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README.md
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library_name: transformers
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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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#### 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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#### 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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## 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: apache-2.0
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base_model: facebook/wav2vec2-large-xlsr-53
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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: xlsr-big-kannn
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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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# xlsr-big-kannn
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0000
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- Wer: 0.0510
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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: 0.0004
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_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: 132
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- num_epochs: 100
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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 |
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|:-------------:|:-------:|:-----:|:---------------:|:------:|
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| 2.0631 | 1.9704 | 200 | 0.6852 | 0.5241 |
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| 0.3968 | 3.9409 | 400 | 0.0531 | 0.1099 |
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| 0.1256 | 5.9113 | 600 | 0.0184 | 0.0633 |
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| 0.0844 | 7.8818 | 800 | 0.0339 | 0.0643 |
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| 0.0669 | 9.8522 | 1000 | 0.0070 | 0.0571 |
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| 0.05 | 11.8227 | 1200 | 0.0029 | 0.0545 |
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| 0.0467 | 13.7931 | 1400 | 0.0049 | 0.0531 |
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| 0.0369 | 15.7635 | 1600 | 0.0051 | 0.0593 |
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| 0.0267 | 17.7340 | 1800 | 0.0016 | 0.0529 |
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| 0.0297 | 19.7044 | 2000 | 0.0010 | 0.0581 |
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| 0.0276 | 21.6749 | 2200 | 0.0041 | 0.0579 |
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| 0.0211 | 23.6453 | 2400 | 0.0020 | 0.0525 |
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| 0.0324 | 25.6158 | 2600 | 0.0091 | 0.0551 |
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| 0.0223 | 27.5862 | 2800 | 0.0013 | 0.0539 |
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| 0.0182 | 29.5567 | 3000 | 0.0026 | 0.0551 |
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| 0.0167 | 31.5271 | 3200 | 0.0010 | 0.0551 |
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| 0.0173 | 33.4975 | 3400 | 0.0007 | 0.0518 |
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| 0.0178 | 35.4680 | 3600 | 0.0012 | 0.0510 |
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| 0.0172 | 37.4384 | 3800 | 0.0008 | 0.0514 |
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| 0.0138 | 39.4089 | 4000 | 0.0006 | 0.0504 |
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| 0.0122 | 41.3793 | 4200 | 0.0002 | 0.0512 |
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| 0.0134 | 43.3498 | 4400 | 0.0003 | 0.0514 |
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| 0.0129 | 45.3202 | 4600 | 0.0003 | 0.0512 |
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| 0.0075 | 47.2906 | 4800 | 0.0001 | 0.0512 |
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| 0.0067 | 49.2611 | 5000 | 0.0001 | 0.0545 |
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| 0.0083 | 51.2315 | 5200 | 0.0003 | 0.0527 |
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| 0.0067 | 53.2020 | 5400 | 0.0001 | 0.0525 |
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| 0.0065 | 55.1724 | 5600 | 0.0004 | 0.0523 |
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| 0.0073 | 57.1429 | 5800 | 0.0000 | 0.0504 |
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| 0.0051 | 59.1133 | 6000 | 0.0001 | 0.0510 |
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| 0.0077 | 61.0837 | 6200 | 0.0006 | 0.0510 |
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| 0.0069 | 63.0542 | 6400 | 0.0006 | 0.0510 |
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| 0.0058 | 65.0246 | 6600 | 0.0001 | 0.0506 |
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| 0.0044 | 66.9951 | 6800 | 0.0003 | 0.0508 |
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| 0.0046 | 68.9655 | 7000 | 0.0000 | 0.0506 |
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| 0.0049 | 70.9360 | 7200 | 0.0000 | 0.0508 |
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| 0.0035 | 72.9064 | 7400 | 0.0001 | 0.0520 |
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| 0.0022 | 74.8768 | 7600 | 0.0000 | 0.0527 |
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| 0.0039 | 76.8473 | 7800 | 0.0000 | 0.0518 |
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| 0.0033 | 78.8177 | 8000 | 0.0000 | 0.0516 |
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| 0.0028 | 80.7882 | 8200 | 0.0000 | 0.0506 |
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| 0.0024 | 82.7586 | 8400 | 0.0000 | 0.0510 |
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| 0.0016 | 84.7291 | 8600 | 0.0000 | 0.0508 |
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| 0.0017 | 86.6995 | 8800 | 0.0000 | 0.0506 |
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| 0.002 | 88.6700 | 9000 | 0.0000 | 0.0512 |
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| 0.0021 | 90.6404 | 9200 | 0.0001 | 0.0510 |
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| 0.0014 | 92.6108 | 9400 | 0.0001 | 0.0508 |
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| 0.0016 | 94.5813 | 9600 | 0.0000 | 0.0510 |
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| 0.0011 | 96.5517 | 9800 | 0.0000 | 0.0510 |
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| 0.001 | 98.5222 | 10000 | 0.0000 | 0.0510 |
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### Framework versions
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- Transformers 4.45.0.dev0
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- Pytorch 2.1.2
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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