 0.9275   | 0.9276 | 0.9275 | 0.9300    | 0.9100 | 0.9908 |
+| 0.7917        | 6.96  | 87   | 0.5208          | 0.93     | 0.9298 | 0.93   | 0.9310    | 0.9128 | 0.9940 |
+| 0.6721        | 8.0   | 100  | 0.4784          | 0.9475   | 0.9476 | 0.9475 | 0.9491    | 0.9348 | 0.9935 |
+| 0.6297        | 8.96  | 112  | 0.4734          | 0.9325   | 0.9326 | 0.9325 | 0.9363    | 0.9166 | 0.9916 |
+| 0.6127        | 9.6   | 120  | 0.4616          | 0.9375   | 0.9377 | 0.9375 | 0.9403    | 0.9225 | 0.9925 |
+
+
+### 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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