End of training

README.md

@@ -1,199 +1,115 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+base_model: facebook/wav2vec2-large-xlsr-53
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: xlsr-a-nomimo
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+<!-- 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. -->
+
+# xlsr-a-nomimo
+
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4323
+- Wer: 0.3434
+
+## 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: 0.0004
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 132
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|
+| 4.7158        | 1.9275  | 200   | 2.5326          | 1.0    |
+| 1.6646        | 3.8502  | 400   | 0.5913          | 0.6836 |
+| 0.4371        | 5.7729  | 600   | 0.2586          | 0.4437 |
+| 0.2367        | 7.6957  | 800   | 0.3040          | 0.3974 |
+| 0.1839        | 9.6184  | 1000  | 0.3419          | 0.3781 |
+| 0.1751        | 11.5411 | 1200  | 0.3340          | 0.3804 |
+| 0.1252        | 13.4638 | 1400  | 0.3298          | 0.3719 |
+| 0.1281        | 15.3865 | 1600  | 0.3086          | 0.3519 |
+| 0.1063        | 17.3092 | 1800  | 0.2961          | 0.3619 |
+| 0.1074        | 19.2319 | 2000  | 0.3216          | 0.3673 |
+| 0.0842        | 21.1546 | 2200  | 0.3414          | 0.3696 |
+| 0.0876        | 23.0773 | 2400  | 0.3611          | 0.3549 |
+| 0.075         | 25.0    | 2600  | 0.3633          | 0.3611 |
+| 0.0714        | 26.9275 | 2800  | 0.3337          | 0.3565 |
+| 0.072         | 28.8502 | 3000  | 0.3967          | 0.3696 |
+| 0.0596        | 30.7729 | 3200  | 0.4276          | 0.3557 |
+| 0.0547        | 32.6957 | 3400  | 0.3842          | 0.3611 |
+| 0.0589        | 34.6184 | 3600  | 0.4189          | 0.3542 |
+| 0.052         | 36.5411 | 3800  | 0.3832          | 0.3542 |
+| 0.0445        | 38.4638 | 4000  | 0.3674          | 0.3627 |
+| 0.0435        | 40.3865 | 4200  | 0.4747          | 0.3557 |
+| 0.0492        | 42.3092 | 4400  | 0.4231          | 0.3526 |
+| 0.0388        | 44.2319 | 4600  | 0.3696          | 0.3681 |
+| 0.0485        | 46.1546 | 4800  | 0.4486          | 0.3526 |
+| 0.0384        | 48.0773 | 5000  | 0.4647          | 0.3549 |
+| 0.0355        | 50.0    | 5200  | 0.3833          | 0.3611 |
+| 0.0365        | 51.9275 | 5400  | 0.4611          | 0.3549 |
+| 0.0337        | 53.8502 | 5600  | 0.4655          | 0.3534 |
+| 0.0378        | 55.7729 | 5800  | 0.4439          | 0.3534 |
+| 0.0285        | 57.6957 | 6000  | 0.4738          | 0.3534 |
+| 0.0312        | 59.6184 | 6200  | 0.4310          | 0.3619 |
+| 0.0329        | 61.5411 | 6400  | 0.4504          | 0.3549 |
+| 0.0253        | 63.4638 | 6600  | 0.4600          | 0.3542 |
+| 0.0291        | 65.3865 | 6800  | 0.4838          | 0.3519 |
+| 0.0272        | 67.3092 | 7000  | 0.4505          | 0.3495 |
+| 0.0266        | 69.2319 | 7200  | 0.4152          | 0.3426 |
+| 0.024         | 71.1546 | 7400  | 0.3947          | 0.3472 |
+| 0.022         | 73.0773 | 7600  | 0.3965          | 0.3495 |
+| 0.0215        | 75.0    | 7800  | 0.4233          | 0.3426 |
+| 0.0225        | 76.9275 | 8000  | 0.3956          | 0.3449 |
+| 0.0224        | 78.8502 | 8200  | 0.4179          | 0.3511 |
+| 0.0207        | 80.7729 | 8400  | 0.4081          | 0.3434 |
+| 0.0168        | 82.6957 | 8600  | 0.4523          | 0.3449 |
+| 0.0194        | 84.6184 | 8800  | 0.3983          | 0.3457 |
+| 0.0169        | 86.5411 | 9000  | 0.4381          | 0.3441 |
+| 0.0173        | 88.4638 | 9200  | 0.4524          | 0.3465 |
+| 0.0145        | 90.3865 | 9400  | 0.4308          | 0.3449 |
+| 0.016         | 92.3092 | 9600  | 0.4272          | 0.3441 |
+| 0.014         | 94.2319 | 9800  | 0.4203          | 0.3426 |
+| 0.0152        | 96.1546 | 10000 | 0.4314          | 0.3457 |
+| 0.0147        | 98.0773 | 10200 | 0.4323          | 0.3434 |
+
+
+### Framework versions
+
+- Transformers 4.47.0.dev0
+- Pytorch 2.4.0
+- Datasets 3.0.1
+- Tokenizers 0.20.0

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