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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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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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- ## Evaluation
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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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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- ## Environmental Impact
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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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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Glossary [optional]
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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-lg-cv-5hr-v1
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+ results: []
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  ---
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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-lg-cv-5hr-v1
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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: 2.8566
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+ - Model Preparation Time: 0.0165
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+ - Wer: 0.9775
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+ - Cer: 0.8923
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+
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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: cosine
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+ - lr_scheduler_warmup_ratio: 0.01
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+ - num_epochs: 100
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer | Cer |
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+ |:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:------:|
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+ | 9.9607 | 0.9948 | 95 | 6.8754 | 0.0165 | 1.0 | 1.0 |
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+ | 5.2586 | 2.0 | 191 | 4.0569 | 0.0165 | 1.0 | 1.0 |
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+ | 3.4197 | 2.9948 | 286 | 3.0508 | 0.0165 | 1.0 | 1.0 |
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+ | 2.9792 | 4.0 | 382 | 2.9586 | 0.0165 | 1.0 | 1.0 |
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+ | 2.9646 | 4.9948 | 477 | 2.9354 | 0.0165 | 1.0 | 1.0 |
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+ | 2.9169 | 6.0 | 573 | 2.9220 | 0.0165 | 1.0 | 1.0 |
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+ | 2.9372 | 6.9948 | 668 | 2.9116 | 0.0165 | 1.0 | 1.0 |
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+ | 2.8971 | 8.0 | 764 | 2.8998 | 0.0165 | 1.0 | 0.9811 |
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+ | 2.918 | 8.9948 | 859 | 2.8893 | 0.0165 | 0.9983 | 0.9652 |
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+ | 2.8795 | 10.0 | 955 | 2.8804 | 0.0165 | 0.9985 | 0.9534 |
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+ | 2.9006 | 10.9948 | 1050 | 2.8683 | 0.0165 | 1.0 | 0.9048 |
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+ | 2.8598 | 12.0 | 1146 | 2.8554 | 0.0165 | 1.0 | 0.9067 |
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+ | 2.8776 | 12.9948 | 1241 | 2.8417 | 0.0165 | 1.0 | 0.8954 |
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+ | 2.8393 | 14.0 | 1337 | 2.8407 | 0.0165 | 0.9970 | 0.9074 |
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+ | 2.8637 | 14.9948 | 1432 | 2.8304 | 0.0165 | 0.9787 | 0.8824 |
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+ | 2.8264 | 16.0 | 1528 | 2.8257 | 0.0165 | 0.9776 | 0.8934 |
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+ | 2.846 | 16.9948 | 1623 | 2.8045 | 0.0165 | 1.0 | 0.8653 |
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+ | 2.8001 | 18.0 | 1719 | 2.7907 | 0.0165 | 1.0022 | 0.8459 |
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+ | 2.8103 | 18.9948 | 1814 | 2.7686 | 0.0165 | 0.9991 | 0.8579 |
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+ | 2.7683 | 20.0 | 1910 | 2.7518 | 0.0165 | 0.9991 | 0.8534 |
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+ | 2.7903 | 20.9948 | 2005 | 2.7481 | 0.0165 | 0.9980 | 0.8568 |
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+ | 2.7561 | 22.0 | 2101 | 2.7468 | 0.0165 | 0.9991 | 0.8478 |
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+ | 2.782 | 22.9948 | 2196 | 2.7383 | 0.0165 | 0.9978 | 0.8497 |
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+ | 2.7473 | 24.0 | 2292 | 2.7345 | 0.0165 | 0.9993 | 0.8492 |
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+ | 2.771 | 24.9948 | 2387 | 2.7175 | 0.0165 | 0.9970 | 0.8258 |
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+ | 2.7049 | 26.0 | 2483 | 2.6822 | 0.0165 | 1.0260 | 0.7733 |
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+ ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.20.0
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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