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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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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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[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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#### 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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## 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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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-studio
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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-studio
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1587
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- Wer: 0.1157
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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: 5e-05
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- train_batch_size: 16
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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: 32
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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: 500
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- num_epochs: 10
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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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| 1.0335 | 0.4932 | 600 | 0.3654 | 0.4387 |
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| 0.1531 | 0.9864 | 1200 | 0.2373 | 0.3332 |
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| 0.1074 | 1.4797 | 1800 | 0.2069 | 0.2953 |
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| 0.0928 | 1.9729 | 2400 | 0.2146 | 0.2814 |
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| 0.0734 | 2.4661 | 3000 | 0.1947 | 0.2433 |
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| 0.0678 | 2.9593 | 3600 | 0.1938 | 0.2406 |
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| 0.0522 | 3.4525 | 4200 | 0.1566 | 0.2053 |
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| 0.0493 | 3.9457 | 4800 | 0.1649 | 0.1988 |
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| 0.0366 | 4.4390 | 5400 | 0.1417 | 0.1834 |
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| 0.0372 | 4.9322 | 6000 | 0.1542 | 0.1749 |
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| 0.028 | 5.4254 | 6600 | 0.1476 | 0.1620 |
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| 0.0263 | 5.9186 | 7200 | 0.1388 | 0.1622 |
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| 0.0195 | 6.4118 | 7800 | 0.1384 | 0.1495 |
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| 0.0185 | 6.9051 | 8400 | 0.1351 | 0.1383 |
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| 0.0136 | 7.3983 | 9000 | 0.1404 | 0.1344 |
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| 0.0119 | 7.8915 | 9600 | 0.1253 | 0.1276 |
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| 0.0087 | 8.3847 | 10200 | 0.1443 | 0.1284 |
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| 0.0066 | 8.8779 | 10800 | 0.1475 | 0.1252 |
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| 0.0049 | 9.3711 | 11400 | 0.1577 | 0.1227 |
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| 0.0038 | 9.8644 | 12000 | 0.1587 | 0.1157 |
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### Framework versions
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- Transformers 4.42.2
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- Pytorch 2.1.1+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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