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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- 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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- 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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- ## Uses
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- ## Evaluation
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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  ---
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  library_name: transformers
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+ license: mit
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+ base_model: facebook/w2v-bert-2.0
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: w2v-bert-cv-grain-lg_both_v2
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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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+ # w2v-bert-cv-grain-lg_both_v2
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+ This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0892
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+ - Wer: 0.0443
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+ - Cer: 0.0123
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+
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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: 0.0001
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+ - train_batch_size: 8
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 80
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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 | Wer | Cer |
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+ |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
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+ | 0.2889 | 1.0 | 10812 | 0.1708 | 0.1703 | 0.0386 |
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+ | 0.1849 | 2.0 | 21624 | 0.1342 | 0.1274 | 0.0285 |
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+ | 0.1512 | 3.0 | 32436 | 0.1144 | 0.1044 | 0.0244 |
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+ | 0.1313 | 4.0 | 43248 | 0.1033 | 0.0918 | 0.0217 |
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+ | 0.117 | 5.0 | 54060 | 0.1034 | 0.0738 | 0.0191 |
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+ | 0.1056 | 6.0 | 64872 | 0.0906 | 0.0738 | 0.0181 |
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+ | 0.0962 | 7.0 | 75684 | 0.0959 | 0.0655 | 0.0168 |
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+ | 0.0885 | 8.0 | 86496 | 0.0860 | 0.0592 | 0.0155 |
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+ | 0.0807 | 9.0 | 97308 | 0.0844 | 0.0603 | 0.0154 |
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+ | 0.0742 | 10.0 | 108120 | 0.0814 | 0.0573 | 0.0144 |
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+ | 0.0683 | 11.0 | 118932 | 0.0858 | 0.0588 | 0.0154 |
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+ | 0.0629 | 12.0 | 129744 | 0.0944 | 0.0538 | 0.0146 |
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+ | 0.0581 | 13.0 | 140556 | 0.0842 | 0.0558 | 0.0151 |
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+ | 0.0528 | 14.0 | 151368 | 0.0873 | 0.0503 | 0.0141 |
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+ | 0.0479 | 15.0 | 162180 | 0.0820 | 0.0503 | 0.0138 |
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+ | 0.0429 | 16.0 | 172992 | 0.0815 | 0.0427 | 0.0125 |
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+ | 0.0392 | 17.0 | 183804 | 0.0864 | 0.0466 | 0.0128 |
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+ | 0.035 | 18.0 | 194616 | 0.0899 | 0.0479 | 0.0128 |
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+ | 0.0316 | 19.0 | 205428 | 0.0872 | 0.0430 | 0.0120 |
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+ | 0.0286 | 20.0 | 216240 | 0.0821 | 0.0425 | 0.0114 |
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+ | 0.0254 | 21.0 | 227052 | 0.0898 | 0.0466 | 0.0122 |
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+ | 0.0229 | 22.0 | 237864 | 0.0864 | 0.0417 | 0.0120 |
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+ | 0.021 | 23.0 | 248676 | 0.0893 | 0.0408 | 0.0122 |
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+ | 0.0192 | 24.0 | 259488 | 0.0878 | 0.0430 | 0.0118 |
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+ | 0.0171 | 25.0 | 270300 | 0.0994 | 0.0473 | 0.0128 |
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+ | 0.0156 | 26.0 | 281112 | 0.0892 | 0.0443 | 0.0123 |
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+ ### Framework versions
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+ - Transformers 4.46.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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