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library_name: transformers
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---
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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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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<!-- 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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## 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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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 2422974460
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8c15eb5a09f5ca2bfb4f26ec70a9139ebe3c6604fd788c5dd39f64866a9d800
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size 2422974460
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