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--- |
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language: |
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- ga |
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- en |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ymoslem/IWSLT2023-GA-EN |
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- ymoslem/FLEURS-GA-EN |
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- ymoslem/BitesizeIrish-GA-EN |
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- ymoslem/SpokenWords-GA-EN-MTed |
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- ymoslem/Tatoeba-Speech-Irish |
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- ymoslem/Wikimedia-Speech-Irish |
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- ymoslem/EUbookshop-Speech-Irish |
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metrics: |
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- bleu |
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- wer |
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model-index: |
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- name: Whisper Medium GA-EN Speech Translation |
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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: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop |
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type: ymoslem/IWSLT2023-GA-EN |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 30.86 |
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- name: Wer |
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type: wer |
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value: 67.04187303016658 |
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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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# Whisper Medium GA-EN Speech Translation |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co./openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, Wikimedia, and EUbookshop dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0885 |
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- Bleu: 30.86 |
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- Chrf: 54.11 |
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- Wer: 67.0419 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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_ratio: 0.03 |
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- training_steps: 8000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:-----:|:-----:|:---------------:|:--------:| |
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| 2.5374 | 0.0138 | 100 | 2.56 | 18.92 | 2.1201 | 222.4674 | |
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| 2.446 | 0.0276 | 200 | 3.07 | 20.56 | 2.1960 | 170.5088 | |
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| 2.2819 | 0.0414 | 300 | 5.87 | 25.17 | 1.9811 | 114.5880 | |
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| 2.1904 | 0.0552 | 400 | 8.41 | 25.65 | 1.9974 | 99.1896 | |
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| 2.026 | 0.0690 | 500 | 7.99 | 27.64 | 1.8961 | 130.7069 | |
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| 2.0448 | 0.0828 | 600 | 9.15 | 27.78 | 1.9410 | 104.9077 | |
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| 1.8606 | 0.0966 | 700 | 9.57 | 29.34 | 1.8451 | 110.4908 | |
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| 1.9887 | 0.1103 | 800 | 13.44 | 32.32 | 1.7419 | 84.3314 | |
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| 1.8633 | 0.1241 | 900 | 13.43 | 31.58 | 1.7376 | 102.1162 | |
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| 1.7576 | 0.1379 | 1000 | 11.9 | 32.68 | 1.6879 | 106.6186 | |
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| 1.7142 | 0.1517 | 1100 | 12.4 | 33.66 | 1.7571 | 102.6114 | |
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| 1.7168 | 0.1655 | 1200 | 17.35 | 36.55 | 1.6003 | 87.9784 | |
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| 1.6741 | 0.1793 | 1300 | 15.41 | 35.46 | 1.5883 | 92.8411 | |
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| 1.6534 | 0.1931 | 1400 | 17.12 | 37.24 | 1.5366 | 90.2296 | |
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| 1.58 | 0.2069 | 1500 | 17.49 | 38.5 | 1.5141 | 92.1207 | |
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| 1.403 | 0.2207 | 1600 | 16.78 | 39.13 | 1.4606 | 88.9689 | |
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| 1.3806 | 0.2345 | 1700 | 19.26 | 40.02 | 1.4263 | 86.7177 | |
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| 1.5111 | 0.2483 | 1800 | 18.4 | 39.47 | 1.4060 | 92.2557 | |
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| 1.4261 | 0.2621 | 1900 | 21.19 | 42.13 | 1.3911 | 78.7033 | |
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| 1.2974 | 0.2759 | 2000 | 15.6 | 38.66 | 1.3871 | 100.3152 | |
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| 1.2694 | 0.2897 | 2100 | 16.21 | 39.99 | 1.3527 | 91.2652 | |
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| 1.204 | 0.3034 | 2200 | 20.2 | 41.18 | 1.3232 | 86.8978 | |
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| 1.1922 | 0.3172 | 2300 | 16.44 | 40.85 | 1.3338 | 103.1968 | |
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| 1.1237 | 0.3310 | 2400 | 19.29 | 43.73 | 1.2830 | 94.4620 | |
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| 1.0989 | 0.3448 | 2500 | 25.11 | 46.84 | 1.2844 | 75.0563 | |
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| 1.0766 | 0.3586 | 2600 | 23.87 | 46.1 | 1.2578 | 74.5160 | |
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| 1.0432 | 0.3724 | 2700 | 22.31 | 44.91 | 1.2414 | 86.9878 | |
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| 1.1588 | 0.3862 | 2800 | 23.32 | 45.94 | 1.2051 | 77.1724 | |
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| 1.0062 | 0.4 | 2900 | 26.15 | 48.27 | 1.2059 | 69.4282 | |
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| 0.9178 | 0.4138 | 3000 | 29.13 | 48.92 | 1.1756 | 64.7456 | |
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| 0.9108 | 0.4276 | 3100 | 28.34 | 48.9 | 1.1665 | 67.2220 | |
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| 0.9868 | 0.4414 | 3200 | 25.64 | 48.93 | 1.1489 | 75.3264 | |
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| 0.9563 | 0.4552 | 3300 | 27.58 | 49.67 | 1.1181 | 71.8145 | |
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| 0.9138 | 0.4690 | 3400 | 28.37 | 50.96 | 1.1247 | 71.4543 | |
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| 0.8508 | 0.4828 | 3500 | 29.75 | 51.41 | 1.1007 | 68.3476 | |
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| 0.836 | 0.4966 | 3600 | 30.99 | 52.2 | 1.1114 | 66.5916 | |
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| 0.8435 | 0.5103 | 3700 | 30.64 | 52.77 | 1.0782 | 68.2125 | |
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| 0.8323 | 0.5241 | 3800 | 29.78 | 52.94 | 1.0744 | 68.9779 | |
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| 0.818 | 0.5379 | 3900 | 31.23 | 53.21 | 1.0639 | 67.7623 | |
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| 0.8095 | 0.5517 | 4000 | 31.02 | 53.51 | 1.0576 | 68.5277 | |
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| 0.922 | 0.5655 | 4100 | 1.2445| 25.47 | 46.16 | 74.2909 | |
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| 1.0387 | 0.5793 | 4200 | 1.2634| 25.44 | 46.19 | 71.0491 | |
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| 0.9386 | 0.5931 | 4300 | 1.2457| 22.36 | 45.4 | 76.8122 | |
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| 0.9297 | 0.6069 | 4400 | 1.2502| 28.65 | 46.48 | 65.7362 | |
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| 0.9837 | 0.6207 | 4500 | 1.2503| 26.81 | 46.53 | 68.9779 | |
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| 1.0226 | 0.6345 | 4600 | 1.2282| 19.37 | 44.1 | 86.4926 | |
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| 0.9896 | 0.6483 | 4700 | 1.2568| 26.06 | 46.46 | 70.8240 | |
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| 0.9805 | 0.6621 | 4800 | 1.2364| 19.29 | 42.56 | 82.0351 | |
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| 0.8982 | 0.6759 | 4900 | 1.2346| 28.58 | 47.84 | 64.6556 | |
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| 0.8303 | 0.6897 | 5000 | 1.2136| 27.25 | 48.15 | 68.3476 | |
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| 0.905 | 0.7034 | 5100 | 1.1808| 27.99 | 50.31 | 67.2220 | |
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| 0.8125 | 0.7172 | 5200 | 1.1971| 28.91 | 47.63 | 65.4660 | |
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| 0.7965 | 0.7310 | 5300 | 1.1789| 25.96 | 47.21 | 69.5633 | |
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| 0.8244 | 0.7448 | 5400 | 1.2237| 28.65 | 48.63 | 66.6367 | |
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| 0.7637 | 0.7586 | 5500 | 1.1765| 30.4 | 50.24 | 66.6817 | |
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| 0.7333 | 0.7724 | 5600 | 1.1295| 29.94 | 51.34 | 68.8879 | |
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| 0.8141 | 0.7862 | 5700 | 1.1238| 27.51 | 50.61 | 74.7861 | |
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| 0.6969 | 0.8 | 5800 | 1.1350| 23.95 | 48.76 | 87.6632 | |
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| 0.7162 | 0.8138 | 5900 | 1.1493| 26.34 | 48.65 | 74.0207 | |
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| 0.7421 | 0.8276 | 6000 | 1.0976| 28.69 | 52.23 | 68.5727 | |
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| 0.593 | 0.8414 | 6100 | 1.1163| 34.96 | 53.13 | 59.3426 | |
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| 0.678 | 0.8552 | 6200 | 1.1072| 34.14 | 53.2 | 61.6839 | |
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| 0.6018 | 0.8690 | 6300 | 1.0959| 31.8 | 53.33 | 64.1153 | |
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| 0.6038 | 0.8828 | 6400 | 1.0959| 24.77 | 50.61 | 84.2413 | |
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| 0.6174 | 0.8966 | 6500 | 1.0891| 25.48 | 50.6 | 81.6749 | |
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| 0.595 | 0.9103 | 6600 | 1.1037| 23.83 | 48.07 | 83.3859 | |
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| 0.6114 | 0.9241 | 6700 | 1.0723| 28.03 | 52.18 | 70.7789 | |
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| 0.6257 | 0.9379 | 6800 | 1.0797| 33.13 | 52.95 | 61.5038 | |
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| 0.6689 | 0.9517 | 6900 | 1.0803| 30.53 | 52.41 | 68.4376 | |
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| 0.4908 | 0.9655 | 7000 | 1.0901| 30.1 | 51.71 | 69.1130 | |
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| 0.5439 | 0.9793 | 7100 | 1.0672| 25.81 | 49.36 | 76.5871 | |
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| 0.5994 | 0.9931 | 7200 | 1.0705| 31.56 | 52.51 | 66.1414 | |
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| 0.2451 | 1.0069 | 7300 | 1.1069| 33.0 | 53.29 | 64.7006 | |
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| 0.2609 | 1.0207 | 7400 | 1.0877| 31.68 | 54.3 | 64.9257 | |
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| 0.2813 | 1.0345 | 7500 | 1.0910| 34.93 | 54.74 | 60.1531 | |
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| 0.2367 | 1.0483 | 7600 | 1.0999| 30.87 | 53.09 | 65.9163 | |
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| 0.2018 | 1.0621 | 7700 | 1.0917| 35.53 | 54.42 | 58.7573 | |
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| 0.2407 | 1.0759 | 7800 | 1.0859| 34.38 | 54.5 | 60.9185 | |
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| 0.2385 | 1.0897 | 7900 | 1.0866| 31.27 | 54.12 | 65.3309 | |
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| 0.2074 | 1.1034 | 8000 | 1.0885| 30.86 | 54.11 | 67.0419 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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