Hugging Face's logo

Ragab167
/
output

Text2Text Generation
Transformers
Safetensors
m2m_100
Generated from Trainer
Inference Endpoints
Model card Files Community
output / README.md
Ragab167's picture
Ragab167
End of training
3fbf416 verified
preview code
|
raw
history blame
12.9 kB
metadata
license: mit
base_model: facebook/m2m100_418M
tags:
  - generated_from_trainer
model-index:
  - name: output
    results: []

output

This model is a fine-tuned version of facebook/m2m100_418M on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6652

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss
3.8819 0.07 100 0.7677
0.7529 0.13 200 0.6311
0.6575 0.2 300 0.6079
0.6478 0.27 400 0.5925
0.5882 0.33 500 0.5750
0.5882 0.4 600 0.5681
0.5635 0.47 700 0.5575
0.6301 0.53 800 0.5525
0.5667 0.6 900 0.5472
0.5591 0.66 1000 0.5430
0.5761 0.73 1100 0.5298
0.556 0.8 1200 0.5318
0.5664 0.86 1300 0.5235
0.5494 0.93 1400 0.5200
0.5416 1.0 1500 0.5171
0.4251 1.06 1600 0.5248
0.4447 1.13 1700 0.5271
0.437 1.2 1800 0.5172
0.4064 1.26 1900 0.5146
0.413 1.33 2000 0.5130
0.4364 1.4 2100 0.5132
0.4002 1.46 2200 0.5203
0.4441 1.53 2300 0.5078
0.4179 1.6 2400 0.5057
0.4438 1.66 2500 0.5039
0.4394 1.73 2600 0.5064
0.4581 1.8 2700 0.5007
0.4366 1.86 2800 0.4977
0.4464 1.93 2900 0.4965
0.447 1.99 3000 0.4940
0.3333 2.06 3100 0.5052
0.3355 2.13 3200 0.5053
0.3227 2.19 3300 0.5066
0.3298 2.26 3400 0.5072
0.3276 2.33 3500 0.5075
0.3252 2.39 3600 0.5025
0.3132 2.46 3700 0.5022
0.3247 2.53 3800 0.5062
0.3311 2.59 3900 0.5010
0.3385 2.66 4000 0.5019
0.3496 2.73 4100 0.5010
0.3164 2.79 4200 0.4975
0.3458 2.86 4300 0.4989
0.3288 2.93 4400 0.5002
0.3341 2.99 4500 0.5034
0.2293 3.06 4600 0.5090
0.2301 3.12 4700 0.5108
0.2253 3.19 4800 0.5088
0.2288 3.26 4900 0.5117
0.238 3.32 5000 0.5157
0.2487 3.39 5100 0.5129
0.2358 3.46 5200 0.5139
0.2491 3.52 5300 0.5185
0.2326 3.59 5400 0.5097
0.243 3.66 5500 0.5142
0.2635 3.72 5600 0.5094
0.2568 3.79 5700 0.5136
0.2608 3.86 5800 0.5053
0.2709 3.92 5900 0.5104
0.2442 3.99 6000 0.5116
0.183 4.06 6100 0.5199
0.1657 4.12 6200 0.5228
0.1537 4.19 6300 0.5230
0.1634 4.26 6400 0.5232
0.1679 4.32 6500 0.5270
0.1695 4.39 6600 0.5293
0.1872 4.45 6700 0.5279
0.1723 4.52 6800 0.5256
0.1624 4.59 6900 0.5320
0.1708 4.65 7000 0.5289
0.1826 4.72 7100 0.5369
0.1772 4.79 7200 0.5331
0.1672 4.85 7300 0.5287
0.1824 4.92 7400 0.5317
0.1782 4.99 7500 0.5309
0.1177 5.05 7600 0.5414
0.1114 5.12 7700 0.5450
0.1117 5.19 7800 0.5491
0.1118 5.25 7900 0.5474
0.1105 5.32 8000 0.5478
0.1015 5.39 8100 0.5515
0.1085 5.45 8200 0.5502
0.1165 5.52 8300 0.5581
0.1193 5.59 8400 0.5529
0.1233 5.65 8500 0.5556
0.122 5.72 8600 0.5494
0.1261 5.78 8700 0.5515
0.126 5.85 8800 0.5516
0.1165 5.92 8900 0.5488
0.1208 5.98 9000 0.5505
0.0772 6.05 9100 0.5591
0.0709 6.12 9200 0.5588
0.0759 6.18 9300 0.5642
0.0672 6.25 9400 0.5669
0.0736 6.32 9500 0.5630
0.0785 6.38 9600 0.5730
0.0721 6.45 9700 0.5720
0.0809 6.52 9800 0.5769
0.0787 6.58 9900 0.5790
0.0776 6.65 10000 0.5713
0.0821 6.72 10100 0.5713
0.0735 6.78 10200 0.5727
0.0742 6.85 10300 0.5780
0.0813 6.91 10400 0.5747
0.0823 6.98 10500 0.5731
0.0521 7.05 10600 0.5849
0.0471 7.11 10700 0.5842
0.0433 7.18 10800 0.5870
0.0463 7.25 10900 0.5889
0.0512 7.31 11000 0.5913
0.0461 7.38 11100 0.5874
0.0521 7.45 11200 0.5943
0.0434 7.51 11300 0.5940
0.0522 7.58 11400 0.5980
0.0607 7.65 11500 0.5891
0.049 7.71 11600 0.5916
0.0494 7.78 11700 0.5960
0.0526 7.85 11800 0.5942
0.0505 7.91 11900 0.5972
0.0579 7.98 12000 0.5930
0.038 8.05 12100 0.6054
0.0295 8.11 12200 0.6017
0.0303 8.18 12300 0.6020
0.0348 8.24 12400 0.6052
0.0318 8.31 12500 0.6103
0.0369 8.38 12600 0.6079
0.0373 8.44 12700 0.6050
0.0319 8.51 12800 0.6095
0.0348 8.58 12900 0.6066
0.0326 8.64 13000 0.6084
0.0335 8.71 13100 0.6148
0.0303 8.78 13200 0.6142
0.0409 8.84 13300 0.6190
0.0357 8.91 13400 0.6121
0.0351 8.98 13500 0.6121
0.0254 9.04 13600 0.6203
0.0215 9.11 13700 0.6235
0.0214 9.18 13800 0.6243
0.0226 9.24 13900 0.6199
0.0224 9.31 14000 0.6225
0.0226 9.38 14100 0.6236
0.0224 9.44 14200 0.6261
0.0262 9.51 14300 0.6259
0.022 9.57 14400 0.6223
0.0248 9.64 14500 0.6275
0.0236 9.71 14600 0.6261
0.022 9.77 14700 0.6303
0.0225 9.84 14800 0.6290
0.0248 9.91 14900 0.6299
0.0233 9.97 15000 0.6302
0.021 10.04 15100 0.6297
0.0153 10.11 15200 0.6355
0.015 10.17 15300 0.6321
0.0153 10.24 15400 0.6349
0.0168 10.31 15500 0.6310
0.0155 10.37 15600 0.6352
0.0153 10.44 15700 0.6391
0.0189 10.51 15800 0.6373
0.0166 10.57 15900 0.6370
0.016 10.64 16000 0.6348
0.0191 10.7 16100 0.6381
0.0172 10.77 16200 0.6394
0.0171 10.84 16300 0.6408
0.0185 10.9 16400 0.6378
0.0167 10.97 16500 0.6437
0.016 11.04 16600 0.6447
0.0127 11.1 16700 0.6408
0.0131 11.17 16800 0.6454
0.0117 11.24 16900 0.6471
0.0125 11.3 17000 0.6484
0.0135 11.37 17100 0.6517
0.0122 11.44 17200 0.6462
0.0132 11.5 17300 0.6505
0.012 11.57 17400 0.6524
0.0152 11.64 17500 0.6491
0.0147 11.7 17600 0.6506
0.0144 11.77 17700 0.6482
0.0143 11.84 17800 0.6482
0.0121 11.9 17900 0.6475
0.0131 11.97 18000 0.6480
0.0113 12.03 18100 0.6491
0.0117 12.1 18200 0.6543
0.0092 12.17 18300 0.6575
0.0102 12.23 18400 0.6530
0.0099 12.3 18500 0.6612
0.0099 12.37 18600 0.6547
0.0089 12.43 18700 0.6553
0.01 12.5 18800 0.6581
0.0092 12.57 18900 0.6579
0.0092 12.63 19000 0.6558
0.0099 12.7 19100 0.6563
0.0099 12.77 19200 0.6578
0.0103 12.83 19300 0.6589
0.0093 12.9 19400 0.6582
0.0093 12.97 19500 0.6582
0.0078 13.03 19600 0.6604
0.0073 13.1 19700 0.6606
0.0082 13.16 19800 0.6582
0.0075 13.23 19900 0.6614
0.0073 13.3 20000 0.6636
0.0072 13.36 20100 0.6578
0.0074 13.43 20200 0.6606
0.009 13.5 20300 0.6623
0.0149 13.56 20400 0.6615
0.0078 13.63 20500 0.6616
0.0069 13.7 20600 0.6653
0.0085 13.76 20700 0.6607
0.0074 13.83 20800 0.6619
0.0088 13.9 20900 0.6621
0.0069 13.96 21000 0.6613
0.0076 14.03 21100 0.6630
0.0062 14.1 21200 0.6635
0.007 14.16 21300 0.6623
0.0066 14.23 21400 0.6627
0.0067 14.3 21500 0.6620
0.0066 14.36 21600 0.6604
0.0068 14.43 21700 0.6620
0.0069 14.49 21800 0.6629
0.0088 14.56 21900 0.6625
0.0069 14.63 22000 0.6642
0.0063 14.69 22100 0.6645
0.0074 14.76 22200 0.6652
0.0053 14.83 22300 0.6652
0.0076 14.89 22400 0.6652
0.0068 14.96 22500 0.6652

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.2