Xls R 300m It Cv8
This model is a speech recognition model fine-tuned on the Common Voice Swedish dataset based on facebook/wav2vec2-xls-r-300m, achieving a word error rate (WER) of 1.0286 on the evaluation set.
Downloads 19
Release Time : 3/2/2022
Model Overview
This is a model for Swedish automatic speech recognition (ASR), based on the Transformer architecture and specifically optimized for Swedish speech data.
Model Features
Low word error rate
Achieved a WER of 1.0286 on the Common Voice Swedish evaluation set, demonstrating excellent performance
Based on large-scale pre-trained model
Fine-tuned from facebook/wav2vec2-xls-r-300m, inheriting powerful speech feature extraction capabilities
Optimized for Swedish
Specifically fine-tuned using Swedish datasets for better recognition performance in Swedish
Model Capabilities
Swedish speech recognition
Speech-to-text
Robust speech event detection
Use Cases
Speech transcription
Swedish speech transcription
Convert Swedish speech content into text
Word error rate 1.0286
Voice assistants
Swedish voice interaction
Used for developing Swedish voice assistants
language:
- it license: apache-2.0 tags:
- robust-speech-event
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer datasets:
- common_voice This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset. It achieves the following results on the evaluation set:
- Loss: 1.0278
- Wer: 1.0286
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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
10.7838 | 0.01 | 5 | 14.5035 | 1.0 |
13.0582 | 0.03 | 10 | 13.6658 | 1.0 |
7.3034 | 0.04 | 15 | 9.7898 | 1.0 |
6.1847 | 0.05 | 20 | 6.9148 | 1.0 |
5.3371 | 0.07 | 25 | 5.3661 | 1.0 |
4.4274 | 0.08 | 30 | 4.6945 | 1.0 |
4.0918 | 0.1 | 35 | 4.3172 | 1.0 |
4.1734 | 0.11 | 40 | 4.0759 | 1.0 |
3.7332 | 0.12 | 45 | 3.9039 | 1.0 |
3.6871 | 0.14 | 50 | 3.7777 | 1.0 |
3.4428 | 0.15 | 55 | 3.6718 | 1.0 |
3.5514 | 0.16 | 60 | 3.5947 | 1.0 |
3.4307 | 0.18 | 65 | 3.5144 | 1.0 |
3.4102 | 0.19 | 70 | 3.4432 | 1.0 |
3.4964 | 0.21 | 75 | 3.3890 | 1.0 |
3.3936 | 0.22 | 80 | 3.3467 | 1.0 |
3.3051 | 0.23 | 85 | 3.3102 | 1.0 |
3.278 | 0.25 | 90 | 3.2801 | 1.0 |
3.2223 | 0.26 | 95 | 3.2440 | 1.0 |
3.1888 | 0.27 | 100 | 3.2900 | 1.0 |
3.218 | 0.29 | 105 | 3.2627 | 1.0 |
3.1308 | 0.3 | 110 | 3.2152 | 1.0 |
3.109 | 0.31 | 115 | 3.1686 | 1.0 |
3.1188 | 0.33 | 120 | 3.1734 | 1.0 |
3.1132 | 0.34 | 125 | 3.1431 | 1.0 |
3.0667 | 0.36 | 130 | 3.1686 | 1.0 |
3.1167 | 0.37 | 135 | 3.1885 | 1.0 |
3.0592 | 0.38 | 140 | 3.1100 | 1.0 |
3.0531 | 0.4 | 145 | 3.1149 | 1.0 |
3.1224 | 0.41 | 150 | 3.1205 | 1.0 |
3.0651 | 0.42 | 155 | 3.1101 | 1.0 |
3.0077 | 0.44 | 160 | 3.0980 | 1.0 |
3.0027 | 0.45 | 165 | 3.1132 | 1.0 |
3.0423 | 0.47 | 170 | 3.0886 | 1.0 |
3.0462 | 0.48 | 175 | 3.0865 | 1.0 |
3.0701 | 0.49 | 180 | 3.0863 | 1.0 |
3.0871 | 0.51 | 185 | 3.0825 | 1.0 |
3.0585 | 0.52 | 190 | 3.0720 | 1.0 |
3.0274 | 0.53 | 195 | 3.0736 | 1.0 |
3.0983 | 0.55 | 200 | 3.0658 | 1.0 |
3.0538 | 0.56 | 205 | 3.1241 | 1.0 |
3.0862 | 0.57 | 210 | 3.0573 | 1.0 |
3.0041 | 0.59 | 215 | 3.0608 | 1.0 |
3.027 | 0.6 | 220 | 3.0614 | 1.0 |
2.9916 | 0.62 | 225 | 3.0527 | 1.0 |
3.0157 | 0.63 | 230 | 3.0514 | 1.0 |
3.0429 | 0.64 | 235 | 3.0391 | 1.0 |
2.999 | 0.66 | 240 | 3.0462 | 1.0 |
3.0053 | 0.67 | 245 | 3.0438 | 1.0 |
2.9812 | 0.68 | 250 | 3.0447 | 1.0 |
3.0062 | 0.7 | 255 | 3.0660 | 1.0 |
3.0045 | 0.71 | 260 | 3.0103 | 1.0 |
2.9684 | 0.73 | 265 | 3.0106 | 1.0 |
2.9885 | 0.74 | 270 | 3.0014 | 1.0 |
3.0062 | 0.75 | 275 | 2.9885 | 1.0 |
2.9736 | 0.77 | 280 | 3.0330 | 1.0 |
2.9766 | 0.78 | 285 | 2.9910 | 1.0 |
2.9545 | 0.79 | 290 | 2.9972 | 1.0 |
2.9936 | 0.81 | 295 | 2.9872 | 1.0 |
3.0832 | 0.82 | 300 | 2.9978 | 1.0 |
2.974 | 0.83 | 305 | 2.9978 | 1.0 |
2.9846 | 0.85 | 310 | 2.9849 | 1.0 |
2.9554 | 0.86 | 315 | 2.9810 | 1.0 |
2.9524 | 0.88 | 320 | 2.9731 | 1.0 |
2.9426 | 0.89 | 325 | 2.9824 | 1.0 |
2.9416 | 0.9 | 330 | 2.9731 | 1.0 |
2.9705 | 0.92 | 335 | 2.9830 | 1.0 |
2.9502 | 0.93 | 340 | 2.9713 | 1.0 |
2.9393 | 0.94 | 345 | 2.9790 | 1.0 |
2.9336 | 0.96 | 350 | 2.9684 | 1.0 |
2.9542 | 0.97 | 355 | 2.9689 | 1.0 |
2.9408 | 0.98 | 360 | 2.9556 | 1.0 |
2.9544 | 1.0 | 365 | 2.9563 | 1.0 |
2.9187 | 1.01 | 370 | 2.9624 | 1.0 |
2.9935 | 1.03 | 375 | 2.9500 | 1.0 |
2.9803 | 1.04 | 380 | 2.9558 | 1.0 |
2.9867 | 1.05 | 385 | 2.9473 | 1.0 |
2.8925 | 1.07 | 390 | 2.9444 | 1.0 |
2.9633 | 1.08 | 395 | 2.9490 | 1.0 |
2.9191 | 1.1 | 400 | 2.9362 | 1.0 |
2.9081 | 1.11 | 405 | 2.9394 | 1.0 |
2.9381 | 1.12 | 410 | 2.9846 | 1.0 |
2.9271 | 1.14 | 415 | 2.9638 | 1.0 |
2.959 | 1.15 | 420 | 2.9835 | 1.0 |
2.9486 | 1.16 | 425 | 2.9361 | 1.0 |
2.9246 | 1.18 | 430 | 2.9615 | 1.0 |
2.923 | 1.19 | 435 | 2.9313 | 1.0 |
2.8908 | 1.21 | 440 | 2.9362 | 1.0 |
2.8976 | 1.22 | 445 | 2.9224 | 1.0 |
2.9278 | 1.23 | 450 | 2.9276 | 1.0 |
2.8429 | 1.25 | 455 | 2.9299 | 1.0 |
2.867 | 1.26 | 460 | 2.9258 | 1.0 |
2.9734 | 1.27 | 465 | 2.9281 | 1.0000 |
2.934 | 1.29 | 470 | 2.9229 | 1.0 |
2.9521 | 1.3 | 475 | 2.9134 | 1.0 |
2.9098 | 1.31 | 480 | 2.9051 | 0.9993 |
2.9112 | 1.33 | 485 | 2.9028 | 0.9999 |
2.8799 | 1.34 | 490 | 2.9101 | 0.9986 |
2.857 | 1.36 | 495 | 2.9005 | 0.9992 |
2.8525 | 1.37 | 500 | 2.8937 | 1.0 |
2.8682 | 1.38 | 505 | 2.8904 | 1.0000 |
2.8899 | 1.4 | 510 | 2.8914 | 0.9964 |
2.7475 | 1.41 | 515 | 2.8842 | 0.9950 |
2.9263 | 1.42 | 520 | 2.8852 | 0.9972 |
2.8603 | 1.44 | 525 | 2.8762 | 0.9966 |
2.864 | 1.45 | 530 | 2.8680 | 0.9978 |
2.8632 | 1.47 | 535 | 2.8602 | 0.9964 |
2.9289 | 1.48 | 540 | 2.8584 | 0.9952 |
2.8689 | 1.49 | 545 | 2.8587 | 0.9956 |
2.8304 | 1.51 | 550 | 2.8511 | 0.9993 |
2.8024 | 1.52 | 555 | 2.8460 | 1.0 |
2.7649 | 1.53 | 560 | 2.8460 | 1.0000 |
2.8756 | 1.55 | 565 | 2.8348 | 0.9987 |
2.8808 | 1.56 | 570 | 2.8539 | 0.9993 |
2.9027 | 1.57 | 575 | 2.8282 | 0.9975 |
2.8586 | 1.59 | 580 | 2.8288 | 0.9976 |
2.8193 | 1.6 | 585 | 2.8101 | 1.0051 |
2.811 | 1.62 | 590 | 2.7965 | 1.0014 |
2.7332 | 1.63 | 595 | 2.7884 | 1.0026 |
2.7717 | 1.64 | 600 | 2.7883 | 1.0060 |
2.6901 | 1.66 | 605 | 2.7801 | 0.9974 |
2.6905 | 1.67 | 610 | 2.8113 | 0.9968 |
2.7442 | 1.68 | 615 | 2.8113 | 1.0007 |
2.8431 | 1.7 | 620 | 2.8152 | 1.0343 |
2.8028 | 1.71 | 625 | 2.7790 | 1.0250 |
2.7151 | 1.73 | 630 | 2.7653 | 1.0287 |
2.7405 | 1.74 | 635 | 2.7714 | 1.1303 |
2.7566 | 1.75 | 640 | 2.7488 | 1.0312 |
2.7337 | 1.77 | 645 | 2.7498 | 1.0176 |
2.7486 | 1.78 | 650 | 2.7496 | 1.0760 |
2.6918 | 1.79 | 655 | 2.7391 | 1.0353 |
2.7142 | 1.81 | 660 | 2.7500 | 1.0283 |
2.7057 | 1.82 | 665 | 2.7612 | 1.0127 |
2.8348 | 1.83 | 670 | 2.7441 | 1.0056 |
2.705 | 1.85 | 675 | 2.7473 | 1.0519 |
2.7547 | 1.86 | 680 | 2.7216 | 1.0218 |
2.7045 | 1.88 | 685 | 2.7261 | 1.1414 |
2.7121 | 1.89 | 690 | 2.7223 | 1.0287 |
2.6877 | 1.9 | 695 | 2.7283 | 1.0274 |
2.6879 | 1.92 | 700 | 2.7451 | 1.1322 |
2.6958 | 1.93 | 705 | 2.7166 | 1.0364 |
2.6692 | 1.94 | 710 | 2.7148 | 1.0074 |
2.5786 | 1.96 | 715 | 2.7101 | 1.0504 |
2.6919 | 1.97 | 720 | 2.6963 | 1.0454 |
2.7256 | 1.98 | 725 | 2.7201 | 1.0349 |
2.6507 | 2.0 | 730 | 2.7099 | 1.1339 |
2.7833 | 2.01 | 735 | 2.7111 | 1.0124 |
2.7521 | 2.03 | 740 | 2.7024 | 1.0275 |
2.6732 | 2.04 | 745 | 2.7058 | 1.0647 |
2.719 | 2.05 | 750 | 2.7200 | 1.0211 |
2.701 | 2.07 | 755 | 2.7024 | 1.0808 |
2.6444 | 2.08 | 760 | 2.6813 | 1.0582 |
2.5592 | 2.1 | 765 | 2.6783 | 1.1010 |
2.6444 | 2.11 | 770 | 2.6707 | 1.0946 |
2.6944 | 2.12 | 775 | 2.7012 | 1.1315 |
2.6733 | 2.14 | 780 | 2.7072 | 1.1144 |
2.6998 | 2.15 | 785 | 2.7132 | 1.0206 |
2.796 | 2.16 | 790 | 2.7076 | 1.1262 |
2.6881 | 2.18 | 795 | 2.6953 | 1.0841 |
2.7382 | 2.19 | 800 | 2.6605 | 1.1234 |
2.5814 | 2.21 | 805 | 2.6814 | 1.1865 |
2.6695 | 2.22 | 810 | 2.6531 | 1.0985 |
2.6415 | 2.23 | 815 | 2.6590 | 1.0804 |
2.646 | 2.25 | 820 | 2.6514 | 1.0853 |
2.6028 | 2.26 | 825 | 2.6723 | 1.1411 |
2.6429 | 2.27 | 830 | 2.6729 | 1.0395 |
2.6736 | 2.29 | 835 | 2.7039 | 1.0355 |
2.6959 | 2.3 | 840 | 2.6510 | 1.0414 |
2.6426 | 2.31 | 845 | 2.6660 | 1.1591 |
2.7152 | 2.33 | 850 | 2.6361 | 1.0276 |
2.7148 | 2.34 | 855 | 2.6723 | 1.2461 |
2.6336 | 2.36 | 860 | 2.6332 | 1.0310 |
2.665 | 2.37 | 865 | 2.6365 | 1.1312 |
2.5607 | 2.38 | 870 | 2.6344 | 1.1301 |
2.5614 | 2.4 | 875 | 2.6437 | 1.1513 |
2.4899 | 2.41 | 880 | 2.6418 | 1.1532 |
2.6794 | 2.42 | 885 | 2.6403 | 1.0272 |
2.6814 | 2.44 | 890 | 2.6420 | 1.1323 |
2.6614 | 2.45 | 895 | 2.6183 | 1.0525 |
2.6629 | 2.47 | 900 | 2.6414 | 1.1569 |
2.6166 | 2.48 | 905 | 2.6167 | 1.0265 |
2.6374 | 2.49 | 910 | 2.6299 | 1.1720 |
2.6035 | 2.51 | 915 | 2.6139 | 1.1565 |
2.595 | 2.52 | 920 | 2.6126 | 1.0557 |
2.6416 | 2.53 | 925 | 2.6190 | 1.0414 |
2.6785 | 2.55 | 930 | 2.6352 | 1.0289 |
2.6986 | 2.56 | 935 | 2.6268 | 1.0077 |
2.6145 | 2.57 | 940 | 2.6166 | 1.0445 |
2.6961 | 2.59 | 945 | 2.6142 | 1.0185 |
2.6852 | 2.6 | 950 | 2.6072 | 1.0122 |
2.5792 | 2.62 | 955 | 2.6078 | 1.1165 |
2.6118 | 2.63 | 960 | 2.6177 | 1.1210 |
2.5472 | 2.64 | 965 | 2.6126 | 1.0044 |
2.577 | 2.66 | 970 | 2.6051 | 1.0881 |
2.5602 | 2.67 | 975 | 2.5992 | 1.0178 |
2.695 | 2.68 | 980 | 2.6023 | 1.0248 |
2.7017 | 2.7 | 985 | 2.6190 | 1.0041 |
2.6327 | 2.71 | 990 | 2.6024 | 1.0142 |
2.6193 | 2.73 | 995 | 2.5897 | 1.0148 |
2.5939 | 2.74 | 1000 | 2.5900 | 1.0329 |
2.5477 | 2.75 | 1005 | 2.5971 | 1.0338 |
2.6089 | 2.77 | 1010 | 2.5969 | 1.0064 |
2.5625 | 2.78 | 1015 | 2.5899 | 1.0648 |
2.5745 | 2.79 | 1020 | 2.5861 | 1.0627 |
2.5702 | 2.81 | 1025 | 2.5923 | 1.0526 |
2.645 | 2.82 | 1030 | 2.6053 | 1.0199 |
2.6869 | 2.83 | 1035 | 2.6227 | 1.0011 |
2.6678 | 2.85 | 1040 | 2.6094 | 1.0179 |
2.6787 | 2.86 | 1045 | 2.5978 | 1.0028 |
2.6246 | 2.88 | 1050 | 2.5965 | 1.0093 |
2.5676 | 2.89 | 1055 | 2.5927 | 1.0627 |
2.6773 | 2.9 | 1060 | 2.5907 | 1.0817 |
2.6114 | 2.92 | 1065 | 2.5932 | 1.1013 |
2.6227 | 2.93 | 1070 | 2.5840 | 1.0402 |
2.594 | 2.94 | 1075 | 2.5997 | 1.1371 |
2.751 | 2.96 | 1080 | 2.5909 | 1.0972 |
2.6366 | 2.97 | 1085 | 2.6081 | 1.0598 |
2.577 | 2.98 | 1090 | 2.5915 | 1.0410 |
2.579 | 3.0 | 1095 | 2.5953 | 1.1433 |
2.6706 | 3.01 | 1100 | 2.5913 | 1.0456 |
2.6161 | 3.03 | 1105 | 2.6079 | 1.1009 |
2.6397 | 3.04 | 1110 | 2.5951 | 1.1771 |
2.6246 | 3.05 | 1115 | 2.5730 | 1.0299 |
2.5637 | 3.07 | 1120 | 2.5622 | 1.0848 |
2.5692 | 3.08 | 1125 | 2.5561 | 1.1472 |
2.5948 | 3.1 | 1130 | 2.5568 | 1.0802 |
2.5372 | 3.11 | 1135 | 2.5638 | 1.1261 |
2.4995 | 3.12 | 1140 | 2.5727 | 1.1395 |
2.6304 | 3.14 | 1145 | 2.5671 | 1.0259 |
2.6395 | 3.15 | 1150 | 2.5778 | 1.0212 |
2.6127 | 3.16 | 1155 | 2.5609 | 1.0457 |
2.5919 | 3.18 | 1160 | 2.5604 | 1.0902 |
2.6111 | 3.19 | 1165 | 2.5463 | 1.0014 |
2.5971 | 3.21 | 1170 | 2.5429 | 1.0022 |
2.5887 | 3.22 | 1175 | 2.5394 | 1.0412 |
2.5644 | 3.23 | 1180 | 2.5342 | 1.0469 |
2.4805 | 3.25 | 1185 | 2.6066 | 1.2668 |
2.5324 | 3.26 | 1190 | 2.5395 | 1.0234 |
2.5491 | 3.27 | 1195 | 2.5431 | 1.0644 |
2.6302 | 3.29 | 1200 | 2.5558 | 1.0680 |
2.6139 | 3.3 | 1205 | 2.5711 | 1.0565 |
2.5607 | 3.31 | 1210 | 2.5635 | 1.0415 |
2.6535 | 3.33 | 1215 | 2.5505 | 1.0613 |
2.6129 | 3.34 | 1220 | 2.5403 | 1.0724 |
2.5157 | 3.36 | 1225 | 2.5294 | 1.0585 |
2.551 | 3.37 | 1230 | 2.5242 | 1.1599 |
2.5527 | 3.38 | 1235 | 2.5474 | 1.2327 |
2.4964 | 3.4 | 1240 | 2.5244 | 1.0857 |
2.5781 | 3.41 | 1245 | 2.5299 | 1.0470 |
2.6143 | 3.42 | 1250 | 2.5313 | 1.0019 |
2.6566 | 3.44 | 1255 | 2.5431 | 1.0488 |
2.5373 | 3.45 | 1260 | 2.5281 | 1.0901 |
2.6597 | 3.47 | 1265 | 2.5300 | 1.0610 |
2.5457 | 3.48 | 1270 | 2.5130 | 1.0420 |
2.5632 | 3.49 | 1275 | 2.5306 | 1.1418 |
2.5267 | 3.51 | 1280 | 2.5021 | 1.0293 |
2.507 | 3.52 | 1285 | 2.5013 | 1.0196 |
2.5713 | 3.53 | 1290 | 2.4978 | 1.0664 |
2.4783 | 3.55 | 1295 | 2.4958 | 1.0530 |
2.5874 | 3.56 | 1300 | 2.4968 | 1.0059 |
2.5744 | 3.57 | 1305 | 2.5078 | 1.0287 |
2.5701 | 3.59 | 1310 | 2.4971 | 1.0366 |
2.5366 | 3.6 | 1315 | 2.4897 | 1.0191 |
2.5679 | 3.62 | 1320 | 2.4830 | 1.0223 |
2.5239 | 3.63 | 1325 | 2.4833 | 1.0784 |
2.5411 | 3.64 | 1330 | 2.4851 | 1.1522 |
2.5037 | 3.66 | 1335 | 2.4792 | 1.0928 |
2.5907 | 3.67 | 1340 | 2.4750 | 1.0187 |
2.5107 | 3.68 | 1345 | 2.4805 | 1.0873 |
2.5908 | 3.7 | 1350 | 2.4753 | 1.0098 |
2.6274 | 3.71 | 1355 | 2.4765 | 1.0045 |
2.5708 | 3.73 | 1360 | 2.4597 | 1.0456 |
2.6039 | 3.74 | 1365 | 2.4503 | 1.0485 |
2.5305 | 3.75 | 1370 | 2.4439 | 1.0126 |
2.4878 | 3.77 | 1375 | 2.4407 | 1.0162 |
2.5055 | 3.78 | 1380 | 2.4421 | 1.0605 |
2.5249 | 3.79 | 1385 | 2.4499 | 1.1163 |
2.5508 | 3.81 | 1390 | 2.4654 | 1.1472 |
2.5827 | 3.82 | 1395 | 2.4510 | 1.0561 |
2.6148 | 3.83 | 1400 | 2.4496 | 0.9998 |
2.5763 | 3.85 | 1405 | 2.4417 | 1.0067 |
2.6077 | 3.86 | 1410 | 2.4458 | 1.0682 |
2.5388 | 3.88 | 1415 | 2.4352 | 1.0820 |
2.5235 | 3.89 | 1420 | 2.4277 | 1.0784 |
2.4996 | 3.9 | 1425 | 2.4245 | 1.0671 |
2.5601 | 3.92 | 1430 | 2.4202 | 1.0650 |
2.5805 | 3.93 | 1435 | 2.4199 | 1.0530 |
2.5841 | 3.94 | 1440 | 2.4228 | 1.0797 |
2.4877 | 3.96 | 1445 | 2.4284 | 1.1159 |
2.5542 | 3.97 | 1450 | 2.4190 | 1.0575 |
2.5961 | 3.98 | 1455 | 2.4162 | 1.0676 |
2.495 | 4.0 | 1460 | 2.4165 | 1.0821 |
2.6157 | 4.01 | 1465 | 2.4119 | 1.0117 |
2.5415 | 4.03 | 1470 | 2.4089 | 1.0110 |
2.4916 | 4.04 | 1475 | 2.4032 | 1.0498 |
2.5445 | 4.05 | 1480 | 2.3997 | 1.0429 |
2.4941 | 4.07 | 1485 | 2.4008 | 1.0141 |
2.5113 | 4.08 | 1490 | 2.3975 | 1.0357 |
2.4707 | 4.1 | 1495 | 2.3938 | 1.0288 |
2.4952 | 4.11 | 1500 | 2.3910 | 1.0300 |
2.5017 | 4.12 | 1505 | 2.3861 | 1.0813 |
2.5566 | 4.14 | 1510 | 2.3919 | 1.1082 |
2.5754 | 4.15 | 1515 | 2.3947 | 1.0074 |
2.6138 | 4.16 | 1520 | 2.4040 | 0.9989 |
2.5024 | 4.18 | 1525 | 2.3949 | 1.0039 |
2.5136 | 4.19 | 1530 | 2.3993 | 1.0496 |
2.5646 | 4.21 | 1535 | 2.3981 | 1.0729 |
2.4556 | 4.22 | 1540 | 2.3952 | 1.0494 |
2.5774 | 4.23 | 1545 | 2.3924 | 1.0345 |
2.5126 | 4.25 | 1550 | 2.3888 | 1.0306 |
2.4596 | 4.26 | 1555 | 2.3960 | 1.0775 |
2.521 | 4.27 | 1560 | 2.3978 | 1.1025 |
2.6304 | 4.29 | 1565 | 2.3885 | 1.0433 |
2.543 | 4.3 | 1570 | 2.3849 | 1.0072 |
2.5601 | 4.31 | 1575 | 2.3855 | 1.0110 |
2.6304 | 4.33 | 1580 | 2.3878 | 1.0369 |
2.4121 | 4.34 | 1585 | 2.3783 | 1.0366 |
2.4261 | 4.36 | 1590 | 2.3746 | 1.0307 |
2.5038 | 4.37 | 1595 | 2.3789 | 1.0611 |
2.5391 | 4.38 | 1600 | 2.3849 | 1.0738 |
2.4341 | 4.4 | 1605 | 2.3779 | 1.0573 |
2.5306 | 4.41 | 1610 | 2.3751 | 1.0460 |
2.5818 | 4.42 | 1615 | 2.3743 | 1.0251 |
2.5531 | 4.44 | 1620 | 2.3723 | 1.0209 |
2.51 | 4.45 | 1625 | 2.3755 | 1.0316 |
2.5788 | 4.47 | 1630 | 2.3725 | 1.0396 |
2.5701 | 4.48 | 1635 | 2.3663 | 1.0292 |
2.4194 | 4.49 | 1640 | 2.3641 | 1.0261 |
2.5439 | 4.51 | 1645 | 2.3629 | 1.0376 |
2.4527 | 4.52 | 1650 | 2.3629 | 1.0563 |
2.5705 | 4.53 | 1655 | 2.3654 | 1.0766 |
2.4552 | 4.55 | 1660 | 2.3708 | 1.0802 |
2.5657 | 4.56 | 1665 | 2.3638 | 1.0248 |
2.5371 | 4.57 | 1670 | 2.3639 | 1.0053 |
2.5365 | 4.59 | 1675 | 2.3626 | 1.0072 |
2.5383 | 4.6 | 1680 | 2.3584 | 1.0170 |
2.546 | 4.62 | 1685 | 2.3574 | 1.0469 |
2.6006 | 4.63 | 1690 | 2.3517 | 1.0509 |
2.4894 | 4.64 | 1695 | 2.3489 | 1.0452 |
2.4732 | 4.66 | 1700 | 2.3489 | 1.0586 |
2.4933 | 4.67 | 1705 | 2.3501 | 1.0694 |
2.4784 | 4.68 | 1710 | 2.3472 | 1.0647 |
2.5349 | 4.7 | 1715 | 2.3419 | 1.0299 |
2.553 | 4.71 | 1720 | 2.3420 | 1.0115 |
2.5035 | 4.73 | 1725 | 2.3415 | 1.0117 |
2.561 | 4.74 | 1730 | 2.3418 | 1.0242 |
2.4773 | 4.75 | 1735 | 2.3420 | 1.0325 |
2.4691 | 4.77 | 1740 | 2.3422 | 1.0394 |
2.4959 | 4.78 | 1745 | 2.3405 | 1.0418 |
2.4928 | 4.79 | 1750 | 2.3394 | 1.0449 |
2.5058 | 4.81 | 1755 | 2.3392 | 1.0489 |
2.5193 | 4.82 | 1760 | 2.3390 | 1.0506 |
2.5369 | 4.83 | 1765 | 2.3392 | 1.0384 |
2.4843 | 4.85 | 1770 | 2.3398 | 1.0236 |
2.5074 | 4.86 | 1775 | 2.3400 | 1.0150 |
2.4941 | 4.88 | 1780 | 2.3386 | 1.0150 |
2.4352 | 4.89 | 1785 | 2.3370 | 1.0172 |
2.4372 | 4.9 | 1790 | 2.3362 | 1.0208 |
2.4855 | 4.92 | 1795 | 2.3358 | 1.0238 |
2.4516 | 4.93 | 1800 | 2.3355 | 1.0276 |
2.5281 | 4.94 | 1805 | 2.3356 | 1.0312 |
2.5519 | 4.96 | 1810 | 2.3352 | 1.0318 |
2.4641 | 4.97 | 1815 | 2.3349 | 1.0294 |
2.4515 | 4.98 | 1820 | 2.3348 | 1.0284 |
2.553 | 5.0 | 1825 | 2.3347 | 1.0286 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
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