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library_name: transformers
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---
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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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[More Information Needed]
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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-2.0-CV_Fleurs-lg-50hrs-v5
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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-2.0-CV_Fleurs-lg-50hrs-v5
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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: 0.3755
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- Wer: 0.3108
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- Cer: 0.0651
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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: 4
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- eval_batch_size: 2
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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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- lr_scheduler_warmup_ratio: 0.1
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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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| 0.8769 | 1.0 | 6320 | 0.3126 | 0.3631 | 0.0763 |
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| 0.2352 | 2.0 | 12640 | 0.2544 | 0.3542 | 0.0705 |
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| 0.2161 | 3.0 | 18960 | 0.2639 | 0.3415 | 0.0695 |
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| 0.2088 | 4.0 | 25280 | 0.2666 | 0.3524 | 0.0745 |
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| 0.2063 | 5.0 | 31600 | 0.2863 | 0.3655 | 0.0789 |
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| 0.2043 | 6.0 | 37920 | 0.2792 | 0.3409 | 0.0700 |
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| 0.2036 | 7.0 | 44240 | 0.2787 | 0.3519 | 0.0736 |
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| 0.2051 | 8.0 | 50560 | 0.2774 | 0.3550 | 0.0746 |
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| 0.1967 | 9.0 | 56880 | 0.2710 | 0.3457 | 0.0728 |
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| 0.1754 | 10.0 | 63200 | 0.2714 | 0.3425 | 0.0721 |
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| 0.157 | 11.0 | 69520 | 0.2800 | 0.3490 | 0.0727 |
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| 0.1411 | 12.0 | 75840 | 0.2571 | 0.3165 | 0.0671 |
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| 0.1305 | 13.0 | 82160 | 0.2768 | 0.3486 | 0.0726 |
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| 0.1164 | 14.0 | 88480 | 0.2963 | 0.3330 | 0.0718 |
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| 0.1067 | 15.0 | 94800 | 0.2663 | 0.3131 | 0.0670 |
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| 0.0954 | 16.0 | 101120 | 0.2660 | 0.3254 | 0.0667 |
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| 0.0849 | 17.0 | 107440 | 0.2751 | 0.3103 | 0.0659 |
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| 0.0769 | 18.0 | 113760 | 0.2721 | 0.3290 | 0.0695 |
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| 0.0675 | 19.0 | 120080 | 0.2986 | 0.3148 | 0.0670 |
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| 0.0606 | 20.0 | 126400 | 0.2850 | 0.3122 | 0.0653 |
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| 0.0536 | 21.0 | 132720 | 0.2987 | 0.3260 | 0.0687 |
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| 0.0478 | 22.0 | 139040 | 0.3226 | 0.3191 | 0.0654 |
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| 0.0429 | 23.0 | 145360 | 0.2981 | 0.3373 | 0.0678 |
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| 0.038 | 24.0 | 151680 | 0.3210 | 0.3172 | 0.0656 |
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| 0.0343 | 25.0 | 158000 | 0.3454 | 0.3056 | 0.0635 |
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| 0.0311 | 26.0 | 164320 | 0.3092 | 0.3153 | 0.0655 |
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| 0.0283 | 27.0 | 170640 | 0.3285 | 0.3165 | 0.0647 |
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| 0.0265 | 28.0 | 176960 | 0.3413 | 0.3125 | 0.0650 |
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| 0.024 | 29.0 | 183280 | 0.3894 | 0.3062 | 0.0636 |
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| 0.0223 | 30.0 | 189600 | 0.3681 | 0.3084 | 0.0645 |
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| 0.0205 | 31.0 | 195920 | 0.3552 | 0.3134 | 0.0655 |
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| 0.0188 | 32.0 | 202240 | 0.3656 | 0.3105 | 0.0661 |
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| 0.018 | 33.0 | 208560 | 0.3640 | 0.3148 | 0.0659 |
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| 0.0163 | 34.0 | 214880 | 0.3805 | 0.3099 | 0.0649 |
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| 0.0153 | 35.0 | 221200 | 0.3755 | 0.3108 | 0.0651 |
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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 2423040060
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version https://git-lfs.github.com/spec/v1
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oid sha256:3b697a87b3ec226f4a922617c1d38ba1505281652e84973b85f7b77096865543
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size 2423040060
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