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--- |
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language: |
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- all |
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license: apache-2.0 |
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tags: |
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- minds14 |
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- google/xtreme_s |
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- generated_from_trainer |
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datasets: |
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- google/xtreme_s |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: xtreme_s_xlsr_300m_minds14 |
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results: [] |
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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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should probably proofread and complete it, then remove this comment. --> |
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# xtreme_s_xlsr_300m_minds14 |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14.ALL dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9033 |
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- Accuracy Cs-cz: 0.9164 |
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- Accuracy De-de: 0.9477 |
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- Accuracy En-au: 0.9235 |
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- Accuracy En-gb: 0.9324 |
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- Accuracy En-us: 0.9326 |
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- Accuracy Es-es: 0.9177 |
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- Accuracy Fr-fr: 0.9444 |
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- Accuracy It-it: 0.9167 |
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- Accuracy Ko-kr: 0.8649 |
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- Accuracy Nl-nl: 0.9450 |
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- Accuracy Pl-pl: 0.9146 |
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- Accuracy Pt-pt: 0.8940 |
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- Accuracy Ru-ru: 0.8667 |
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- Accuracy Zh-cn: 0.7291 |
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- F1: 0.9015 |
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- F1 Cs-cz: 0.9154 |
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- F1 De-de: 0.9467 |
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- F1 En-au: 0.9199 |
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- F1 En-gb: 0.9334 |
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- F1 En-us: 0.9308 |
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- F1 Es-es: 0.9158 |
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- F1 Fr-fr: 0.9436 |
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- F1 It-it: 0.9135 |
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- F1 Ko-kr: 0.8642 |
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- F1 Nl-nl: 0.9440 |
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- F1 Pl-pl: 0.9159 |
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- F1 Pt-pt: 0.8883 |
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- F1 Ru-ru: 0.8646 |
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- F1 Zh-cn: 0.7249 |
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- Loss: 0.4119 |
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- Loss Cs-cz: 0.3790 |
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- Loss De-de: 0.2649 |
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- Loss En-au: 0.3459 |
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- Loss En-gb: 0.2853 |
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- Loss En-us: 0.2203 |
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- Loss Es-es: 0.2731 |
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- Loss Fr-fr: 0.1909 |
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- Loss It-it: 0.3520 |
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- Loss Ko-kr: 0.5431 |
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- Loss Nl-nl: 0.2515 |
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- Loss Pl-pl: 0.4113 |
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- Loss Pt-pt: 0.4798 |
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- Loss Ru-ru: 0.6470 |
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- Loss Zh-cn: 1.1216 |
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- Predict Samples: 4086 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 50.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:| |
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| 2.6739 | 5.41 | 200 | 2.5687 | 0.0430 | 0.1190 | |
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| 1.4953 | 10.81 | 400 | 1.6052 | 0.5550 | 0.5692 | |
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| 0.6177 | 16.22 | 600 | 0.7927 | 0.8052 | 0.8011 | |
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| 0.3609 | 21.62 | 800 | 0.5679 | 0.8609 | 0.8609 | |
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| 0.4972 | 27.03 | 1000 | 0.5944 | 0.8509 | 0.8523 | |
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| 0.1799 | 32.43 | 1200 | 0.6194 | 0.8623 | 0.8621 | |
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| 0.1308 | 37.84 | 1400 | 0.5956 | 0.8569 | 0.8548 | |
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| 0.2298 | 43.24 | 1600 | 0.5201 | 0.8732 | 0.8743 | |
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| 0.0052 | 48.65 | 1800 | 0.3826 | 0.9106 | 0.9103 | |
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### Framework versions |
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- Transformers 4.18.0.dev0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 2.0.1.dev0 |
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- Tokenizers 0.11.6 |
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