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wav2vec2-large-xls-r-300m-lg-cv-1hr-vr

Automatic Speech Recognition
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wav2vec2
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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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- 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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- ## How to Get Started with the Model
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- #### Preprocessing [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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  ---
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  library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-xls-r-300m
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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: wav2vec2-large-xls-r-300m-lg-cv-1hr-vr
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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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+ # wav2vec2-large-xls-r-300m-lg-cv-1hr-vr
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.4686
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+ - Model Preparation Time: 0.0074
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+ - Wer: 0.9247
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+ - Cer: 0.2572
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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: 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: 150
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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 | Model Preparation Time | Wer | Cer |
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+ |:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:------:|
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+ | 15.4113 | 1.0 | 18 | 13.3832 | 0.0074 | 1.0 | 1.0 |
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+ | 8.4071 | 2.0 | 36 | 5.0022 | 0.0074 | 1.0 | 1.0 |
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+ | 4.3781 | 3.0 | 54 | 3.8902 | 0.0074 | 1.0 | 1.0 |
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+ | 3.7341 | 4.0 | 72 | 3.5371 | 0.0074 | 1.0 | 1.0 |
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+ | 3.4503 | 5.0 | 90 | 3.3256 | 0.0074 | 1.0 | 1.0 |
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+ | 3.268 | 6.0 | 108 | 3.1846 | 0.0074 | 1.0 | 1.0 |
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+ | 3.1464 | 7.0 | 126 | 3.0891 | 0.0074 | 1.0 | 1.0 |
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+ | 3.0601 | 8.0 | 144 | 3.0319 | 0.0074 | 1.0 | 1.0 |
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+ | 3.0119 | 9.0 | 162 | 2.9856 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9756 | 10.0 | 180 | 2.9687 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9535 | 11.0 | 198 | 2.9495 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9376 | 12.0 | 216 | 2.9651 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9256 | 13.0 | 234 | 2.9529 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9159 | 14.0 | 252 | 2.9260 | 0.0074 | 1.0 | 1.0 |
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+ | 2.9046 | 15.0 | 270 | 2.9180 | 0.0074 | 1.0 | 1.0 |
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+ | 2.8973 | 16.0 | 288 | 2.9079 | 0.0074 | 1.0 | 1.0 |
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+ | 2.8891 | 17.0 | 306 | 2.9027 | 0.0074 | 1.0 | 0.9906 |
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+ | 2.8793 | 18.0 | 324 | 2.8907 | 0.0074 | 1.0 | 0.9882 |
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+ | 2.8616 | 19.0 | 342 | 2.8788 | 0.0074 | 1.0 | 0.9625 |
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+ | 2.8416 | 20.0 | 360 | 2.8412 | 0.0074 | 1.0 | 0.9559 |
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+ | 2.7586 | 21.0 | 378 | 2.7118 | 0.0074 | 1.0 | 0.8715 |
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+ | 2.6084 | 22.0 | 396 | 2.5263 | 0.0074 | 1.0 | 0.8093 |
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+ | 2.4068 | 23.0 | 414 | 2.3225 | 0.0074 | 1.0005 | 0.7330 |
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+ | 2.1542 | 24.0 | 432 | 2.0424 | 0.0074 | 1.0025 | 0.5160 |
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+ | 1.8099 | 25.0 | 450 | 1.7306 | 0.0074 | 1.0203 | 0.4231 |
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+ | 1.464 | 26.0 | 468 | 1.4744 | 0.0074 | 0.9682 | 0.3198 |
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+ | 1.1766 | 27.0 | 486 | 1.3177 | 0.0074 | 0.9476 | 0.2827 |
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+ | 0.9688 | 28.0 | 504 | 1.2492 | 0.0074 | 0.9482 | 0.2704 |
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+ | 0.8301 | 29.0 | 522 | 1.2105 | 0.0074 | 0.9837 | 0.2689 |
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+ | 0.7473 | 30.0 | 540 | 1.1642 | 0.0074 | 0.9340 | 0.2571 |
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+ | 0.6502 | 31.0 | 558 | 1.1645 | 0.0074 | 0.9358 | 0.2542 |
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+ | 0.5898 | 32.0 | 576 | 1.1443 | 0.0074 | 0.9389 | 0.2534 |
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+ | 0.5306 | 33.0 | 594 | 1.1486 | 0.0074 | 0.9363 | 0.2589 |
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+ | 0.5048 | 34.0 | 612 | 1.1764 | 0.0074 | 0.9586 | 0.2548 |
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+ | 0.4661 | 35.0 | 630 | 1.1654 | 0.0074 | 0.9414 | 0.2595 |
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+ | 0.441 | 36.0 | 648 | 1.1358 | 0.0074 | 0.9401 | 0.2481 |
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+ | 0.4155 | 37.0 | 666 | 1.1788 | 0.0074 | 0.9776 | 0.2563 |
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+ | 0.3924 | 38.0 | 684 | 1.1449 | 0.0074 | 0.9477 | 0.2517 |
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+ | 0.3733 | 39.0 | 702 | 1.1898 | 0.0074 | 0.9537 | 0.2516 |
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+ | 0.3603 | 40.0 | 720 | 1.1532 | 0.0074 | 0.9327 | 0.2468 |
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+ | 0.3459 | 41.0 | 738 | 1.1934 | 0.0074 | 0.9692 | 0.2614 |
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+ | 0.3238 | 42.0 | 756 | 1.1646 | 0.0074 | 0.9384 | 0.2512 |
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+ | 0.3156 | 43.0 | 774 | 1.1860 | 0.0074 | 0.9586 | 0.2570 |
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+ | 0.3081 | 44.0 | 792 | 1.2123 | 0.0074 | 0.9340 | 0.2545 |
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+ | 0.2857 | 45.0 | 810 | 1.2501 | 0.0074 | 0.9499 | 0.2566 |
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+ | 0.2776 | 46.0 | 828 | 1.1802 | 0.0074 | 0.9281 | 0.2545 |
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+ | 0.2702 | 47.0 | 846 | 1.2074 | 0.0074 | 0.9286 | 0.2567 |
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+ | 0.2693 | 48.0 | 864 | 1.1991 | 0.0074 | 0.9628 | 0.2490 |
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+ | 0.2467 | 49.0 | 882 | 1.2253 | 0.0074 | 0.9289 | 0.2538 |
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+ | 0.2453 | 50.0 | 900 | 1.2126 | 0.0074 | 0.9335 | 0.2506 |
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+ | 0.2455 | 51.0 | 918 | 1.1971 | 0.0074 | 0.9383 | 0.2502 |
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+ | 0.2353 | 52.0 | 936 | 1.2120 | 0.0074 | 0.9175 | 0.2400 |
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+ | 0.2242 | 53.0 | 954 | 1.1997 | 0.0074 | 0.9272 | 0.2405 |
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+ | 0.2142 | 54.0 | 972 | 1.2027 | 0.0074 | 0.9284 | 0.2449 |
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+ | 0.2166 | 55.0 | 990 | 1.2304 | 0.0074 | 0.9251 | 0.2489 |
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+ | 0.209 | 56.0 | 1008 | 1.2486 | 0.0074 | 0.9472 | 0.2487 |
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+ | 0.1989 | 57.0 | 1026 | 1.2205 | 0.0074 | 0.9301 | 0.2456 |
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+ | 0.1988 | 58.0 | 1044 | 1.2305 | 0.0074 | 0.9208 | 0.2428 |
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+ | 0.1951 | 59.0 | 1062 | 1.2508 | 0.0074 | 0.9402 | 0.2449 |
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+ | 0.1906 | 60.0 | 1080 | 1.2638 | 0.0074 | 0.9333 | 0.2474 |
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+ | 0.1869 | 61.0 | 1098 | 1.2625 | 0.0074 | 0.9249 | 0.2438 |
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+ | 0.1812 | 62.0 | 1116 | 1.2536 | 0.0074 | 0.9170 | 0.2473 |
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+ | 0.1822 | 63.0 | 1134 | 1.2459 | 0.0074 | 0.9315 | 0.2434 |
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+ | 0.1732 | 64.0 | 1152 | 1.2313 | 0.0074 | 0.9128 | 0.2435 |
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+ | 0.1723 | 65.0 | 1170 | 1.2817 | 0.0074 | 0.9130 | 0.2497 |
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+ | 0.1748 | 66.0 | 1188 | 1.2766 | 0.0074 | 0.9093 | 0.2388 |
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+ | 0.1667 | 67.0 | 1206 | 1.2803 | 0.0074 | 0.9271 | 0.2498 |
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+ | 0.1652 | 68.0 | 1224 | 1.2886 | 0.0074 | 0.9312 | 0.2439 |
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+ | 0.1566 | 69.0 | 1242 | 1.2814 | 0.0074 | 0.9104 | 0.2414 |
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+ | 0.1512 | 70.0 | 1260 | 1.2557 | 0.0074 | 0.9226 | 0.2432 |
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+ | 0.1455 | 71.0 | 1278 | 1.2875 | 0.0074 | 0.9178 | 0.2455 |
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+ | 0.1548 | 72.0 | 1296 | 1.3325 | 0.0074 | 0.9661 | 0.2468 |
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+ | 0.158 | 73.0 | 1314 | 1.2506 | 0.0074 | 0.9303 | 0.2474 |
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+ | 0.1579 | 74.0 | 1332 | 1.2974 | 0.0074 | 0.9341 | 0.2465 |
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+ | 0.1492 | 75.0 | 1350 | 1.2893 | 0.0074 | 0.9199 | 0.2490 |
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+ | 0.1471 | 76.0 | 1368 | 1.3097 | 0.0074 | 0.9194 | 0.2466 |
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+ | 0.1454 | 77.0 | 1386 | 1.3065 | 0.0074 | 0.9099 | 0.2428 |
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+ | 0.1456 | 78.0 | 1404 | 1.2618 | 0.0074 | 0.9074 | 0.2406 |
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+ | 0.1515 | 79.0 | 1422 | 1.3326 | 0.0074 | 0.9213 | 0.2503 |
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+ | 0.1452 | 80.0 | 1440 | 1.3146 | 0.0074 | 0.9082 | 0.2384 |
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+ | 0.1326 | 81.0 | 1458 | 1.3145 | 0.0074 | 0.9089 | 0.2412 |
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+ | 0.1368 | 82.0 | 1476 | 1.2851 | 0.0074 | 0.9039 | 0.2433 |
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+ | 0.1314 | 83.0 | 1494 | 1.3079 | 0.0074 | 0.9099 | 0.2410 |
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+ | 0.1272 | 84.0 | 1512 | 1.3156 | 0.0074 | 0.9081 | 0.2399 |
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+ | 0.1359 | 85.0 | 1530 | 1.3318 | 0.0074 | 0.9235 | 0.2435 |
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+ | 0.1288 | 86.0 | 1548 | 1.3015 | 0.0074 | 0.9057 | 0.2390 |
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+ | 0.1243 | 87.0 | 1566 | 1.3271 | 0.0074 | 0.9111 | 0.2405 |
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+ | 0.1334 | 88.0 | 1584 | 1.3117 | 0.0074 | 0.9120 | 0.2410 |
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+ | 0.1248 | 89.0 | 1602 | 1.3005 | 0.0074 | 0.9193 | 0.2436 |
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+ | 0.1163 | 90.0 | 1620 | 1.3611 | 0.0074 | 0.9195 | 0.2416 |
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+ | 0.1233 | 91.0 | 1638 | 1.3070 | 0.0074 | 0.9117 | 0.2416 |
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+ | 0.1264 | 92.0 | 1656 | 1.3325 | 0.0074 | 0.9109 | 0.2384 |
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+ | 0.1249 | 93.0 | 1674 | 1.3528 | 0.0074 | 0.9150 | 0.2411 |
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+ | 0.1189 | 94.0 | 1692 | 1.3673 | 0.0074 | 0.9134 | 0.2434 |
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+ | 0.1197 | 95.0 | 1710 | 1.3391 | 0.0074 | 0.9097 | 0.2449 |
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+ | 0.1212 | 96.0 | 1728 | 1.3404 | 0.0074 | 0.9178 | 0.2400 |
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+ | 0.1168 | 97.0 | 1746 | 1.3651 | 0.0074 | 0.9153 | 0.2408 |
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+ | 0.1133 | 98.0 | 1764 | 1.3744 | 0.0074 | 0.9236 | 0.2430 |
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+ | 0.113 | 99.0 | 1782 | 1.3383 | 0.0074 | 0.9000 | 0.2358 |
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+ | 0.1244 | 100.0 | 1800 | 1.3553 | 0.0074 | 0.9028 | 0.2356 |
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+ | 0.1116 | 101.0 | 1818 | 1.3768 | 0.0074 | 0.9136 | 0.2392 |
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+ | 0.1078 | 102.0 | 1836 | 1.3629 | 0.0074 | 0.9116 | 0.2409 |
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+ | 0.1117 | 103.0 | 1854 | 1.3564 | 0.0074 | 0.9276 | 0.2410 |
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+ | 0.1063 | 104.0 | 1872 | 1.3593 | 0.0074 | 0.9146 | 0.2395 |
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+ | 0.1083 | 105.0 | 1890 | 1.3516 | 0.0074 | 0.9053 | 0.2406 |
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+ | 0.1096 | 106.0 | 1908 | 1.3819 | 0.0074 | 0.9338 | 0.2439 |
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+ | 0.1013 | 107.0 | 1926 | 1.3928 | 0.0074 | 0.9266 | 0.2433 |
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+ | 0.1123 | 108.0 | 1944 | 1.3754 | 0.0074 | 0.9200 | 0.2422 |
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+ | 0.1107 | 109.0 | 1962 | 1.3677 | 0.0074 | 0.9108 | 0.2401 |
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+ | 0.1061 | 110.0 | 1980 | 1.3938 | 0.0074 | 0.9139 | 0.2397 |
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+ | 0.1034 | 111.0 | 1998 | 1.3845 | 0.0074 | 0.9349 | 0.2457 |
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+ | 0.1042 | 112.0 | 2016 | 1.3915 | 0.0074 | 0.9184 | 0.2426 |
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+ | 0.1077 | 113.0 | 2034 | 1.3786 | 0.0074 | 0.9124 | 0.2421 |
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+ | 0.1038 | 115.0 | 2070 | 1.3943 | 0.0074 | 0.9266 | 0.2447 |
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+ | 0.0981 | 116.0 | 2088 | 1.3904 | 0.0074 | 0.9224 | 0.2438 |
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+ | 0.0978 | 118.0 | 2124 | 1.4044 | 0.0074 | 0.9184 | 0.2427 |
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+ | 0.0999 | 119.0 | 2142 | 1.3869 | 0.0074 | 0.9257 | 0.2449 |
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+ | 0.1005 | 120.0 | 2160 | 1.3821 | 0.0074 | 0.9192 | 0.2440 |
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+ | 0.0948 | 122.0 | 2196 | 1.3892 | 0.0074 | 0.9229 | 0.2458 |
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+ | 0.0968 | 123.0 | 2214 | 1.4042 | 0.0074 | 0.9192 | 0.2449 |
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+ | 0.0897 | 124.0 | 2232 | 1.4130 | 0.0074 | 0.9172 | 0.2448 |
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+ | 0.1031 | 125.0 | 2250 | 1.4104 | 0.0074 | 0.9189 | 0.2431 |
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+ | 0.094 | 126.0 | 2268 | 1.4125 | 0.0074 | 0.9182 | 0.2434 |
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+ | 0.0897 | 127.0 | 2286 | 1.4098 | 0.0074 | 0.9135 | 0.2436 |
186
+ | 0.1005 | 128.0 | 2304 | 1.4042 | 0.0074 | 0.9157 | 0.2432 |
187
+ | 0.0923 | 129.0 | 2322 | 1.4027 | 0.0074 | 0.9175 | 0.2429 |
188
+ | 0.0994 | 130.0 | 2340 | 1.4018 | 0.0074 | 0.9202 | 0.2442 |
189
+ | 0.1006 | 131.0 | 2358 | 1.4007 | 0.0074 | 0.9138 | 0.2429 |
190
+ | 0.0927 | 132.0 | 2376 | 1.4001 | 0.0074 | 0.9160 | 0.2424 |
191
+ | 0.0907 | 133.0 | 2394 | 1.4063 | 0.0074 | 0.9150 | 0.2416 |
192
+ | 0.0964 | 134.0 | 2412 | 1.4093 | 0.0074 | 0.9133 | 0.2421 |
193
+ | 0.0923 | 135.0 | 2430 | 1.4123 | 0.0074 | 0.9146 | 0.2423 |
194
+ | 0.0991 | 136.0 | 2448 | 1.4134 | 0.0074 | 0.9147 | 0.2432 |
195
+ | 0.093 | 137.0 | 2466 | 1.4116 | 0.0074 | 0.9133 | 0.2427 |
196
+ | 0.1007 | 138.0 | 2484 | 1.4096 | 0.0074 | 0.9141 | 0.2427 |
197
+ | 0.0921 | 139.0 | 2502 | 1.4086 | 0.0074 | 0.9145 | 0.2426 |
198
+ | 0.0929 | 140.0 | 2520 | 1.4094 | 0.0074 | 0.9142 | 0.2422 |
199
+ | 0.0996 | 141.0 | 2538 | 1.4108 | 0.0074 | 0.9162 | 0.2426 |
200
+ | 0.0925 | 142.0 | 2556 | 1.4120 | 0.0074 | 0.9164 | 0.2425 |
201
+ | 0.0928 | 143.0 | 2574 | 1.4127 | 0.0074 | 0.9166 | 0.2424 |
202
+ | 0.0899 | 144.0 | 2592 | 1.4129 | 0.0074 | 0.9168 | 0.2426 |
203
+ | 0.0921 | 145.0 | 2610 | 1.4128 | 0.0074 | 0.9161 | 0.2425 |
204
+ | 0.0942 | 146.0 | 2628 | 1.4129 | 0.0074 | 0.9169 | 0.2426 |
205
+ | 0.0915 | 147.0 | 2646 | 1.4128 | 0.0074 | 0.9169 | 0.2429 |
206
+ | 0.0876 | 148.0 | 2664 | 1.4127 | 0.0074 | 0.9170 | 0.2427 |
207
+ | 0.0942 | 149.0 | 2682 | 1.4127 | 0.0074 | 0.9172 | 0.2427 |
208
+ | 0.0864 | 150.0 | 2700 | 1.4127 | 0.0074 | 0.9169 | 0.2427 |
209
+
210
+
211
+ ### Framework versions
212
+
213
+ - Transformers 4.44.2
214
+ - Pytorch 2.1.0+cu118
215
+ - Datasets 2.20.0
216
+ - Tokenizers 0.19.1
model.safetensors CHANGED
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