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wav2vec2-large-xlsr-sw-common-voice-16

Automatic Speech Recognition
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wav2vec2
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README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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- ## Model Details
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- ### Model Description
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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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- ## Bias, Risks, and Limitations
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- ## How to Get Started with the Model
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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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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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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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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- 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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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-large-xlsr-53
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - common_voice_16_0
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: wav2vec2-large-xlsr-sw-common-voice-16
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+ results:
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+ - task:
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+ name: Automatic Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: common_voice_16_0
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+ type: common_voice_16_0
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+ config: sw
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+ split: test
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+ args: sw
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.3082326604654753
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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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+ # wav2vec2-large-xlsr-sw-common-voice-16
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_16_0 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: 0.3082
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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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+ More information needed
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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: 0.0003
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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: 500
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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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+ | 5.8691 | 0.11 | 400 | inf | 1.0 |
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+ | 1.673 | 0.22 | 800 | inf | 0.7178 |
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+ | 0.6454 | 0.33 | 1200 | inf | 0.6274 |
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+ | 0.5527 | 0.44 | 1600 | inf | 0.5747 |
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+ | 0.4989 | 0.55 | 2000 | inf | 0.5174 |
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+ | 0.4827 | 0.66 | 2400 | inf | 0.5302 |
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+ | 0.4462 | 0.77 | 2800 | inf | 0.4916 |
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+ | 0.4374 | 0.88 | 3200 | inf | 0.4769 |
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+ | 0.4183 | 0.99 | 3600 | inf | 0.4687 |
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+ | 0.3854 | 1.1 | 4000 | inf | 0.4669 |
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+ | 0.3802 | 1.2 | 4400 | inf | 0.4513 |
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+ | 0.3727 | 1.31 | 4800 | inf | 0.4505 |
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+ | 0.3694 | 1.42 | 5200 | inf | 0.4405 |
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+ | 0.3709 | 1.53 | 5600 | inf | 0.4364 |
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+ | 0.363 | 1.64 | 6000 | inf | 0.4318 |
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+ | 0.3669 | 1.75 | 6400 | inf | 0.4398 |
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+ | 0.3597 | 1.86 | 6800 | inf | 0.4353 |
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+ | 0.3541 | 1.97 | 7200 | inf | 0.4251 |
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+ | 0.3277 | 2.08 | 7600 | inf | 0.4153 |
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+ | 0.3211 | 2.19 | 8000 | inf | 0.4178 |
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+ | 0.3225 | 2.3 | 8400 | inf | 0.4267 |
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+ | 0.3215 | 2.41 | 8800 | inf | 0.4139 |
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+ | 0.3224 | 2.52 | 9200 | inf | 0.4054 |
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+ | 0.3106 | 2.63 | 9600 | inf | 0.4155 |
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+ | 0.3141 | 2.74 | 10000 | inf | 0.4188 |
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+ | 0.3189 | 2.85 | 10400 | inf | 0.4036 |
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+ | 0.3213 | 2.96 | 10800 | inf | 0.4071 |
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+ | 0.3005 | 3.07 | 11200 | inf | 0.3954 |
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+ | 0.2872 | 3.18 | 11600 | inf | 0.3974 |
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+ | 0.2855 | 3.29 | 12000 | inf | 0.3982 |
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+ | 0.2898 | 3.39 | 12400 | inf | 0.3987 |
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+ | 0.288 | 3.5 | 12800 | inf | 0.4021 |
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+ | 0.2941 | 3.61 | 13200 | inf | 0.3955 |
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+ | 0.2951 | 3.72 | 13600 | inf | 0.4022 |
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+ | 0.2916 | 3.83 | 14000 | inf | 0.3960 |
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+ | 0.2896 | 3.94 | 14400 | inf | 0.3903 |
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+ | 0.2794 | 4.05 | 14800 | inf | 0.3918 |
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+ | 0.2707 | 4.16 | 15200 | inf | 0.3873 |
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+ | 0.2682 | 4.27 | 15600 | inf | 0.3927 |
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+ | 0.2654 | 4.38 | 16000 | inf | 0.3844 |
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+ | 0.2699 | 4.49 | 16400 | inf | 0.3909 |
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+ | 0.2723 | 4.6 | 16800 | inf | 0.3904 |
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+ | 0.2762 | 4.71 | 17200 | inf | 0.3857 |
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+ | 0.2621 | 4.82 | 17600 | inf | 0.3795 |
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+ | 0.26 | 4.93 | 18000 | inf | 0.3764 |
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+ | 0.2659 | 5.04 | 18400 | inf | 0.3842 |
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+ | 0.2479 | 5.15 | 18800 | inf | 0.3719 |
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+ | 0.2518 | 5.26 | 19200 | inf | 0.3822 |
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+ | 0.2591 | 5.37 | 19600 | inf | 0.3837 |
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+ | 0.2491 | 5.48 | 20000 | inf | 0.3871 |
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+ | 0.2466 | 5.59 | 20400 | inf | 0.3747 |
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+ | 0.2519 | 5.69 | 20800 | inf | 0.3788 |
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+ | 0.2516 | 5.8 | 21200 | inf | 0.3781 |
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+ | 0.2422 | 5.91 | 21600 | inf | 0.3751 |
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+ | 0.2439 | 6.02 | 22000 | inf | 0.3693 |
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+ | 0.2327 | 6.13 | 22400 | inf | 0.3752 |
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+ | 0.2327 | 6.24 | 22800 | inf | 0.3706 |
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+ | 0.2302 | 6.35 | 23200 | inf | 0.3687 |
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+ | 0.2313 | 6.46 | 23600 | inf | 0.3690 |
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+ | 0.2363 | 6.57 | 24000 | inf | 0.3686 |
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+ | 0.2329 | 6.68 | 24400 | inf | 0.3681 |
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+ | 0.2328 | 6.79 | 24800 | inf | 0.3626 |
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+ | 0.2329 | 6.9 | 25200 | inf | 0.3652 |
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+ | 0.2254 | 7.01 | 25600 | inf | 0.3606 |
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+ | 0.2124 | 7.12 | 26000 | inf | 0.3648 |
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+ | 0.2206 | 7.23 | 26400 | inf | 0.3686 |
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+ | 0.2151 | 7.34 | 26800 | inf | 0.3646 |
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+ | 0.2167 | 7.45 | 27200 | inf | 0.3630 |
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+ | 0.2196 | 7.56 | 27600 | inf | 0.3597 |
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+ | 0.2089 | 7.67 | 28000 | inf | 0.3561 |
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+ | 0.2183 | 7.78 | 28400 | inf | 0.3593 |
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+ | 0.2148 | 7.89 | 28800 | inf | 0.3580 |
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+ | 0.2232 | 7.99 | 29200 | inf | 0.3597 |
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+ | 0.2002 | 8.1 | 29600 | inf | 0.3581 |
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+ | 0.1924 | 8.21 | 30000 | inf | 0.3585 |
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+ | 0.2046 | 8.32 | 30400 | inf | 0.3606 |
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+ | 0.2057 | 8.43 | 30800 | inf | 0.3611 |
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+ | 0.2042 | 8.54 | 31200 | inf | 0.3618 |
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+ | 0.21 | 8.65 | 31600 | inf | 0.3599 |
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+ | 0.2076 | 8.76 | 32000 | inf | 0.3568 |
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+ | 0.208 | 8.87 | 32400 | inf | 0.3564 |
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+ | 0.2154 | 8.98 | 32800 | inf | 0.3566 |
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+ | 0.1991 | 9.09 | 33200 | inf | 0.3621 |
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+ | 0.1986 | 9.2 | 33600 | inf | 0.3571 |
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+ | 0.1898 | 9.31 | 34000 | inf | 0.3515 |
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+ | 0.1961 | 9.42 | 34400 | inf | 0.3559 |
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+ | 0.1947 | 9.53 | 34800 | inf | 0.3521 |
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+ | 0.1886 | 9.64 | 35200 | inf | 0.3500 |
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+ | 0.1901 | 9.75 | 35600 | inf | 0.3557 |
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+ | 0.1998 | 9.86 | 36000 | inf | 0.3547 |
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+ | 0.1873 | 9.97 | 36400 | inf | 0.3498 |
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+ | 0.1858 | 10.08 | 36800 | inf | 0.3552 |
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+ | 0.1804 | 10.18 | 37200 | inf | 0.3518 |
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+ | 0.18 | 10.29 | 37600 | inf | 0.3504 |
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+ | 0.1777 | 10.4 | 38000 | inf | 0.3532 |
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+ | 0.1777 | 10.51 | 38400 | inf | 0.3530 |
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+ | 0.1801 | 10.62 | 38800 | inf | 0.3515 |
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+ | 0.1839 | 10.73 | 39200 | inf | 0.3538 |
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+ | 0.1913 | 10.84 | 39600 | inf | 0.3554 |
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+ | 0.1909 | 10.95 | 40000 | inf | 0.3479 |
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+ | 0.1812 | 11.06 | 40400 | inf | 0.3467 |
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+ | 0.1664 | 11.17 | 40800 | inf | 0.3491 |
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+ | 0.175 | 11.28 | 41200 | inf | 0.3446 |
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+ | 0.1733 | 11.39 | 41600 | inf | 0.3464 |
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+ | 0.1709 | 11.5 | 42000 | inf | 0.3467 |
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+ | 0.1777 | 11.61 | 42400 | inf | 0.3469 |
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+ | 0.1735 | 11.72 | 42800 | inf | 0.3452 |
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+ | 0.1765 | 11.83 | 43200 | inf | 0.3471 |
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+ | 0.1738 | 11.94 | 43600 | inf | 0.3496 |
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+ | 0.1649 | 12.05 | 44000 | inf | 0.3445 |
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+ | 0.1601 | 12.16 | 44400 | inf | 0.3464 |
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+ | 0.1603 | 12.27 | 44800 | inf | 0.3416 |
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+ | 0.1634 | 12.38 | 45200 | inf | 0.3445 |
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+ | 0.1628 | 12.48 | 45600 | inf | 0.3452 |
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+ | 0.1621 | 12.59 | 46000 | inf | 0.3403 |
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+ | 0.1596 | 12.7 | 46400 | inf | 0.3394 |
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+ | 0.1589 | 12.81 | 46800 | inf | 0.3401 |
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+ | 0.1632 | 12.92 | 47200 | inf | 0.3403 |
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+ | 0.163 | 13.03 | 47600 | inf | 0.3429 |
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+ | 0.1297 | 16.43 | 60000 | inf | 0.3332 |
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+ | 0.1265 | 16.54 | 60400 | inf | 0.3313 |
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+ | 0.1271 | 16.65 | 60800 | inf | 0.3310 |
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+ | 0.1315 | 16.76 | 61200 | inf | 0.3307 |
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+ | 0.1271 | 16.87 | 61600 | inf | 0.3337 |
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+ | 0.1298 | 16.97 | 62000 | inf | 0.3318 |
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+ | 0.1211 | 17.08 | 62400 | inf | 0.3326 |
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+ | 0.1192 | 17.19 | 62800 | inf | 0.3290 |
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+ | 0.1232 | 17.3 | 63200 | inf | 0.3291 |
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+ | 0.1229 | 17.41 | 63600 | inf | 0.3349 |
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+ | 0.1162 | 17.52 | 64000 | inf | 0.3281 |
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+ | 0.1207 | 17.63 | 64400 | inf | 0.3308 |
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+ | 0.1179 | 17.74 | 64800 | inf | 0.3257 |
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+ | 0.1207 | 17.85 | 65200 | inf | 0.3290 |
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+ | 0.1256 | 17.96 | 65600 | inf | 0.3297 |
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+ | 0.119 | 18.07 | 66000 | inf | 0.3279 |
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+ | 0.1111 | 18.18 | 66400 | inf | 0.3302 |
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+ | 0.1086 | 18.29 | 66800 | inf | 0.3285 |
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+ | 0.1179 | 18.4 | 67200 | inf | 0.3274 |
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+ | 0.1099 | 18.51 | 67600 | inf | 0.3281 |
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+ | 0.1141 | 18.62 | 68000 | inf | 0.3281 |
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+ | 0.1091 | 18.73 | 68400 | inf | 0.3301 |
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+ | 0.1147 | 18.84 | 68800 | inf | 0.3270 |
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+ | 0.1158 | 18.95 | 69200 | inf | 0.3246 |
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+ | 0.1111 | 19.06 | 69600 | inf | 0.3227 |
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+ | 0.1075 | 19.16 | 70000 | inf | 0.3249 |
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+ | 0.1051 | 19.27 | 70400 | inf | 0.3253 |
247
+ | 0.1029 | 19.38 | 70800 | inf | 0.3252 |
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+ | 0.1039 | 19.49 | 71200 | inf | 0.3264 |
249
+ | 0.1063 | 19.6 | 71600 | inf | 0.3242 |
250
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251
+ | 0.1063 | 19.82 | 72400 | inf | 0.3248 |
252
+ | 0.1085 | 19.93 | 72800 | inf | 0.3247 |
253
+ | 0.1038 | 20.04 | 73200 | inf | 0.3242 |
254
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255
+ | 0.099 | 20.26 | 74000 | inf | 0.3247 |
256
+ | 0.0971 | 20.37 | 74400 | inf | 0.3258 |
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+ | 0.1002 | 20.48 | 74800 | inf | 0.3223 |
258
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+ | 0.0967 | 20.81 | 76000 | inf | 0.3215 |
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+ | 0.1008 | 20.92 | 76400 | inf | 0.3212 |
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+ | 0.0975 | 21.03 | 76800 | inf | 0.3191 |
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+ | 0.0893 | 21.14 | 77200 | inf | 0.3210 |
264
+ | 0.0911 | 21.25 | 77600 | inf | 0.3206 |
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+ | 0.0959 | 21.36 | 78000 | inf | 0.3211 |
266
+ | 0.094 | 21.46 | 78400 | inf | 0.3198 |
267
+ | 0.0939 | 21.57 | 78800 | inf | 0.3202 |
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+ | 0.0936 | 21.68 | 79200 | inf | 0.3202 |
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+ | 0.0938 | 21.79 | 79600 | inf | 0.3195 |
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+ | 0.0938 | 21.9 | 80000 | inf | 0.3184 |
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+ | 0.0916 | 22.01 | 80400 | inf | 0.3185 |
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+ | 0.0858 | 22.12 | 80800 | inf | 0.3177 |
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+ | 0.0909 | 22.23 | 81200 | inf | 0.3211 |
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275
+ | 0.088 | 22.45 | 82000 | inf | 0.3194 |
276
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+ | 0.0868 | 22.67 | 82800 | inf | 0.3174 |
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+ | 0.0871 | 22.78 | 83200 | inf | 0.3201 |
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289
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295
+ | 0.078 | 24.64 | 90000 | inf | 0.3145 |
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297
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298
+ | 0.0763 | 24.97 | 91200 | inf | 0.3144 |
299
+ | 0.0705 | 25.08 | 91600 | inf | 0.3138 |
300
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+ | 0.0743 | 25.41 | 92800 | inf | 0.3144 |
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+ | 0.0729 | 25.52 | 93200 | inf | 0.3133 |
304
+ | 0.071 | 25.63 | 93600 | inf | 0.3139 |
305
+ | 0.0764 | 25.74 | 94000 | inf | 0.3122 |
306
+ | 0.0726 | 25.85 | 94400 | inf | 0.3128 |
307
+ | 0.0714 | 25.95 | 94800 | inf | 0.3135 |
308
+ | 0.0725 | 26.06 | 95200 | inf | 0.3147 |
309
+ | 0.0711 | 26.17 | 95600 | inf | 0.3130 |
310
+ | 0.0684 | 26.28 | 96000 | inf | 0.3125 |
311
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312
+ | 0.0698 | 26.5 | 96800 | inf | 0.3135 |
313
+ | 0.0687 | 26.61 | 97200 | inf | 0.3131 |
314
+ | 0.0675 | 26.72 | 97600 | inf | 0.3119 |
315
+ | 0.0678 | 26.83 | 98000 | inf | 0.3105 |
316
+ | 0.0677 | 26.94 | 98400 | inf | 0.3102 |
317
+ | 0.068 | 27.05 | 98800 | inf | 0.3128 |
318
+ | 0.0694 | 27.16 | 99200 | inf | 0.3111 |
319
+ | 0.0681 | 27.27 | 99600 | inf | 0.3118 |
320
+ | 0.0656 | 27.38 | 100000 | inf | 0.3110 |
321
+ | 0.065 | 27.49 | 100400 | inf | 0.3113 |
322
+ | 0.0649 | 27.6 | 100800 | inf | 0.3113 |
323
+ | 0.0643 | 27.71 | 101200 | inf | 0.3107 |
324
+ | 0.0651 | 27.82 | 101600 | inf | 0.3102 |
325
+ | 0.0643 | 27.93 | 102000 | inf | 0.3109 |
326
+ | 0.063 | 28.04 | 102400 | inf | 0.3110 |
327
+ | 0.0604 | 28.15 | 102800 | inf | 0.3108 |
328
+ | 0.062 | 28.25 | 103200 | inf | 0.3110 |
329
+ | 0.0623 | 28.36 | 103600 | inf | 0.3106 |
330
+ | 0.063 | 28.47 | 104000 | inf | 0.3102 |
331
+ | 0.0619 | 28.58 | 104400 | inf | 0.3101 |
332
+ | 0.0636 | 28.69 | 104800 | inf | 0.3108 |
333
+ | 0.0636 | 28.8 | 105200 | inf | 0.3099 |
334
+ | 0.0643 | 28.91 | 105600 | inf | 0.3089 |
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+ | 0.0607 | 29.02 | 106000 | inf | 0.3094 |
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+ | 0.0597 | 29.13 | 106400 | inf | 0.3091 |
337
+ | 0.0616 | 29.24 | 106800 | inf | 0.3087 |
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+ | 0.0594 | 29.35 | 107200 | inf | 0.3087 |
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+ | 0.0614 | 29.46 | 107600 | inf | 0.3087 |
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+ | 0.06 | 29.9 | 109200 | inf | 0.3082 |
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+
345
+
346
+ ### Framework versions
347
+
348
+ - Transformers 4.37.1
349
+ - Pytorch 2.2.0+cu121
350
+ - Datasets 2.16.1
351
+ - Tokenizers 0.15.0
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