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deberta-v3-large-sentiment

Text Classification
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deberta-v2
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deberta-v3-large-sentiment / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: deberta-v3-large-sentiment-lr5e-6-gas2-ls0.0
    results: []

deberta-v3-large-sentiment-lr5e-6-gas2-ls0.0

This model is a fine-tuned version of microsoft/deberta-v3-large on an tweet_eval/sentiment dataset. It achieves the following results on the evaluation set:

  • Loss: 1.3253
  • Accuracy: 0.7365

Model description

Model Emotion Hate Irony Offensive Sentiment Average
deberta-v3-large 86.28 61.28 87.12 86.4 73.93

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-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 50
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.0614 0.07 100 1.0196 0.4345
0.8601 0.14 200 0.7561 0.6460
0.734 0.21 300 0.6796 0.6955
0.6753 0.28 400 0.6521 0.7000
0.6408 0.35 500 0.6119 0.7440
0.5991 0.42 600 0.6034 0.7370
0.6069 0.49 700 0.5976 0.7375
0.6122 0.56 800 0.5871 0.7425
0.5908 0.63 900 0.5935 0.7445
0.5884 0.7 1000 0.5792 0.7520
0.5839 0.77 1100 0.5780 0.7555
0.5772 0.84 1200 0.5727 0.7570
0.5895 0.91 1300 0.5601 0.7550
0.5757 0.98 1400 0.5613 0.7525
0.5121 1.05 1500 0.5867 0.7600
0.5254 1.12 1600 0.5595 0.7630
0.5074 1.19 1700 0.5594 0.7585
0.4947 1.26 1800 0.5697 0.7575
0.5019 1.33 1900 0.5665 0.7580
0.5005 1.4 2000 0.5484 0.7655
0.5125 1.47 2100 0.5626 0.7605
0.5241 1.54 2200 0.5561 0.7560
0.5198 1.61 2300 0.5602 0.7600
0.5124 1.68 2400 0.5654 0.7490
0.5096 1.75 2500 0.5803 0.7515
0.4885 1.82 2600 0.5889 0.75
0.5111 1.89 2700 0.5508 0.7665
0.4868 1.96 2800 0.5621 0.7635
0.4599 2.04 2900 0.5995 0.7615
0.4147 2.11 3000 0.6202 0.7530
0.4233 2.18 3100 0.5875 0.7625
0.4324 2.25 3200 0.5794 0.7610
0.4141 2.32 3300 0.5902 0.7460
0.4306 2.39 3400 0.6053 0.7545
0.4266 2.46 3500 0.5979 0.7570
0.4227 2.53 3600 0.5920 0.7650
0.4226 2.6 3700 0.6166 0.7455
0.3978 2.67 3800 0.6126 0.7560
0.3954 2.74 3900 0.6152 0.7550
0.4209 2.81 4000 0.5980 0.75
0.3982 2.88 4100 0.6096 0.7490
0.4016 2.95 4200 0.6541 0.7425
0.3966 3.02 4300 0.6377 0.7545
0.3074 3.09 4400 0.6860 0.75
0.3551 3.16 4500 0.6160 0.7550
0.3323 3.23 4600 0.6714 0.7520
0.3171 3.3 4700 0.6538 0.7535
0.3403 3.37 4800 0.6774 0.7465
0.3396 3.44 4900 0.6726 0.7465
0.3259 3.51 5000 0.6465 0.7480
0.3392 3.58 5100 0.6860 0.7460
0.3251 3.65 5200 0.6697 0.7495
0.3253 3.72 5300 0.6770 0.7430
0.3455 3.79 5400 0.7177 0.7360
0.3323 3.86 5500 0.6943 0.7400
0.3335 3.93 5600 0.6507 0.7555
0.3368 4.0 5700 0.6580 0.7485
0.2479 4.07 5800 0.7667 0.7430
0.2613 4.14 5900 0.7513 0.7505
0.2557 4.21 6000 0.7927 0.7485
0.243 4.28 6100 0.7792 0.7450
0.2473 4.35 6200 0.8107 0.7355
0.2447 4.42 6300 0.7851 0.7370
0.2515 4.49 6400 0.7529 0.7465
0.274 4.56 6500 0.7390 0.7465
0.2674 4.63 6600 0.7658 0.7460
0.2416 4.7 6700 0.7915 0.7485
0.2432 4.77 6800 0.7989 0.7435
0.2595 4.84 6900 0.7850 0.7380
0.2736 4.91 7000 0.7577 0.7395
0.2783 4.98 7100 0.7650 0.7405
0.2304 5.05 7200 0.8542 0.7385
0.1937 5.12 7300 0.8390 0.7345
0.1878 5.19 7400 0.9150 0.7330
0.1921 5.26 7500 0.8792 0.7405
0.1916 5.33 7600 0.8892 0.7410
0.2011 5.4 7700 0.9012 0.7325
0.211 5.47 7800 0.8608 0.7420
0.2194 5.54 7900 0.8852 0.7320
0.205 5.61 8000 0.8803 0.7385
0.1981 5.68 8100 0.8681 0.7330
0.1908 5.75 8200 0.9020 0.7435
0.1942 5.82 8300 0.8780 0.7410
0.1958 5.89 8400 0.8937 0.7345
0.1883 5.96 8500 0.9121 0.7360
0.1819 6.04 8600 0.9409 0.7430
0.145 6.11 8700 1.1390 0.7265
0.1696 6.18 8800 0.9189 0.7430
0.1488 6.25 8900 0.9718 0.7400
0.1637 6.32 9000 0.9702 0.7450
0.1547 6.39 9100 1.0033 0.7410
0.1605 6.46 9200 0.9973 0.7355
0.1552 6.53 9300 1.0491 0.7290
0.1731 6.6 9400 1.0271 0.7335
0.1738 6.67 9500 0.9575 0.7430
0.1669 6.74 9600 0.9614 0.7350
0.1347 6.81 9700 1.0263 0.7365
0.1593 6.88 9800 1.0173 0.7360
0.1549 6.95 9900 1.0398 0.7350
0.1675 7.02 10000 0.9975 0.7380
0.1182 7.09 10100 1.1059 0.7350
0.1351 7.16 10200 1.0933 0.7400
0.1496 7.23 10300 1.0731 0.7355
0.1197 7.3 10400 1.1089 0.7360
0.1111 7.37 10500 1.1381 0.7405
0.1494 7.44 10600 1.0252 0.7425
0.1235 7.51 10700 1.0906 0.7360
0.133 7.58 10800 1.1796 0.7375
0.1248 7.65 10900 1.1332 0.7420
0.1268 7.72 11000 1.1304 0.7415
0.1368 7.79 11100 1.1345 0.7380
0.1228 7.86 11200 1.2018 0.7320
0.1281 7.93 11300 1.1884 0.7350
0.1449 8.0 11400 1.1571 0.7345
0.1025 8.07 11500 1.1538 0.7345
0.1199 8.14 11600 1.2113 0.7390
0.1016 8.21 11700 1.2882 0.7370
0.114 8.28 11800 1.2872 0.7390
0.1019 8.35 11900 1.2876 0.7380
0.1142 8.42 12000 1.2791 0.7385
0.1135 8.49 12100 1.2883 0.7380
0.1139 8.56 12200 1.2829 0.7360
0.1107 8.63 12300 1.2698 0.7365
0.1183 8.7 12400 1.2660 0.7345
0.1064 8.77 12500 1.2889 0.7365
0.0895 8.84 12600 1.3480 0.7330
0.1244 8.91 12700 1.2872 0.7325
0.1209 8.98 12800 1.2681 0.7375
0.1144 9.05 12900 1.2711 0.7370
0.1034 9.12 13000 1.2801 0.7360
0.113 9.19 13100 1.2801 0.7350
0.0994 9.26 13200 1.2920 0.7360
0.0966 9.33 13300 1.2761 0.7335
0.0939 9.4 13400 1.2909 0.7365
0.0975 9.47 13500 1.2953 0.7360
0.0842 9.54 13600 1.3179 0.7335
0.0871 9.61 13700 1.3149 0.7385
0.1162 9.68 13800 1.3124 0.7350
0.085 9.75 13900 1.3207 0.7355
0.0966 9.82 14000 1.3248 0.7335
0.1064 9.89 14100 1.3261 0.7335
0.1046 9.96 14200 1.3255 0.7360

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.9.0
  • Datasets 2.2.2
  • Tokenizers 0.11.6