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--- |
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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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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: xlsr-he-adap-de |
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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_17_0 |
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type: common_voice_17_0 |
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config: he |
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split: validation |
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args: he |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5332994407727504 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-hebrew/runs/qt2xs8yp) |
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# xlsr-he-adap-de |
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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 the common_voice_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1481 |
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- Wer: 0.5333 |
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- Cer: 0.1968 |
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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: 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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 50 |
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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 | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:------:|:------:| |
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| 3.697 | 0.8368 | 100 | 3.7808 | 1.0 | 1.0 | |
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| 3.1842 | 1.6736 | 200 | 3.5119 | 1.0 | 1.0 | |
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| 3.3472 | 2.5105 | 300 | 3.4405 | 1.0 | 1.0 | |
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| 1.6876 | 3.3473 | 400 | 2.0791 | 0.9664 | 0.5128 | |
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| 1.1828 | 4.1841 | 500 | 1.5367 | 0.8851 | 0.3903 | |
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| 0.9151 | 5.0209 | 600 | 1.2217 | 0.8210 | 0.3806 | |
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| 0.7117 | 5.8577 | 700 | 1.0726 | 0.7824 | 0.3500 | |
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| 0.809 | 6.6946 | 800 | 1.1018 | 0.8094 | 0.3451 | |
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| 0.9377 | 7.5314 | 900 | 0.9955 | 0.7438 | 0.3255 | |
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| 0.5836 | 8.3682 | 1000 | 0.9658 | 0.7605 | 0.3209 | |
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| 0.5226 | 9.2050 | 1100 | 0.9701 | 0.7316 | 0.3125 | |
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| 0.4732 | 10.0418 | 1200 | 0.9576 | 0.7636 | 0.2993 | |
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| 0.5439 | 10.8787 | 1300 | 0.9689 | 0.7743 | 0.2976 | |
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| 0.3479 | 11.7155 | 1400 | 1.0207 | 0.7026 | 0.2813 | |
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| 0.4111 | 12.5523 | 1500 | 1.0051 | 0.6873 | 0.2725 | |
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| 0.2865 | 13.3891 | 1600 | 0.9566 | 0.7087 | 0.2716 | |
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| 0.3942 | 14.2259 | 1700 | 1.0009 | 0.6929 | 0.2730 | |
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| 0.3058 | 15.0628 | 1800 | 0.9195 | 0.6695 | 0.2583 | |
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| 0.2141 | 15.8996 | 1900 | 0.9707 | 0.6523 | 0.2532 | |
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| 0.4893 | 16.7364 | 2000 | 1.0019 | 0.6772 | 0.2548 | |
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| 0.2922 | 17.5732 | 2100 | 1.0317 | 0.6721 | 0.2645 | |
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| 0.3056 | 18.4100 | 2200 | 1.0440 | 0.6385 | 0.2595 | |
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| 0.3616 | 19.2469 | 2300 | 1.1057 | 0.6406 | 0.2516 | |
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| 0.271 | 20.0837 | 2400 | 1.1302 | 0.6411 | 0.2532 | |
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| 0.2183 | 20.9205 | 2500 | 1.2060 | 0.6050 | 0.2513 | |
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| 0.3128 | 21.7573 | 2600 | 1.1261 | 0.6436 | 0.2522 | |
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| 0.1602 | 22.5941 | 2700 | 1.1014 | 0.6141 | 0.2394 | |
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| 0.2255 | 23.4310 | 2800 | 1.2601 | 0.6009 | 0.2480 | |
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| 0.3142 | 24.2678 | 2900 | 1.0729 | 0.6151 | 0.2410 | |
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| 0.1815 | 25.1046 | 3000 | 1.0396 | 0.6111 | 0.2314 | |
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| 0.2507 | 25.9414 | 3100 | 1.1343 | 0.5760 | 0.2236 | |
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| 0.151 | 26.7782 | 3200 | 1.1477 | 0.6263 | 0.2382 | |
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| 0.1531 | 27.6151 | 3300 | 1.0935 | 0.5984 | 0.2281 | |
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| 0.1943 | 28.4519 | 3400 | 1.0250 | 0.5689 | 0.2150 | |
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| 0.2592 | 29.2887 | 3500 | 1.0309 | 0.5780 | 0.2115 | |
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| 0.2394 | 30.1255 | 3600 | 1.0363 | 0.5735 | 0.2176 | |
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| 0.2146 | 30.9623 | 3700 | 1.0521 | 0.5582 | 0.2098 | |
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| 0.1629 | 31.7992 | 3800 | 1.0586 | 0.5816 | 0.2116 | |
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| 0.099 | 32.6360 | 3900 | 1.0348 | 0.5643 | 0.2100 | |
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| 0.1748 | 33.4728 | 4000 | 1.0983 | 0.5841 | 0.2147 | |
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| 0.1143 | 34.3096 | 4100 | 1.0979 | 0.5567 | 0.2059 | |
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| 0.1364 | 35.1464 | 4200 | 1.1404 | 0.5663 | 0.2094 | |
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| 0.1552 | 35.9833 | 4300 | 1.0805 | 0.5628 | 0.2085 | |
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| 0.1121 | 36.8201 | 4400 | 1.1262 | 0.5628 | 0.2061 | |
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| 0.1051 | 37.6569 | 4500 | 1.1390 | 0.5425 | 0.2059 | |
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| 0.1384 | 38.4937 | 4600 | 1.1252 | 0.5394 | 0.2016 | |
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| 0.1268 | 39.3305 | 4700 | 1.1607 | 0.5552 | 0.2068 | |
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| 0.1233 | 40.1674 | 4800 | 1.1776 | 0.5618 | 0.2072 | |
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| 0.2489 | 41.0042 | 4900 | 1.1335 | 0.5399 | 0.1977 | |
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| 0.1468 | 41.8410 | 5000 | 1.1419 | 0.5404 | 0.1964 | |
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| 0.1148 | 42.6778 | 5100 | 1.1404 | 0.5455 | 0.2008 | |
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| 0.1415 | 43.5146 | 5200 | 1.1149 | 0.5425 | 0.2005 | |
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| 0.1358 | 44.3515 | 5300 | 1.1354 | 0.5430 | 0.2013 | |
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| 0.1231 | 45.1883 | 5400 | 1.1457 | 0.5374 | 0.1999 | |
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| 0.0898 | 46.0251 | 5500 | 1.1218 | 0.5343 | 0.1989 | |
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| 0.1271 | 46.8619 | 5600 | 1.1404 | 0.5353 | 0.1977 | |
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| 0.1467 | 47.6987 | 5700 | 1.1765 | 0.5318 | 0.1961 | |
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| 0.1757 | 48.5356 | 5800 | 1.1517 | 0.5292 | 0.1973 | |
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| 0.1471 | 49.3724 | 5900 | 1.1481 | 0.5333 | 0.1968 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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