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update model card README.md

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@@ -14,8 +14,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2278
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- - Wer: 0.2379
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  ## Model description
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@@ -48,35 +48,35 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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- | 1.5239 | 1.0 | 500 | 0.3262 | 0.2950 |
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- | 0.655 | 2.01 | 1000 | 0.2197 | 0.2649 |
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- | 0.4153 | 3.01 | 1500 | 0.2049 | 0.2569 |
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- | 0.3449 | 4.02 | 2000 | 0.2035 | 0.2562 |
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- | 0.2926 | 5.02 | 2500 | 0.1979 | 0.2519 |
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- | 0.2597 | 6.02 | 3000 | 0.2197 | 0.2561 |
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- | 0.2337 | 7.03 | 3500 | 0.2711 | 0.2525 |
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- | 0.1829 | 8.03 | 4000 | 0.2875 | 0.2571 |
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- | 0.1608 | 9.04 | 4500 | 0.2398 | 0.2614 |
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- | 0.1902 | 10.04 | 5000 | 0.2146 | 0.2571 |
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- | 0.1367 | 11.04 | 5500 | 0.2411 | 0.2533 |
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- | 0.1465 | 12.05 | 6000 | 0.2193 | 0.2565 |
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- | 0.1244 | 13.05 | 6500 | 0.2626 | 0.2488 |
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- | 0.124 | 14.06 | 7000 | 0.2575 | 0.2507 |
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- | 0.111 | 15.06 | 7500 | 0.3106 | 0.2452 |
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- | 0.1036 | 16.06 | 8000 | 0.2525 | 0.2480 |
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- | 0.0989 | 17.07 | 8500 | 0.2693 | 0.2429 |
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- | 0.1122 | 18.07 | 9000 | 0.2569 | 0.2474 |
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- | 0.091 | 19.08 | 9500 | 0.2477 | 0.2432 |
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- | 0.0837 | 20.08 | 10000 | 0.2646 | 0.2432 |
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- | 0.097 | 21.08 | 10500 | 0.2547 | 0.2441 |
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- | 0.077 | 22.09 | 11000 | 0.2668 | 0.2417 |
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- | 0.0643 | 23.09 | 11500 | 0.2357 | 0.2425 |
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- | 0.069 | 24.1 | 12000 | 0.2372 | 0.2409 |
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- | 0.0598 | 25.1 | 12500 | 0.2338 | 0.2435 |
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- | 0.0615 | 26.1 | 13000 | 0.2369 | 0.2411 |
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- | 0.0762 | 27.11 | 13500 | 0.2294 | 0.2415 |
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- | 0.0598 | 28.11 | 14000 | 0.2284 | 0.2393 |
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- | 0.055 | 29.12 | 14500 | 0.2278 | 0.2379 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2188
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+ - Wer: 0.2310
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 5.6272 | 1.0 | 500 | 4.1587 | 1.0 |
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+ | 2.6402 | 2.01 | 1000 | 0.3609 | 0.3751 |
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+ | 0.6464 | 3.01 | 1500 | 0.2678 | 0.2673 |
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+ | 0.5417 | 4.02 | 2000 | 0.2225 | 0.2591 |
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+ | 0.3863 | 5.02 | 2500 | 0.2243 | 0.2735 |
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+ | 0.3231 | 6.02 | 3000 | 0.2294 | 0.2626 |
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+ | 0.3165 | 7.03 | 3500 | 0.2287 | 0.2594 |
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+ | 0.2057 | 8.03 | 4000 | 0.2325 | 0.2582 |
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+ | 0.1966 | 9.04 | 4500 | 0.2180 | 0.2608 |
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+ | 0.1604 | 10.04 | 5000 | 0.2000 | 0.2458 |
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+ | 0.1923 | 11.04 | 5500 | 0.2375 | 0.2573 |
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+ | 0.1657 | 12.05 | 6000 | 0.2533 | 0.2696 |
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+ | 0.1621 | 13.05 | 6500 | 0.2522 | 0.2529 |
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+ | 0.1285 | 14.06 | 7000 | 0.2275 | 0.2573 |
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+ | 0.1254 | 15.06 | 7500 | 0.2319 | 0.2482 |
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+ | 0.1074 | 16.06 | 8000 | 0.2331 | 0.2465 |
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+ | 0.147 | 17.07 | 8500 | 0.2290 | 0.2470 |
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+ | 0.1028 | 18.07 | 9000 | 0.2315 | 0.2507 |
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+ | 0.0916 | 19.08 | 9500 | 0.2456 | 0.2496 |
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+ | 0.0918 | 20.08 | 10000 | 0.2361 | 0.2467 |
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+ | 0.0889 | 21.08 | 10500 | 0.2291 | 0.2422 |
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+ | 0.0721 | 22.09 | 11000 | 0.2452 | 0.2425 |
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+ | 0.0767 | 23.09 | 11500 | 0.2366 | 0.2403 |
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+ | 0.0779 | 24.1 | 12000 | 0.2310 | 0.2400 |
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+ | 0.06 | 25.1 | 12500 | 0.2213 | 0.2344 |
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+ | 0.0657 | 26.1 | 13000 | 0.2211 | 0.2322 |
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+ | 0.0644 | 27.11 | 13500 | 0.2228 | 0.2316 |
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+ | 0.0758 | 28.11 | 14000 | 0.2206 | 0.2325 |
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+ | 0.0624 | 29.12 | 14500 | 0.2188 | 0.2310 |
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  ### Framework versions