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--- |
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library_name: transformers |
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language: |
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- af |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- generated_from_trainer |
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datasets: |
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- NCHLT_speech_corpus |
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metrics: |
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- wer |
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model-index: |
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- name: facebook mms-1b-all Afrikaans - Beijuka Bruno |
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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: NCHLT_speech_corpus/Afrikaans |
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type: NCHLT_speech_corpus |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.6848588703158848 |
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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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# facebook mms-1b-all Afrikaans - Beijuka Bruno |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the NCHLT_speech_corpus/Afrikaans dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6359 |
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- Model Preparation Time: 0.0118 |
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- Wer: 0.6849 |
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- Cer: 0.1502 |
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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: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Wer | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:----------------------:|:------:|:------:| |
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| 130.0625 | 1.0 | 39 | 11.2286 | 0.0118 | 6.1049 | 1.7167 | |
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| 54.5199 | 2.0 | 78 | 3.0118 | 0.0118 | 1.0 | 0.9810 | |
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| 12.1633 | 3.0 | 117 | 0.3363 | 0.0118 | 0.4967 | 0.0774 | |
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| 3.4303 | 4.0 | 156 | 0.2186 | 0.0118 | 0.3785 | 0.0544 | |
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| 2.6944 | 5.0 | 195 | 0.1856 | 0.0118 | 0.3473 | 0.0487 | |
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| 2.4226 | 6.0 | 234 | 0.1697 | 0.0118 | 0.3265 | 0.0450 | |
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| 2.2073 | 7.0 | 273 | 0.1595 | 0.0118 | 0.3118 | 0.0425 | |
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| 2.0856 | 8.0 | 312 | 0.1544 | 0.0118 | 0.3059 | 0.0409 | |
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| 2.0081 | 9.0 | 351 | 0.1517 | 0.0118 | 0.2985 | 0.0403 | |
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| 1.9099 | 10.0 | 390 | 0.1475 | 0.0118 | 0.2847 | 0.0391 | |
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| 1.7942 | 11.0 | 429 | 0.1479 | 0.0118 | 0.2834 | 0.0393 | |
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| 1.8086 | 12.0 | 468 | 0.1497 | 0.0118 | 0.2863 | 0.0393 | |
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| 1.7132 | 13.0 | 507 | 0.1456 | 0.0118 | 0.2880 | 0.0389 | |
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| 1.67 | 14.0 | 546 | 0.1420 | 0.0118 | 0.2693 | 0.0373 | |
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| 1.5467 | 15.0 | 585 | 0.1390 | 0.0118 | 0.2695 | 0.0366 | |
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| 1.5461 | 16.0 | 624 | 0.1377 | 0.0118 | 0.2657 | 0.0358 | |
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| 1.5588 | 17.0 | 663 | 0.1397 | 0.0118 | 0.2622 | 0.0355 | |
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| 1.5061 | 18.0 | 702 | 0.1344 | 0.0118 | 0.2552 | 0.0352 | |
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| 1.4278 | 19.0 | 741 | 0.1330 | 0.0118 | 0.2584 | 0.0350 | |
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| 1.3602 | 20.0 | 780 | 0.1315 | 0.0118 | 0.2459 | 0.0333 | |
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| 1.3764 | 21.0 | 819 | 0.1304 | 0.0118 | 0.2476 | 0.0329 | |
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| 1.3472 | 22.0 | 858 | 0.1353 | 0.0118 | 0.2638 | 0.0347 | |
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| 1.3776 | 23.0 | 897 | 0.1343 | 0.0118 | 0.2546 | 0.0335 | |
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| 1.3292 | 24.0 | 936 | 0.1298 | 0.0118 | 0.2411 | 0.0325 | |
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| 1.234 | 25.0 | 975 | 0.1316 | 0.0118 | 0.2503 | 0.0345 | |
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| 1.2623 | 26.0 | 1014 | 0.1286 | 0.0118 | 0.2413 | 0.0326 | |
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| 1.2282 | 27.0 | 1053 | 0.1261 | 0.0118 | 0.2354 | 0.0320 | |
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| 1.2085 | 28.0 | 1092 | 0.1256 | 0.0118 | 0.2362 | 0.0320 | |
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| 1.1564 | 29.0 | 1131 | 0.1269 | 0.0118 | 0.2310 | 0.0318 | |
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| 1.147 | 30.0 | 1170 | 0.1305 | 0.0118 | 0.2289 | 0.0315 | |
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| 1.1448 | 31.0 | 1209 | 0.1271 | 0.0118 | 0.2270 | 0.0310 | |
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| 1.1173 | 32.0 | 1248 | 0.1262 | 0.0118 | 0.2367 | 0.0314 | |
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| 1.095 | 33.0 | 1287 | 0.1270 | 0.0118 | 0.2302 | 0.0312 | |
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| 1.143 | 34.0 | 1326 | 0.1292 | 0.0118 | 0.2248 | 0.0315 | |
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| 1.0139 | 35.0 | 1365 | 0.1300 | 0.0118 | 0.2259 | 0.0316 | |
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| 1.0125 | 36.0 | 1404 | 0.1287 | 0.0118 | 0.2229 | 0.0311 | |
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| 1.0034 | 37.0 | 1443 | 0.1265 | 0.0118 | 0.2194 | 0.0302 | |
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| 1.0737 | 38.0 | 1482 | 0.1274 | 0.0118 | 0.2253 | 0.0307 | |
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| 1.0448 | 39.0 | 1521 | 0.1259 | 0.0118 | 0.2248 | 0.0304 | |
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| 0.9256 | 40.0 | 1560 | 0.1261 | 0.0118 | 0.2120 | 0.0293 | |
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| 0.9715 | 41.0 | 1599 | 0.1278 | 0.0118 | 0.2131 | 0.0295 | |
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| 1.0504 | 42.0 | 1638 | 0.1261 | 0.0118 | 0.2188 | 0.0299 | |
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| 0.8878 | 43.0 | 1677 | 0.1269 | 0.0118 | 0.2099 | 0.0296 | |
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| 0.9358 | 44.0 | 1716 | 0.1275 | 0.0118 | 0.2101 | 0.0298 | |
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| 0.9141 | 45.0 | 1755 | 0.1278 | 0.0118 | 0.2367 | 0.0312 | |
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| 0.9163 | 46.0 | 1794 | 0.1234 | 0.0118 | 0.2183 | 0.0300 | |
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| 0.9371 | 47.0 | 1833 | 0.1274 | 0.0118 | 0.2101 | 0.0296 | |
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| 0.8757 | 48.0 | 1872 | 0.1288 | 0.0118 | 0.2061 | 0.0287 | |
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| 0.8129 | 49.0 | 1911 | 0.1279 | 0.0118 | 0.2120 | 0.0293 | |
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| 0.9089 | 50.0 | 1950 | 0.1265 | 0.0118 | 0.2050 | 0.0288 | |
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| 0.8816 | 51.0 | 1989 | 0.1288 | 0.0118 | 0.2270 | 0.0307 | |
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| 0.8614 | 52.0 | 2028 | 0.1288 | 0.0118 | 0.2194 | 0.0306 | |
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| 0.8606 | 53.0 | 2067 | 0.1261 | 0.0118 | 0.2223 | 0.0296 | |
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| 0.8022 | 54.0 | 2106 | 0.1266 | 0.0118 | 0.2007 | 0.0281 | |
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| 0.7914 | 55.0 | 2145 | 0.1272 | 0.0118 | 0.2055 | 0.0284 | |
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| 0.7952 | 56.0 | 2184 | 0.1251 | 0.0118 | 0.2096 | 0.0289 | |
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| 0.7639 | 57.0 | 2223 | 0.1255 | 0.0118 | 0.2034 | 0.0282 | |
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| 0.831 | 58.0 | 2262 | 0.1255 | 0.0118 | 0.2025 | 0.0284 | |
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| 0.8013 | 59.0 | 2301 | 0.1258 | 0.0118 | 0.1982 | 0.0279 | |
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| 0.8245 | 60.0 | 2340 | 0.1268 | 0.0118 | 0.2137 | 0.0287 | |
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| 0.76 | 61.0 | 2379 | 0.1256 | 0.0118 | 0.1996 | 0.0273 | |
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| 0.8253 | 62.0 | 2418 | 0.1278 | 0.0118 | 0.2153 | 0.0281 | |
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| 0.7767 | 63.0 | 2457 | 0.1257 | 0.0118 | 0.1944 | 0.0269 | |
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| 0.7583 | 64.0 | 2496 | 0.1264 | 0.0118 | 0.1933 | 0.0274 | |
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| 0.7451 | 65.0 | 2535 | 0.1279 | 0.0118 | 0.2074 | 0.0280 | |
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| 0.7453 | 66.0 | 2574 | 0.1290 | 0.0118 | 0.1998 | 0.0278 | |
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| 0.706 | 67.0 | 2613 | 0.1298 | 0.0118 | 0.2053 | 0.0281 | |
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| 0.7925 | 68.0 | 2652 | 0.1270 | 0.0118 | 0.2036 | 0.0283 | |
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| 0.698 | 69.0 | 2691 | 0.1271 | 0.0118 | 0.2007 | 0.0273 | |
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| 0.699 | 70.0 | 2730 | 0.1286 | 0.0118 | 0.2004 | 0.0269 | |
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| 0.7131 | 71.0 | 2769 | 0.1272 | 0.0118 | 0.1998 | 0.0271 | |
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| 0.7582 | 72.0 | 2808 | 0.1251 | 0.0118 | 0.1988 | 0.0272 | |
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| 0.6473 | 73.0 | 2847 | 0.1259 | 0.0118 | 0.1950 | 0.0271 | |
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| 0.7017 | 74.0 | 2886 | 0.1261 | 0.0118 | 0.1944 | 0.0272 | |
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| 0.655 | 75.0 | 2925 | 0.1277 | 0.0118 | 0.1979 | 0.0275 | |
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| 0.6369 | 76.0 | 2964 | 0.1274 | 0.0118 | 0.1966 | 0.0269 | |
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| 0.691 | 77.0 | 3003 | 0.1275 | 0.0118 | 0.1979 | 0.0271 | |
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| 0.7352 | 78.0 | 3042 | 0.1268 | 0.0118 | 0.1952 | 0.0269 | |
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| 0.6745 | 79.0 | 3081 | 0.1266 | 0.0118 | 0.1917 | 0.0267 | |
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| 0.6987 | 80.0 | 3120 | 0.1273 | 0.0118 | 0.1917 | 0.0264 | |
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| 0.6736 | 81.0 | 3159 | 0.1276 | 0.0118 | 0.1925 | 0.0266 | |
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| 0.7108 | 82.0 | 3198 | 0.1256 | 0.0118 | 0.1914 | 0.0261 | |
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| 0.6158 | 83.0 | 3237 | 0.1255 | 0.0118 | 0.1925 | 0.0266 | |
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| 0.6384 | 84.0 | 3276 | 0.1260 | 0.0118 | 0.1939 | 0.0267 | |
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| 0.6876 | 85.0 | 3315 | 0.1254 | 0.0118 | 0.1950 | 0.0264 | |
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| 0.658 | 86.0 | 3354 | 0.1252 | 0.0118 | 0.1947 | 0.0267 | |
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| 0.5993 | 87.0 | 3393 | 0.1250 | 0.0118 | 0.1936 | 0.0263 | |
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| 0.7441 | 88.0 | 3432 | 0.1248 | 0.0118 | 0.1939 | 0.0265 | |
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| 0.684 | 89.0 | 3471 | 0.1240 | 0.0118 | 0.1933 | 0.0263 | |
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| 0.7157 | 90.0 | 3510 | 0.1244 | 0.0118 | 0.1901 | 0.0261 | |
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| 0.5917 | 91.0 | 3549 | 0.1240 | 0.0118 | 0.1906 | 0.0260 | |
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| 0.637 | 92.0 | 3588 | 0.1245 | 0.0118 | 0.1920 | 0.0262 | |
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| 0.6691 | 93.0 | 3627 | 0.1235 | 0.0118 | 0.1912 | 0.0261 | |
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| 0.6616 | 94.0 | 3666 | 0.1239 | 0.0118 | 0.1895 | 0.0258 | |
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| 0.6641 | 95.0 | 3705 | 0.1232 | 0.0118 | 0.1890 | 0.0258 | |
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| 0.638 | 96.0 | 3744 | 0.1234 | 0.0118 | 0.1895 | 0.0257 | |
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| 0.6668 | 97.0 | 3783 | 0.1232 | 0.0118 | 0.1890 | 0.0257 | |
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| 0.5726 | 97.4459 | 3800 | 0.1233 | 0.0118 | 0.1895 | 0.0258 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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