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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-odia_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# w2v-bert-odia_v2
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.
It achieves the following results on the evaluation set:
- Loss: 0.2548
- Wer: 0.1898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 2.9674 | 0.0342 | 300 | 1.3305 | 0.7001 |
| 1.2476 | 0.0683 | 600 | 1.1660 | 0.5879 |
| 1.0692 | 0.1025 | 900 | 0.9110 | 0.4886 |
| 0.9443 | 0.1366 | 1200 | 0.7601 | 0.4727 |
| 0.8235 | 0.1708 | 1500 | 0.7761 | 0.3973 |
| 0.8155 | 0.2050 | 1800 | 0.7084 | 0.4022 |
| 0.767 | 0.2391 | 2100 | 0.6251 | 0.3756 |
| 0.7517 | 0.2733 | 2400 | 0.6125 | 0.3654 |
| 0.687 | 0.3075 | 2700 | 0.5848 | 0.3439 |
| 0.6509 | 0.3416 | 3000 | 0.5643 | 0.3282 |
| 0.6632 | 0.3758 | 3300 | 0.5509 | 0.3199 |
| 0.6108 | 0.4099 | 3600 | 0.5393 | 0.3341 |
| 0.5898 | 0.4441 | 3900 | 0.5223 | 0.3277 |
| 0.595 | 0.4783 | 4200 | 0.5199 | 0.3200 |
| 0.5644 | 0.5124 | 4500 | 0.5508 | 0.2919 |
| 0.5787 | 0.5466 | 4800 | 0.4994 | 0.3060 |
| 0.5752 | 0.5807 | 5100 | 0.4966 | 0.2997 |
| 0.5353 | 0.6149 | 5400 | 0.4731 | 0.3237 |
| 0.5473 | 0.6491 | 5700 | 0.4665 | 0.3062 |
| 0.5498 | 0.6832 | 6000 | 0.4890 | 0.2876 |
| 0.5146 | 0.7174 | 6300 | 0.4747 | 0.2926 |
| 0.5398 | 0.7516 | 6600 | 0.4581 | 0.2907 |
| 0.5154 | 0.7857 | 6900 | 0.4557 | 0.2995 |
| 0.5386 | 0.8199 | 7200 | 0.4515 | 0.2948 |
| 0.5037 | 0.8540 | 7500 | 0.4456 | 0.2961 |
| 0.5344 | 0.8882 | 7800 | 0.4509 | 0.2988 |
| 0.501 | 0.9224 | 8100 | 0.4436 | 0.2711 |
| 0.487 | 0.9565 | 8400 | 0.4233 | 0.2749 |
| 0.4692 | 0.9907 | 8700 | 0.4661 | 0.2532 |
| 0.462 | 1.0249 | 9000 | 0.4197 | 0.2723 |
| 0.4508 | 1.0590 | 9300 | 0.4316 | 0.2584 |
| 0.4702 | 1.0932 | 9600 | 0.4148 | 0.2689 |
| 0.4517 | 1.1273 | 9900 | 0.3950 | 0.2549 |
| 0.4408 | 1.1615 | 10200 | 0.4308 | 0.2551 |
| 0.4636 | 1.1957 | 10500 | 0.4033 | 0.2700 |
| 0.4583 | 1.2298 | 10800 | 0.4096 | 0.2556 |
| 0.4315 | 1.2640 | 11100 | 0.3883 | 0.2681 |
| 0.4172 | 1.2981 | 11400 | 0.3737 | 0.2529 |
| 0.4177 | 1.3323 | 11700 | 0.3992 | 0.2472 |
| 0.3975 | 1.3665 | 12000 | 0.3716 | 0.2485 |
| 0.4044 | 1.4006 | 12300 | 0.3853 | 0.2523 |
| 0.4497 | 1.4348 | 12600 | 0.3798 | 0.2465 |
| 0.4188 | 1.4690 | 12900 | 0.3822 | 0.2494 |
| 0.4424 | 1.5031 | 13200 | 0.3560 | 0.2449 |
| 0.4249 | 1.5373 | 13500 | 0.3630 | 0.2514 |
| 0.4287 | 1.5714 | 13800 | 0.3662 | 0.2417 |
| 0.3712 | 1.6056 | 14100 | 0.3714 | 0.2562 |
| 0.3893 | 1.6398 | 14400 | 0.3711 | 0.2333 |
| 0.3935 | 1.6739 | 14700 | 0.3715 | 0.2413 |
| 0.3982 | 1.7081 | 15000 | 0.3551 | 0.2482 |
| 0.4124 | 1.7422 | 15300 | 0.3519 | 0.2412 |
| 0.3853 | 1.7764 | 15600 | 0.3429 | 0.2418 |
| 0.4096 | 1.8106 | 15900 | 0.3407 | 0.2394 |
| 0.3816 | 1.8447 | 16200 | 0.3607 | 0.2370 |
| 0.3769 | 1.8789 | 16500 | 0.3601 | 0.2291 |
| 0.3428 | 1.9131 | 16800 | 0.3578 | 0.2283 |
| 0.3636 | 1.9472 | 17100 | 0.3485 | 0.2334 |
| 0.3594 | 1.9814 | 17400 | 0.3539 | 0.2341 |
| 0.3692 | 2.0155 | 17700 | 0.3383 | 0.2282 |
| 0.3295 | 2.0497 | 18000 | 0.3354 | 0.2374 |
| 0.3442 | 2.0839 | 18300 | 0.3393 | 0.2340 |
| 0.3306 | 2.1180 | 18600 | 0.3567 | 0.2382 |
| 0.3243 | 2.1522 | 18900 | 0.3410 | 0.2287 |
| 0.3426 | 2.1864 | 19200 | 0.3244 | 0.2323 |
| 0.3552 | 2.2205 | 19500 | 0.3356 | 0.2318 |
| 0.3558 | 2.2547 | 19800 | 0.3686 | 0.2225 |
| 0.3485 | 2.2888 | 20100 | 0.3485 | 0.2230 |
| 0.3195 | 2.3230 | 20400 | 0.3197 | 0.2230 |
| 0.3145 | 2.3572 | 20700 | 0.3312 | 0.2294 |
| 0.3238 | 2.3913 | 21000 | 0.3331 | 0.2210 |
| 0.3288 | 2.4255 | 21300 | 0.3172 | 0.2272 |
| 0.3398 | 2.4596 | 21600 | 0.3228 | 0.2182 |
| 0.3185 | 2.4940 | 21900 | 0.3057 | 0.2272 |
| 0.3152 | 2.5281 | 22200 | 0.3133 | 0.2175 |
| 0.312 | 2.5623 | 22500 | 0.3155 | 0.2155 |
| 0.3131 | 2.5965 | 22800 | 0.3087 | 0.2200 |
| 0.2993 | 2.6306 | 23100 | 0.3123 | 0.2216 |
| 0.2953 | 2.6648 | 23400 | 0.3116 | 0.2203 |
| 0.274 | 2.6989 | 23700 | 0.3221 | 0.2099 |
| 0.3043 | 2.7331 | 24000 | 0.3092 | 0.2131 |
| 0.2939 | 2.7673 | 24300 | 0.3084 | 0.2134 |
| 0.3063 | 2.8014 | 24600 | 0.3119 | 0.2094 |
| 0.3108 | 2.8356 | 24900 | 0.2987 | 0.2104 |
| 0.3188 | 2.8698 | 25200 | 0.3030 | 0.2082 |
| 0.2921 | 2.9039 | 25500 | 0.3051 | 0.2090 |
| 0.2994 | 2.9381 | 25800 | 0.2939 | 0.2148 |
| 0.2789 | 2.9722 | 26100 | 0.3012 | 0.2068 |
| 0.2902 | 3.0064 | 26400 | 0.2981 | 0.2138 |
| 0.2899 | 3.0406 | 26700 | 0.2931 | 0.2062 |
| 0.2796 | 3.0747 | 27000 | 0.2953 | 0.2067 |
| 0.287 | 3.1089 | 27300 | 0.3006 | 0.2105 |
| 0.2828 | 3.1431 | 27600 | 0.2916 | 0.2121 |
| 0.2798 | 3.1772 | 27900 | 0.2974 | 0.2060 |
| 0.2757 | 3.2114 | 28200 | 0.2908 | 0.2042 |
| 0.2694 | 3.2455 | 28500 | 0.2905 | 0.2058 |
| 0.262 | 3.2797 | 28800 | 0.2866 | 0.2048 |
| 0.2623 | 3.3139 | 29100 | 0.2794 | 0.2062 |
| 0.282 | 3.3480 | 29400 | 0.2814 | 0.2004 |
| 0.2655 | 3.3822 | 29700 | 0.2891 | 0.2006 |
| 0.2757 | 3.4163 | 30000 | 0.2845 | 0.1983 |
| 0.2686 | 3.4505 | 30300 | 0.2818 | 0.2013 |
| 0.2571 | 3.4847 | 30600 | 0.2825 | 0.2003 |
| 0.2681 | 3.5188 | 30900 | 0.2814 | 0.2051 |
| 0.2628 | 3.5530 | 31200 | 0.2831 | 0.1998 |
| 0.2625 | 3.5872 | 31500 | 0.2775 | 0.2032 |
| 0.2448 | 3.6213 | 31800 | 0.2770 | 0.1984 |
| 0.2599 | 3.6555 | 32100 | 0.2732 | 0.2002 |
| 0.2492 | 3.6896 | 32400 | 0.2880 | 0.1942 |
| 0.2666 | 3.7238 | 32700 | 0.2701 | 0.1984 |
| 0.257 | 3.7580 | 33000 | 0.2687 | 0.1997 |
| 0.2589 | 3.7921 | 33300 | 0.2665 | 0.1997 |
| 0.2735 | 3.8263 | 33600 | 0.2678 | 0.1990 |
| 0.2477 | 3.8604 | 33900 | 0.2704 | 0.1958 |
| 0.2525 | 3.8946 | 34200 | 0.2695 | 0.1946 |
| 0.2401 | 3.9288 | 34500 | 0.2732 | 0.1931 |
| 0.2585 | 3.9629 | 34800 | 0.2682 | 0.1945 |
| 0.2568 | 3.9972 | 35100 | 0.2857 | 0.2078 |
| 0.2682 | 4.0313 | 35400 | 0.3001 | 0.2073 |
| 0.2727 | 4.0655 | 35700 | 0.2817 | 0.2129 |
| 0.2849 | 4.0997 | 36000 | 0.2932 | 0.2050 |
| 0.2863 | 4.1338 | 36300 | 0.2903 | 0.2051 |
| 0.2706 | 4.1680 | 36600 | 0.2835 | 0.2050 |
| 0.2745 | 4.2022 | 36900 | 0.2865 | 0.2048 |
| 0.2676 | 4.2363 | 37200 | 0.2835 | 0.2042 |
| 0.2694 | 4.2705 | 37500 | 0.2882 | 0.2092 |
| 0.2708 | 4.3046 | 37800 | 0.2783 | 0.2063 |
| 0.2635 | 4.3388 | 38100 | 0.2898 | 0.2088 |
| 0.2647 | 4.3730 | 38400 | 0.3015 | 0.2062 |
| 0.2558 | 4.4071 | 38700 | 0.2848 | 0.2046 |
| 0.2821 | 4.4413 | 39000 | 0.2769 | 0.2036 |
| 0.2625 | 4.4754 | 39300 | 0.2910 | 0.2012 |
| 0.2861 | 4.5096 | 39600 | 0.2875 | 0.2046 |
| 0.2619 | 4.5438 | 39900 | 0.2810 | 0.2011 |
| 0.2561 | 4.5779 | 40200 | 0.2769 | 0.2037 |
| 0.2571 | 4.6121 | 40500 | 0.2824 | 0.2074 |
| 0.2629 | 4.6463 | 40800 | 0.2743 | 0.2032 |
| 0.2752 | 4.6804 | 41100 | 0.2804 | 0.1982 |
| 0.2625 | 4.7146 | 41400 | 0.2803 | 0.1979 |
| 0.2661 | 4.7487 | 41700 | 0.2794 | 0.2027 |
| 0.2681 | 4.7829 | 42000 | 0.2731 | 0.1972 |
| 0.2586 | 4.8171 | 42300 | 0.2734 | 0.1953 |
| 0.2742 | 4.8512 | 42600 | 0.2655 | 0.1992 |
| 0.259 | 4.8854 | 42900 | 0.2787 | 0.1958 |
| 0.2485 | 4.9195 | 43200 | 0.2759 | 0.1949 |
| 0.2654 | 4.9537 | 43500 | 0.2662 | 0.1983 |
| 0.2581 | 4.9879 | 43800 | 0.2776 | 0.1921 |
| 0.2363 | 5.0220 | 44100 | 0.2676 | 0.1970 |
| 0.2517 | 5.0562 | 44400 | 0.2663 | 0.1988 |
| 0.2308 | 5.0904 | 44700 | 0.2683 | 0.1975 |
| 0.2406 | 5.1245 | 45000 | 0.2707 | 0.1958 |
| 0.2286 | 5.1587 | 45300 | 0.2637 | 0.2022 |
| 0.235 | 5.1928 | 45600 | 0.2684 | 0.1947 |
| 0.2334 | 5.2270 | 45900 | 0.2722 | 0.1964 |
| 0.2369 | 5.2612 | 46200 | 0.2760 | 0.1972 |
| 0.2275 | 5.2953 | 46500 | 0.2647 | 0.1950 |
| 0.2363 | 5.3295 | 46800 | 0.2673 | 0.1972 |
| 0.2353 | 5.3637 | 47100 | 0.2846 | 0.1912 |
| 0.2414 | 5.3978 | 47400 | 0.2610 | 0.1967 |
| 0.2377 | 5.4320 | 47700 | 0.2607 | 0.1941 |
| 0.2398 | 5.4661 | 48000 | 0.2623 | 0.1949 |
| 0.2202 | 5.5003 | 48300 | 0.2677 | 0.1957 |
| 0.2235 | 5.5345 | 48600 | 0.2637 | 0.1915 |
| 0.2288 | 5.5686 | 48900 | 0.2615 | 0.1935 |
| 0.2348 | 5.6028 | 49200 | 0.2568 | 0.1971 |
| 0.236 | 5.6369 | 49500 | 0.2594 | 0.1930 |
| 0.2235 | 5.6711 | 49800 | 0.2660 | 0.1898 |
| 0.2349 | 5.7053 | 50100 | 0.2563 | 0.1919 |
| 0.2186 | 5.7394 | 50400 | 0.2631 | 0.1904 |
| 0.2368 | 5.7736 | 50700 | 0.2579 | 0.1906 |
| 0.2453 | 5.8078 | 51000 | 0.2556 | 0.1906 |
| 0.2238 | 5.8419 | 51300 | 0.2581 | 0.1884 |
| 0.2305 | 5.8761 | 51600 | 0.2576 | 0.1888 |
| 0.2249 | 5.9102 | 51900 | 0.2548 | 0.1908 |
| 0.2346 | 5.9444 | 52200 | 0.2544 | 0.1902 |
| 0.237 | 5.9786 | 52500 | 0.2548 | 0.1898 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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