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  1. README.md +96 -195
  2. generation_config.json +175 -0
  3. model.safetensors +1 -1
README.md CHANGED
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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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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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-
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- ## Model Details
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-
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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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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-
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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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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-
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- ### Direct Use
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-
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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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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-
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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-
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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-
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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-
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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-
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- ## How to Get Started with the Model
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-
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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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-
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- ## Training Details
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-
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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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-
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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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-
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- #### Preprocessing [optional]
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- [More Information Needed]
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-
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- #### Training Hyperparameters
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-
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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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-
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- #### Speeds, Sizes, Times [optional]
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-
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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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-
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- ## Evaluation
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-
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- <!-- This section describes the evaluation protocols and provides the results. -->
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-
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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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-
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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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-
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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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- ## 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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-
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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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-
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- ## Technical Specifications [optional]
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-
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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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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- ## 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: openai/whisper-small
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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: whisper-ai-nose
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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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+
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+ # whisper-ai-nose
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+
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+ This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) 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: 14.3032
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 30
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 0.8578 | 0.8163 | 100 | 0.0827 | 9.4132 |
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+ | 0.2317 | 1.6327 | 200 | 0.0545 | 18.9487 |
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+ | 0.079 | 2.4490 | 300 | 0.0488 | 204.5232 |
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+ | 0.0586 | 3.2653 | 400 | 0.0493 | 227.9951 |
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+ | 0.0396 | 4.0816 | 500 | 0.0277 | 23.9609 |
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+ | 0.1368 | 4.8980 | 600 | 0.2826 | 94.6210 |
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+ | 0.1721 | 5.7143 | 700 | 0.0024 | 145.1100 |
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+ | 0.0246 | 6.5306 | 800 | 0.0024 | 22.9829 |
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+ | 0.0143 | 7.3469 | 900 | 0.0008 | 25.1834 |
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+ | 0.0185 | 8.1633 | 1000 | 0.0026 | 69.4377 |
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+ | 0.0171 | 8.9796 | 1100 | 0.0069 | 22.4939 |
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+ | 0.0229 | 9.7959 | 1200 | 0.0004 | 43.6430 |
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+ | 0.0033 | 10.6122 | 1300 | 0.0018 | 16.8704 |
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+ | 0.0073 | 11.4286 | 1400 | 0.0076 | 14.6699 |
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+ | 0.0053 | 12.2449 | 1500 | 0.0030 | 14.4254 |
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+ | 0.0038 | 13.0612 | 1600 | 0.0027 | 13.0807 |
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+ | 0.0004 | 13.8776 | 1700 | 0.0000 | 13.4474 |
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+ | 0.0008 | 14.6939 | 1800 | 0.0001 | 11.4914 |
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+ | 0.0014 | 15.5102 | 1900 | 0.0002 | 11.9804 |
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+ | 0.0047 | 16.3265 | 2000 | 0.0002 | 14.9144 |
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+ | 0.0031 | 17.1429 | 2100 | 0.0001 | 15.1589 |
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+ | 0.0018 | 17.9592 | 2200 | 0.0002 | 15.7702 |
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+ | 0.0019 | 18.7755 | 2300 | 0.0000 | 15.1589 |
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+ | 0.0006 | 19.5918 | 2400 | 0.0000 | 15.0367 |
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+ | 0.0001 | 20.4082 | 2500 | 0.0000 | 14.0587 |
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+ | 0.0001 | 21.2245 | 2600 | 0.0000 | 14.4254 |
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+ | 0.0 | 22.0408 | 2700 | 0.0000 | 14.1809 |
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+ | 0.0 | 22.8571 | 2800 | 0.0000 | 14.5477 |
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+ | 0.0 | 23.6735 | 2900 | 0.0000 | 14.4254 |
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+ | 0.0 | 24.4898 | 3000 | 0.0000 | 14.4254 |
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+ | 0.0 | 25.3061 | 3100 | 0.0000 | 14.4254 |
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+ | 0.0 | 26.1224 | 3200 | 0.0000 | 14.3032 |
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+ | 0.0 | 26.9388 | 3300 | 0.0000 | 14.3032 |
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+ | 0.0 | 27.7551 | 3400 | 0.0000 | 14.4254 |
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+ | 0.0 | 28.5714 | 3500 | 0.0000 | 14.3032 |
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+ | 0.0 | 29.3878 | 3600 | 0.0000 | 14.3032 |
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.0.dev0
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+ - Pytorch 2.4.0
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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