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ilyes25
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kab-dz

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
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  1. README.md +35 -73
  2. adapter.ardz.safetensors +3 -0
  3. config.json +2 -2
  4. model.safetensors +2 -2
  5. training_args.bin +1 -1
README.md CHANGED
@@ -9,21 +9,21 @@ metrics:
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  - bleu
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  - rouge
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  model-index:
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- - name: wav2vec2-large-mms-1b-testkabtodz
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  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
19
- # wav2vec2-large-mms-1b-testkabtodz
20
 
21
  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2934
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- - Wer: 0.3763
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- - Bleu: 0.4201
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- - Rouge: {'rouge1': 0.6991211401254616, 'rouge2': 0.5113991327668065, 'rougeL': 0.6986732788231161, 'rougeLsum': 0.6986739135320972}
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  ## Model description
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@@ -42,83 +42,45 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.001
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  - train_batch_size: 8
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- - eval_batch_size: 8
48
  - seed: 42
49
  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 32
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  - optimizer: Use OptimizerNames.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: 6
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  - mixed_precision_training: Native AMP
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57
  ### Training results
58
 
59
- | Training Loss | Epoch | Step | Validation Loss | Wer | Bleu | Rouge |
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- |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:----------------------------------------------------------------------------------------------------------------------------:|
61
- | 4.3997 | 0.0975 | 100 | 0.5135 | 0.5571 | 0.2220 | {'rouge1': 0.5475180597055971, 'rouge2': 0.3270968169315924, 'rougeL': 0.5466017270688334, 'rougeLsum': 0.5466539047963868} |
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- | 0.7974 | 0.1950 | 200 | 0.4535 | 0.5082 | 0.2685 | {'rouge1': 0.5883604555009918, 'rouge2': 0.37224278148732814, 'rougeL': 0.58778413455352, 'rougeLsum': 0.5878866254405216} |
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- | 0.7687 | 0.2925 | 300 | 0.4353 | 0.5107 | 0.2702 | {'rouge1': 0.5791620440545029, 'rouge2': 0.3647635082576582, 'rougeL': 0.5786726661069095, 'rougeLsum': 0.578427085732115} |
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- | 0.7347 | 0.3901 | 400 | 0.4203 | 0.4935 | 0.2822 | {'rouge1': 0.5933214201446024, 'rouge2': 0.3802725646533577, 'rougeL': 0.592789497390066, 'rougeLsum': 0.5928900139888104} |
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- | 0.739 | 0.4876 | 500 | 0.4219 | 0.4948 | 0.2853 | {'rouge1': 0.5930020254317057, 'rouge2': 0.3836260554784502, 'rougeL': 0.5924936819832712, 'rougeLsum': 0.5924175204674462} |
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- | 0.71 | 0.5851 | 600 | 0.4071 | 0.4845 | 0.2926 | {'rouge1': 0.6064479955042643, 'rouge2': 0.39667769990788043, 'rougeL': 0.6057773765484327, 'rougeLsum': 0.6057319550255804} |
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- | 0.7092 | 0.6826 | 700 | 0.4061 | 0.5079 | 0.2735 | {'rouge1': 0.5852701143314596, 'rouge2': 0.3740597630469874, 'rougeL': 0.5846072239010475, 'rougeLsum': 0.5847361320290986} |
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- | 0.7115 | 0.7801 | 800 | 0.3900 | 0.4704 | 0.3131 | {'rouge1': 0.6124360392599644, 'rouge2': 0.40568786403727697, 'rougeL': 0.6118523579549737, 'rougeLsum': 0.6119675977304625} |
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- | 0.6838 | 0.8776 | 900 | 0.4072 | 0.4770 | 0.3096 | {'rouge1': 0.6201184692333361, 'rouge2': 0.41106426012710773, 'rougeL': 0.6198915088918809, 'rougeLsum': 0.6197408359420962} |
70
- | 0.6856 | 0.9751 | 1000 | 0.3952 | 0.4700 | 0.3107 | {'rouge1': 0.6216365358309555, 'rouge2': 0.41277678000049217, 'rougeL': 0.620856162523816, 'rougeLsum': 0.6207997080976501} |
71
- | 0.6674 | 1.0731 | 1100 | 0.3721 | 0.4572 | 0.3294 | {'rouge1': 0.6265691010783012, 'rouge2': 0.42290161403750415, 'rougeL': 0.6261639876595877, 'rougeLsum': 0.6261591135372615} |
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- | 0.6724 | 1.1706 | 1200 | 0.3736 | 0.4499 | 0.3343 | {'rouge1': 0.6336199282955304, 'rouge2': 0.4300982515566308, 'rougeL': 0.6330532552585475, 'rougeLsum': 0.633105822459274} |
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- | 0.6632 | 1.2682 | 1300 | 0.3728 | 0.4445 | 0.3389 | {'rouge1': 0.6346996622011346, 'rouge2': 0.4307222639248516, 'rougeL': 0.6343748576151161, 'rougeLsum': 0.6341498296555887} |
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- | 0.6657 | 1.3657 | 1400 | 0.3729 | 0.4522 | 0.3369 | {'rouge1': 0.6397041793580083, 'rouge2': 0.43442104995643704, 'rougeL': 0.6390537417286732, 'rougeLsum': 0.6390297337673353} |
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- | 0.6518 | 1.4632 | 1500 | 0.3665 | 0.4496 | 0.3342 | {'rouge1': 0.6290383024547792, 'rouge2': 0.42389320352057036, 'rougeL': 0.6288306745091069, 'rougeLsum': 0.6287260862882811} |
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- | 0.6449 | 1.5607 | 1600 | 0.3660 | 0.4428 | 0.3433 | {'rouge1': 0.6366059619022576, 'rouge2': 0.432127142607018, 'rougeL': 0.6361950274472504, 'rougeLsum': 0.6362311315882483} |
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- | 0.6504 | 1.6582 | 1700 | 0.3610 | 0.4404 | 0.3437 | {'rouge1': 0.6446583030590606, 'rouge2': 0.441441091990041, 'rougeL': 0.6441935623170799, 'rougeLsum': 0.6441440891668418} |
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- | 0.6518 | 1.7557 | 1800 | 0.3567 | 0.4356 | 0.3490 | {'rouge1': 0.6414408742516806, 'rouge2': 0.438412441455466, 'rougeL': 0.6410284997921896, 'rougeLsum': 0.6409023788035733} |
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- | 0.6365 | 1.8532 | 1900 | 0.3551 | 0.4306 | 0.3556 | {'rouge1': 0.6546014266867779, 'rouge2': 0.4541339617751781, 'rougeL': 0.6541955004535241, 'rougeLsum': 0.6542146368444082} |
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- | 0.6461 | 1.9508 | 2000 | 0.3474 | 0.4320 | 0.3529 | {'rouge1': 0.6493813027870413, 'rouge2': 0.44870066279659765, 'rougeL': 0.6486450521361194, 'rougeLsum': 0.6488808404178003} |
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- | 0.6455 | 2.0488 | 2100 | 0.3526 | 0.4336 | 0.3502 | {'rouge1': 0.6464633128513417, 'rouge2': 0.44548114036987474, 'rougeL': 0.6460289414664477, 'rougeLsum': 0.6461897742988368} |
82
- | 0.6317 | 2.1463 | 2200 | 0.3535 | 0.4328 | 0.3489 | {'rouge1': 0.6459287664424098, 'rouge2': 0.4431699811723434, 'rougeL': 0.6457213736715056, 'rougeLsum': 0.6457315635195204} |
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- | 0.6194 | 2.2438 | 2300 | 0.3438 | 0.4243 | 0.3607 | {'rouge1': 0.6582903194787642, 'rouge2': 0.4586001978434361, 'rougeL': 0.657779263560347, 'rougeLsum': 0.6577134105856606} |
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- | 0.6327 | 2.3413 | 2400 | 0.3475 | 0.4403 | 0.3480 | {'rouge1': 0.6437891592593289, 'rouge2': 0.44349428929588836, 'rougeL': 0.6434560290609752, 'rougeLsum': 0.6434450349375109} |
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- | 0.6226 | 2.4388 | 2500 | 0.3396 | 0.4257 | 0.3641 | {'rouge1': 0.6540925308500153, 'rouge2': 0.4547444984600552, 'rougeL': 0.6535404954649788, 'rougeLsum': 0.6534025409179112} |
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- | 0.6175 | 2.5363 | 2600 | 0.3420 | 0.4238 | 0.3618 | {'rouge1': 0.6570293538260654, 'rouge2': 0.45661791700993576, 'rougeL': 0.6565349834626244, 'rougeLsum': 0.6563589283917969} |
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- | 0.6087 | 2.6338 | 2700 | 0.3378 | 0.4340 | 0.3562 | {'rouge1': 0.6470455955080758, 'rouge2': 0.4490930911063734, 'rougeL': 0.6470109060599167, 'rougeLsum': 0.6468990937615842} |
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- | 0.6136 | 2.7314 | 2800 | 0.3357 | 0.4340 | 0.3569 | {'rouge1': 0.6462711100612597, 'rouge2': 0.44787292745812923, 'rougeL': 0.6457431436135709, 'rougeLsum': 0.6457443356600687} |
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- | 0.603 | 2.8289 | 2900 | 0.3333 | 0.4211 | 0.3665 | {'rouge1': 0.658326692310857, 'rouge2': 0.4591672202918055, 'rougeL': 0.6579324587764817, 'rougeLsum': 0.657831017441442} |
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- | 0.6111 | 2.9264 | 3000 | 0.3278 | 0.4115 | 0.3769 | {'rouge1': 0.6680275850005191, 'rouge2': 0.47111044939536956, 'rougeL': 0.667594152546402, 'rougeLsum': 0.6675417104884547} |
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- | 0.5823 | 3.0244 | 3100 | 0.3259 | 0.4138 | 0.3738 | {'rouge1': 0.6658105281688206, 'rouge2': 0.46831258779891827, 'rougeL': 0.6654463258976879, 'rougeLsum': 0.6653623068947464} |
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- | 0.596 | 3.1219 | 3200 | 0.3291 | 0.4075 | 0.3857 | {'rouge1': 0.6760034844772485, 'rouge2': 0.48075322253291103, 'rougeL': 0.6753352830167898, 'rougeLsum': 0.6753654994638207} |
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- | 0.585 | 3.2194 | 3300 | 0.3218 | 0.4066 | 0.3854 | {'rouge1': 0.6708033471134076, 'rouge2': 0.4760878515017991, 'rougeL': 0.6704396025219446, 'rougeLsum': 0.6705375169622321} |
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- | 0.5966 | 3.3169 | 3400 | 0.3225 | 0.4046 | 0.3855 | {'rouge1': 0.6733506484650071, 'rouge2': 0.47866589105906143, 'rougeL': 0.6725823331259932, 'rougeLsum': 0.6726322301227529} |
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- | 0.5927 | 3.4144 | 3500 | 0.3225 | 0.4036 | 0.3837 | {'rouge1': 0.6763429608911748, 'rouge2': 0.4799554819160149, 'rougeL': 0.6760199809645441, 'rougeLsum': 0.6759470534272627} |
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- | 0.5916 | 3.5119 | 3600 | 0.3211 | 0.3978 | 0.3948 | {'rouge1': 0.6835849587362359, 'rouge2': 0.49067004972462647, 'rougeL': 0.6830631826963516, 'rougeLsum': 0.683021270557737} |
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- | 0.5985 | 3.6095 | 3700 | 0.3193 | 0.3950 | 0.3971 | {'rouge1': 0.6838483117017133, 'rouge2': 0.49042401786291456, 'rougeL': 0.6832829603653612, 'rougeLsum': 0.6833601954138526} |
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- | 0.5967 | 3.7070 | 3800 | 0.3196 | 0.3944 | 0.3977 | {'rouge1': 0.6832470151375148, 'rouge2': 0.48997365042491053, 'rougeL': 0.6828971433052323, 'rougeLsum': 0.6826154295596226} |
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- | 0.5861 | 3.8045 | 3900 | 0.3164 | 0.3953 | 0.3966 | {'rouge1': 0.6815194009337533, 'rouge2': 0.48901293150413283, 'rougeL': 0.6811599766209577, 'rougeLsum': 0.681167364543434} |
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- | 0.5744 | 3.9020 | 4000 | 0.3115 | 0.3988 | 0.3949 | {'rouge1': 0.677956894381051, 'rouge2': 0.48577956453068094, 'rougeL': 0.677533258370969, 'rougeLsum': 0.6775310259371528} |
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- | 0.6032 | 3.9995 | 4100 | 0.3159 | 0.4112 | 0.3833 | {'rouge1': 0.666858825858121, 'rouge2': 0.47345226473180235, 'rougeL': 0.666467300377577, 'rougeLsum': 0.666485598809812} |
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- | 0.5642 | 4.0975 | 4200 | 0.3107 | 0.3960 | 0.3965 | {'rouge1': 0.6798209619380302, 'rouge2': 0.4877476049191384, 'rougeL': 0.6792681025423539, 'rougeLsum': 0.679441807529453} |
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- | 0.5772 | 4.1950 | 4300 | 0.3124 | 0.4021 | 0.3907 | {'rouge1': 0.6769968634627295, 'rouge2': 0.4855589558164317, 'rougeL': 0.6763733852000889, 'rougeLsum': 0.6764190462864809} |
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- | 0.5667 | 4.2925 | 4400 | 0.3090 | 0.3989 | 0.3963 | {'rouge1': 0.6784269657572182, 'rouge2': 0.48622812356075396, 'rougeL': 0.6778490035977014, 'rougeLsum': 0.6776967017467932} |
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- | 0.5713 | 4.3901 | 4500 | 0.3080 | 0.3925 | 0.4029 | {'rouge1': 0.6803911080488889, 'rouge2': 0.4889196805179151, 'rougeL': 0.679897767360062, 'rougeLsum': 0.6799181233054294} |
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- | 0.575 | 4.4876 | 4600 | 0.3069 | 0.3890 | 0.4032 | {'rouge1': 0.6866256247806055, 'rouge2': 0.4947643833678781, 'rougeL': 0.6863600226556883, 'rougeLsum': 0.6862665256307836} |
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- | 0.5669 | 4.5851 | 4700 | 0.3042 | 0.3886 | 0.4048 | {'rouge1': 0.6873428477118608, 'rouge2': 0.49739034530961257, 'rougeL': 0.6869537171773277, 'rougeLsum': 0.6869787755670816} |
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- | 0.563 | 4.6826 | 4800 | 0.3085 | 0.4161 | 0.3792 | {'rouge1': 0.6615511703414414, 'rouge2': 0.4676904378632397, 'rougeL': 0.6614414779567332, 'rougeLsum': 0.661189370181686} |
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- | 0.5465 | 4.7801 | 4900 | 0.3041 | 0.3921 | 0.4008 | {'rouge1': 0.6845900895016952, 'rouge2': 0.4927761748243858, 'rougeL': 0.684031365756208, 'rougeLsum': 0.6840579744316673} |
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- | 0.5703 | 4.8776 | 5000 | 0.3009 | 0.3863 | 0.4069 | {'rouge1': 0.6886709663424477, 'rouge2': 0.49819776939282057, 'rougeL': 0.6881154820537174, 'rougeLsum': 0.6883937877015258} |
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- | 0.5591 | 4.9751 | 5100 | 0.3016 | 0.3881 | 0.4052 | {'rouge1': 0.6889386752704709, 'rouge2': 0.4983154761005506, 'rougeL': 0.6887126877038267, 'rougeLsum': 0.6888475857510377} |
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- | 0.5447 | 5.0731 | 5200 | 0.2988 | 0.3783 | 0.4174 | {'rouge1': 0.6970646487324009, 'rouge2': 0.5084578165741072, 'rougeL': 0.6968278006524977, 'rougeLsum': 0.696619847658498} |
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- | 0.5569 | 5.1706 | 5300 | 0.2979 | 0.3792 | 0.4168 | {'rouge1': 0.6961276289823202, 'rouge2': 0.5070859993554945, 'rougeL': 0.6958119782278983, 'rougeLsum': 0.6956873029334603} |
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- | 0.55 | 5.2682 | 5400 | 0.2971 | 0.3805 | 0.4133 | {'rouge1': 0.6955416923122605, 'rouge2': 0.5065597123927292, 'rougeL': 0.6952089770716992, 'rougeLsum': 0.6951133501808373} |
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- | 0.5488 | 5.3657 | 5500 | 0.2977 | 0.3764 | 0.4190 | {'rouge1': 0.6997508899061681, 'rouge2': 0.511478673711731, 'rougeL': 0.6995150573688741, 'rougeLsum': 0.6993470178997533} |
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- | 0.5616 | 5.4632 | 5600 | 0.2965 | 0.3732 | 0.4235 | {'rouge1': 0.7013465455303209, 'rouge2': 0.5139126595456542, 'rougeL': 0.7008859589300989, 'rougeLsum': 0.7008571800466421} |
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- | 0.5511 | 5.5607 | 5700 | 0.2955 | 0.3755 | 0.4198 | {'rouge1': 0.6979003572654013, 'rouge2': 0.5104101490913873, 'rougeL': 0.6974357128935622, 'rougeLsum': 0.6976939197435674} |
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- | 0.5475 | 5.6582 | 5800 | 0.2944 | 0.3744 | 0.4224 | {'rouge1': 0.6997485561362005, 'rouge2': 0.5117237925763095, 'rougeL': 0.699315501838293, 'rougeLsum': 0.6993609881095812} |
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- | 0.535 | 5.7557 | 5900 | 0.2943 | 0.3745 | 0.4227 | {'rouge1': 0.7004123958977562, 'rouge2': 0.5130778231448121, 'rougeL': 0.6999992378819626, 'rougeLsum': 0.6999034701936646} |
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- | 0.5381 | 5.8532 | 6000 | 0.2939 | 0.3756 | 0.4204 | {'rouge1': 0.6993128402555228, 'rouge2': 0.5118520741032332, 'rougeL': 0.6990872979187841, 'rougeLsum': 0.6988728786986375} |
121
- | 0.5542 | 5.9508 | 6100 | 0.2934 | 0.3763 | 0.4201 | {'rouge1': 0.6991211401254616, 'rouge2': 0.5113991327668065, 'rougeL': 0.6986732788231161, 'rougeLsum': 0.6986739135320972} |
122
 
123
 
124
  ### Framework versions
 
9
  - bleu
10
  - rouge
11
  model-index:
12
+ - name: kab-dz
13
  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
19
+ # kab-dz
20
 
21
  This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.3296
24
+ - Wer: 0.5537
25
+ - Bleu: {'bleu': 0.17822041427852187, 'precisions': [0.46242010138858275, 0.24001479289940827, 0.13158998741434763, 0.0734417780641005], 'brevity_penalty': 0.984798238899528, 'length_ratio': 0.9849126234668404, 'translation_length': 9074, 'reference_length': 9213}
26
+ - Rouge: {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0}
27
 
28
  ## Model description
29
 
 
42
  ### Training hyperparameters
43
 
44
  The following hyperparameters were used during training:
45
+ - learning_rate: 0.0001
46
  - train_batch_size: 8
47
+ - eval_batch_size: 16
48
  - seed: 42
49
  - gradient_accumulation_steps: 4
50
  - total_train_batch_size: 32
51
  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
52
  - lr_scheduler_type: linear
53
+ - lr_scheduler_warmup_steps: 500
54
+ - num_epochs: 30
55
  - mixed_precision_training: Native AMP
56
 
57
  ### Training results
58
 
59
+ | Training Loss | Epoch | Step | Validation Loss | Wer | Bleu | Rouge |
60
+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------:|
61
+ | 8.3957 | 1.0 | 121 | 6.4435 | 1.0002 | {'bleu': 0.0, 'precisions': [0.0, 0.0, 0.0, 0.0], 'brevity_penalty': 0.028413494474637858, 'length_ratio': 0.21925539997829155, 'translation_length': 2020, 'reference_length': 9213} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
62
+ | 3.8246 | 2.0 | 242 | 1.7852 | 1.0036 | {'bleu': 0.0, 'precisions': [0.0019450800915331808, 0.0, 0.0, 0.0], 'brevity_penalty': 0.9475361779864253, 'length_ratio': 0.9488654869178157, 'translation_length': 8740, 'reference_length': 9211} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
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+ | 0.8242 | 3.0 | 363 | 0.5552 | 0.7259 | {'bleu': 0.05984194666820544, 'precisions': [0.2893541597429932, 0.10006199628022319, 0.036166619757951025, 0.013298734998378203], 'brevity_penalty': 0.9796059773354316, 'length_ratio': 0.9798111364376425, 'translation_length': 9027, 'reference_length': 9213} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
64
+ | 0.6641 | 4.0 | 484 | 0.4531 | 0.6539 | {'bleu': 0.09740569316232665, 'precisions': [0.36318407960199006, 0.15155264134603488, 0.06640460480134774, 0.026528631510837918], 'brevity_penalty': 0.9815976322925238, 'length_ratio': 0.981764897427548, 'translation_length': 9045, 'reference_length': 9213} | {'rouge1': 0.0, 'rouge2': 0.0, 'rougeL': 0.0, 'rougeLsum': 0.0} |
65
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