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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-base-ls960 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: hubert-base-ls960-finetuned-ic-slurp-wt_init |
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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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# hubert-base-ls960-finetuned-ic-slurp-wt_init |
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This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1377 |
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- Accuracy: 0.4604 |
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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: 5e-05 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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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_ratio: 0.1 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:| |
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| 3.9613 | 1.0 | 527 | 3.8944 | 0.0803 | |
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| 3.7817 | 2.0 | 1055 | 3.7275 | 0.0910 | |
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| 3.6357 | 3.0 | 1582 | 3.5410 | 0.1308 | |
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| 3.4527 | 4.0 | 2110 | 3.3426 | 0.1676 | |
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| 3.0715 | 5.0 | 2637 | 3.0751 | 0.2331 | |
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| 2.9153 | 6.0 | 3165 | 2.8168 | 0.2969 | |
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| 2.5333 | 7.0 | 3692 | 2.6229 | 0.3375 | |
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| 2.3807 | 8.0 | 4220 | 2.5673 | 0.3620 | |
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| 2.181 | 9.0 | 4747 | 2.4933 | 0.3835 | |
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| 1.9118 | 10.0 | 5275 | 2.4411 | 0.4046 | |
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| 1.9015 | 11.0 | 5802 | 2.4254 | 0.4126 | |
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| 1.5811 | 12.0 | 6330 | 2.4216 | 0.4275 | |
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| 1.491 | 13.0 | 6857 | 2.4833 | 0.4284 | |
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| 1.3697 | 14.0 | 7385 | 2.5243 | 0.4368 | |
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| 1.1232 | 15.0 | 7912 | 2.5944 | 0.4309 | |
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| 1.1071 | 16.0 | 8440 | 2.6475 | 0.4317 | |
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| 0.9439 | 17.0 | 8967 | 2.6379 | 0.4449 | |
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| 0.917 | 18.0 | 9495 | 2.7438 | 0.4468 | |
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| 0.7628 | 19.0 | 10022 | 2.7671 | 0.4513 | |
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| 0.7642 | 20.0 | 10550 | 2.8993 | 0.4418 | |
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| 0.6716 | 21.0 | 11077 | 2.9354 | 0.4472 | |
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| 0.6166 | 22.0 | 11605 | 2.9961 | 0.4510 | |
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| 0.4819 | 23.0 | 12132 | 3.0959 | 0.4451 | |
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| 0.5903 | 24.0 | 12660 | 3.0542 | 0.4557 | |
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| 0.515 | 25.0 | 13187 | 3.0723 | 0.4589 | |
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| 0.518 | 26.0 | 13715 | 3.1377 | 0.4604 | |
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| 0.3902 | 27.0 | 14242 | 3.2230 | 0.4524 | |
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| 0.4825 | 28.0 | 14770 | 3.2925 | 0.4583 | |
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| 0.29 | 29.0 | 15297 | 3.4027 | 0.4498 | |
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| 0.2789 | 30.0 | 15825 | 3.3573 | 0.4598 | |
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| 0.3202 | 31.0 | 16352 | 3.4381 | 0.4542 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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