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---
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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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- f1
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model-index:
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- name: wavlm-large-finetuned-iemocap2
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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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# wavlm-large-finetuned-iemocap2
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This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.0935
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- Accuracy: 0.5335
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- F1: 0.5005
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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: 3e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 20
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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| 1.3826 | 0.98 | 25 | 1.3815 | 0.2502 | 0.1003 |
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| 1.3263 | 1.98 | 50 | 1.3663 | 0.2502 | 0.1002 |
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| 1.2563 | 2.98 | 75 | 1.2589 | 0.3870 | 0.3051 |
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| 1.1869 | 3.98 | 100 | 1.2042 | 0.3977 | 0.3428 |
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| 1.1291 | 4.98 | 125 | 1.1768 | 0.4539 | 0.4557 |
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| 1.1171 | 5.98 | 150 | 1.1425 | 0.4888 | 0.4799 |
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| 1.0811 | 6.98 | 175 | 1.1316 | 0.4956 | 0.4851 |
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| 1.0627 | 7.98 | 200 | 1.1241 | 0.5044 | 0.4859 |
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| 1.079 | 8.98 | 225 | 1.1026 | 0.5228 | 0.5031 |
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| 1.0294 | 9.98 | 250 | 1.1018 | 0.5199 | 0.4959 |
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| 1.0088 | 10.98 | 275 | 1.0903 | 0.5325 | 0.5046 |
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| 1.0217 | 11.98 | 300 | 1.0966 | 0.5296 | 0.5015 |
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| 1.0034 | 12.98 | 325 | 1.1012 | 0.5296 | 0.4990 |
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| 1.0024 | 13.98 | 350 | 1.0832 | 0.5393 | 0.5127 |
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| 1.0047 | 14.98 | 375 | 1.0902 | 0.5315 | 0.4986 |
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| 0.9436 | 15.98 | 400 | 1.0896 | 0.5373 | 0.5085 |
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| 0.9584 | 16.98 | 425 | 1.0859 | 0.5412 | 0.5114 |
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| 0.9859 | 17.98 | 450 | 1.0865 | 0.5412 | 0.5120 |
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| 0.9679 | 18.98 | 475 | 1.0926 | 0.5335 | 0.4999 |
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| 0.9468 | 19.98 | 500 | 1.0935 | 0.5335 | 0.5005 |
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### Framework versions
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- Transformers 4.26.1
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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