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README.md
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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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#### 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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**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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## 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-2.0-tamil-gpu-custom_preprocessed_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-2.0-tamil-gpu-custom_preprocessed_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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: inf
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- Wer: 0.4310
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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: 4.53567e-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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| 3.2473 | 0.24 | 300 | inf | 0.4771 |
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| 0.7121 | 0.49 | 600 | inf | 0.3487 |
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| 0.552 | 0.73 | 900 | inf | 0.3140 |
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| 0.4973 | 0.97 | 1200 | inf | 0.3202 |
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| 0.499 | 1.22 | 1500 | inf | 0.2678 |
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| 0.4667 | 1.46 | 1800 | inf | 0.2784 |
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| 0.5909 | 1.71 | 2100 | inf | 0.3930 |
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| 1.411 | 1.95 | 2400 | inf | 0.3839 |
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| 2.1124 | 2.19 | 2700 | inf | 0.4063 |
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| 2.2398 | 2.44 | 3000 | inf | 0.4310 |
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| 2.3058 | 2.68 | 3300 | inf | 0.4310 |
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| 2.262 | 2.92 | 3600 | inf | 0.4310 |
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| 2.2588 | 3.17 | 3900 | inf | 0.4310 |
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| 2.3649 | 3.41 | 4200 | inf | 0.4310 |
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| 2.2835 | 3.66 | 4500 | inf | 0.4310 |
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| 2.3228 | 3.9 | 4800 | inf | 0.4310 |
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| 2.2322 | 4.14 | 5100 | inf | 0.4310 |
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| 2.3131 | 4.39 | 5400 | inf | 0.4310 |
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| 2.2916 | 4.63 | 5700 | inf | 0.4310 |
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| 2.3239 | 4.87 | 6000 | inf | 0.4310 |
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| 2.3533 | 5.12 | 6300 | inf | 0.4310 |
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| 2.2787 | 5.36 | 6600 | inf | 0.4310 |
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| 2.2776 | 5.61 | 6900 | inf | 0.4310 |
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| 2.3143 | 5.85 | 7200 | inf | 0.4310 |
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| 2.3105 | 6.09 | 7500 | inf | 0.4310 |
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| 2.2639 | 6.34 | 7800 | inf | 0.4310 |
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| 2.3211 | 6.58 | 8100 | inf | 0.4310 |
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| 2.2755 | 6.82 | 8400 | inf | 0.4310 |
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| 2.3074 | 7.07 | 8700 | inf | 0.4310 |
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| 2.2627 | 7.31 | 9000 | inf | 0.4310 |
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| 2.2756 | 7.55 | 9300 | inf | 0.4310 |
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| 2.2594 | 7.8 | 9600 | inf | 0.4310 |
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| 2.2221 | 8.04 | 9900 | inf | 0.4310 |
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| 2.2932 | 8.29 | 10200 | inf | 0.4310 |
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| 2.2978 | 8.53 | 10500 | inf | 0.4310 |
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| 2.2958 | 8.77 | 10800 | inf | 0.4310 |
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| 2.3239 | 9.02 | 11100 | inf | 0.4310 |
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| 2.281 | 9.26 | 11400 | inf | 0.4310 |
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| 2.272 | 9.5 | 11700 | inf | 0.4310 |
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| 2.2544 | 9.75 | 12000 | inf | 0.4310 |
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| 2.3103 | 9.99 | 12300 | inf | 0.4310 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.1.2+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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runs/Apr30_23-59-59_GPU/events.out.tfevents.1714502216.GPU.220052.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:8480a26a6afce9162fa4d49b8cd905a59a54afb09afa0da7e03d24e0d9adcdca
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size 27985
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