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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-xls-r-300m-a-hebrew |
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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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# wav2vec2-xls-r-300m-a-hebrew |
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This model is a fine-tuned version of [imvladikon/wav2vec2-xls-r-300m-a-hebrew](https://huggingface.co/imvladikon/wav2vec2-xls-r-300m-a-hebrew) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4502 |
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- Wer: 0.2318 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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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### Training hyperparameters |
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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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- total_eval_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: 1000 |
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- num_epochs: 60.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| No log | 0.7 | 1000 | 0.5371 | 0.3811 | |
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| 1.3606 | 1.41 | 2000 | 0.5247 | 0.3902 | |
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| 1.3606 | 2.12 | 3000 | 0.5126 | 0.3859 | |
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| 1.3671 | 2.82 | 4000 | 0.5062 | 0.3828 | |
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| 1.3671 | 3.53 | 5000 | 0.4979 | 0.3672 | |
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| 1.3421 | 4.23 | 6000 | 0.4906 | 0.3816 | |
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| 1.3421 | 4.94 | 7000 | 0.4784 | 0.3651 | |
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| 1.328 | 5.64 | 8000 | 0.4810 | 0.3669 | |
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| 1.328 | 6.35 | 9000 | 0.4747 | 0.3597 | |
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| 1.3109 | 7.05 | 10000 | 0.4813 | 0.3808 | |
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| 1.3109 | 7.76 | 11000 | 0.4631 | 0.3561 | |
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| 1.2873 | 8.46 | 12000 | 0.4603 | 0.3431 | |
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| 1.2873 | 9.17 | 13000 | 0.4579 | 0.3533 | |
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| 1.2661 | 9.87 | 14000 | 0.4471 | 0.3365 | |
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| 1.2661 | 10.58 | 15000 | 0.4584 | 0.3437 | |
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| 1.249 | 11.28 | 16000 | 0.4461 | 0.3454 | |
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| 1.249 | 11.99 | 17000 | 0.4482 | 0.3367 | |
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| 1.2322 | 12.69 | 18000 | 0.4464 | 0.3335 | |
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| 1.2322 | 13.4 | 19000 | 0.4427 | 0.3454 | |
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| 1.22 | 14.1 | 20000 | 0.4440 | 0.3395 | |
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| 1.22 | 14.81 | 21000 | 0.4459 | 0.3378 | |
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| 1.2044 | 15.51 | 22000 | 0.4406 | 0.3199 | |
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| 1.2044 | 16.22 | 23000 | 0.4398 | 0.3155 | |
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| 1.1913 | 16.92 | 24000 | 0.4237 | 0.3150 | |
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| 1.1913 | 17.63 | 25000 | 0.4287 | 0.3279 | |
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| 1.1705 | 18.34 | 26000 | 0.4253 | 0.3103 | |
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| 1.1705 | 19.04 | 27000 | 0.4234 | 0.3098 | |
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| 1.1564 | 19.75 | 28000 | 0.4174 | 0.3076 | |
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| 1.1564 | 20.45 | 29000 | 0.4260 | 0.3160 | |
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| 1.1461 | 21.16 | 30000 | 0.4235 | 0.3036 | |
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| 1.1461 | 21.86 | 31000 | 0.4309 | 0.3055 | |
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| 1.1285 | 22.57 | 32000 | 0.4264 | 0.3006 | |
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| 1.1285 | 23.27 | 33000 | 0.4201 | 0.2880 | |
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| 1.1135 | 23.98 | 34000 | 0.4131 | 0.2975 | |
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| 1.1135 | 24.68 | 35000 | 0.4202 | 0.2849 | |
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| 1.0968 | 25.39 | 36000 | 0.4105 | 0.2888 | |
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| 1.0968 | 26.09 | 37000 | 0.4210 | 0.2834 | |
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| 1.087 | 26.8 | 38000 | 0.4123 | 0.2843 | |
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| 1.087 | 27.5 | 39000 | 0.4216 | 0.2803 | |
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| 1.0707 | 28.21 | 40000 | 0.4161 | 0.2787 | |
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| 1.0707 | 28.91 | 41000 | 0.4186 | 0.2740 | |
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| 1.0575 | 29.62 | 42000 | 0.4118 | 0.2845 | |
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| 1.0575 | 30.32 | 43000 | 0.4243 | 0.2773 | |
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| 1.0474 | 31.03 | 44000 | 0.4221 | 0.2707 | |
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| 1.0474 | 31.73 | 45000 | 0.4138 | 0.2700 | |
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| 1.0333 | 32.44 | 46000 | 0.4102 | 0.2638 | |
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| 1.0333 | 33.15 | 47000 | 0.4162 | 0.2650 | |
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| 1.0191 | 33.85 | 48000 | 0.4155 | 0.2636 | |
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| 1.0191 | 34.56 | 49000 | 0.4129 | 0.2656 | |
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| 1.0087 | 35.26 | 50000 | 0.4157 | 0.2632 | |
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| 1.0087 | 35.97 | 51000 | 0.4090 | 0.2654 | |
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| 0.9901 | 36.67 | 52000 | 0.4183 | 0.2587 | |
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| 0.9901 | 37.38 | 53000 | 0.4251 | 0.2648 | |
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| 0.9795 | 38.08 | 54000 | 0.4229 | 0.2555 | |
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| 0.9795 | 38.79 | 55000 | 0.4176 | 0.2546 | |
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| 0.9644 | 39.49 | 56000 | 0.4223 | 0.2513 | |
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| 0.9644 | 40.2 | 57000 | 0.4244 | 0.2530 | |
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| 0.9534 | 40.9 | 58000 | 0.4175 | 0.2538 | |
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| 0.9534 | 41.61 | 59000 | 0.4213 | 0.2505 | |
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| 0.9397 | 42.31 | 60000 | 0.4275 | 0.2565 | |
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| 0.9397 | 43.02 | 61000 | 0.4315 | 0.2528 | |
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| 0.9269 | 43.72 | 62000 | 0.4316 | 0.2501 | |
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| 0.9269 | 44.43 | 63000 | 0.4247 | 0.2471 | |
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| 0.9175 | 45.13 | 64000 | 0.4376 | 0.2469 | |
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| 0.9175 | 45.84 | 65000 | 0.4335 | 0.2450 | |
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| 0.9026 | 46.54 | 66000 | 0.4336 | 0.2452 | |
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| 0.9026 | 47.25 | 67000 | 0.4400 | 0.2427 | |
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| 0.8929 | 47.95 | 68000 | 0.4382 | 0.2429 | |
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| 0.8929 | 48.66 | 69000 | 0.4361 | 0.2415 | |
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| 0.8786 | 49.37 | 70000 | 0.4413 | 0.2398 | |
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| 0.8786 | 50.07 | 71000 | 0.4392 | 0.2415 | |
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| 0.8714 | 50.78 | 72000 | 0.4345 | 0.2406 | |
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| 0.8714 | 51.48 | 73000 | 0.4475 | 0.2402 | |
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| 0.8589 | 52.19 | 74000 | 0.4473 | 0.2374 | |
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| 0.8589 | 52.89 | 75000 | 0.4457 | 0.2357 | |
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| 0.8493 | 53.6 | 76000 | 0.4462 | 0.2366 | |
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| 0.8493 | 54.3 | 77000 | 0.4494 | 0.2356 | |
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| 0.8395 | 55.01 | 78000 | 0.4472 | 0.2352 | |
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| 0.8395 | 55.71 | 79000 | 0.4490 | 0.2339 | |
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| 0.8295 | 56.42 | 80000 | 0.4489 | 0.2318 | |
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| 0.8295 | 57.12 | 81000 | 0.4469 | 0.2320 | |
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| 0.8225 | 57.83 | 82000 | 0.4478 | 0.2321 | |
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| 0.8225 | 58.53 | 83000 | 0.4525 | 0.2326 | |
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| 0.816 | 59.24 | 84000 | 0.4532 | 0.2316 | |
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| 0.816 | 59.94 | 85000 | 0.4502 | 0.2318 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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