End of training

README.md

@@ -0,0 +1,74 @@
+---
+language:
+- zh
+base_model: sit-justin/whisper-small-test
+tags:
+- generated_from_trainer
+datasets:
+- custom_datset
+model-index:
+- name: Whisper Small Chinese MOE Response
+  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. -->
+
+# Whisper Small Chinese MOE Response
+
+This model is a fine-tuned version of [sit-justin/whisper-small-test](https://huggingface.co/sit-justin/whisper-small-test) on the MOE Response Chinese dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0158
+- Cer: 2.5487
+
+## 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: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 250
+- training_steps: 2000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Cer    |
+|:-------------:|:------:|:----:|:---------------:|:------:|
+| 0.0804        | 0.5405 | 200  | 0.0771          | 5.5753 |
+| 0.0695        | 1.0811 | 400  | 0.0632          | 5.4650 |
+| 0.0374        | 1.6216 | 600  | 0.0467          | 4.6808 |
+| 0.0228        | 2.1622 | 800  | 0.0439          | 4.9994 |
+| 0.0142        | 2.7027 | 1000 | 0.0339          | 3.4310 |
+| 0.0068        | 3.2432 | 1200 | 0.0257          | 5.1587 |
+| 0.005         | 3.7838 | 1400 | 0.0216          | 2.7815 |
+| 0.0019        | 4.3243 | 1600 | 0.0176          | 2.3772 |
+| 0.0023        | 4.8649 | 1800 | 0.0158          | 2.5242 |
+| 0.0012        | 5.4054 | 2000 | 0.0158          | 2.5487 |
+
+
+### Framework versions
+
+- Transformers 4.42.3
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

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+}

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