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patentbert-cased-2b

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metadata
license: mit
base_model: dheerajpai/patentbert
tags:
  - generated_from_trainer
model-index:
  - name: patentbert-cased-2b
    results: []

patentbert-cased-2b

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

  • Loss: 1.6765

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: 32
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
No log 0.0 500 9.7138
No log 0.01 1000 8.8994
No log 0.01 1500 8.0568
No log 0.02 2000 7.1249
No log 0.02 2500 6.7644
No log 0.02 3000 6.6609
No log 0.03 3500 6.4539
No log 0.03 4000 6.4159
No log 0.04 4500 6.2912
7.4382 0.04 5000 6.2655
7.4382 0.04 5500 6.1653
7.4382 0.05 6000 6.0521
7.4382 0.05 6500 6.0127
7.4382 0.06 7000 5.9010
7.4382 0.06 7500 5.6470
7.4382 0.06 8000 5.9284
7.4382 0.07 8500 5.8607
7.4382 0.07 9000 5.6770
7.4382 0.08 9500 5.6702
5.909 0.08 10000 5.7809
5.909 0.08 10500 5.6887
5.909 0.09 11000 5.5835
5.909 0.09 11500 5.4876
5.909 0.1 12000 5.3873
5.909 0.1 12500 5.3155
5.909 0.1 13000 5.4199
5.909 0.11 13500 5.4683
5.909 0.11 14000 5.5431
5.909 0.12 14500 5.2682
5.452 0.12 15000 5.3033
5.452 0.12 15500 5.1011
5.452 0.13 16000 5.0596
5.452 0.13 16500 5.2932
5.452 0.14 17000 5.1327
5.452 0.14 17500 5.1718
5.452 0.14 18000 5.0993
5.452 0.15 18500 5.0052
5.452 0.15 19000 5.1058
5.452 0.16 19500 5.1275
5.1622 0.16 20000 4.9027
5.1622 0.16 20500 4.9368
5.1622 0.17 21000 5.0207
5.1622 0.17 21500 5.0132
5.1622 0.18 22000 4.8983
5.1622 0.18 22500 5.0904
5.1622 0.18 23000 4.9643
5.1622 0.19 23500 4.8202
5.1622 0.19 24000 4.9618
5.1622 0.2 24500 4.8981
4.9639 0.2 25000 4.9170
4.9639 0.2 25500 4.8487
4.9639 0.21 26000 4.9493
4.9639 0.21 26500 4.7741
4.9639 0.22 27000 4.6247
4.9639 0.22 27500 4.8149
4.9639 0.22 28000 4.7340
4.9639 0.23 28500 4.6638
4.9639 0.23 29000 4.4906
4.9639 0.24 29500 4.4666
4.7493 0.24 30000 4.3591
4.7493 0.24 30500 4.3064
4.7493 0.25 31000 4.1517
4.7493 0.25 31500 4.2189
4.7493 0.26 32000 3.9452
4.7493 0.26 32500 4.2082
4.7493 0.26 33000 4.1326
4.7493 0.27 33500 3.9694
4.7493 0.27 34000 4.0213
4.7493 0.28 34500 3.7256
4.2572 0.28 35000 3.9048
4.2572 0.28 35500 3.7937
4.2572 0.29 36000 3.5790
4.2572 0.29 36500 3.7600
4.2572 0.29 37000 3.5873
4.2572 0.3 37500 3.4409
4.2572 0.3 38000 3.4437
4.2572 0.31 38500 3.2380
4.2572 0.31 39000 3.1350
4.2572 0.31 39500 3.2821
3.7 0.32 40000 3.1141
3.7 0.32 40500 2.8792
3.7 0.33 41000 2.9130
3.7 0.33 41500 2.7695
3.7 0.33 42000 2.7399
3.7 0.34 42500 3.0070
3.7 0.34 43000 2.7522
3.7 0.35 43500 2.7255
3.7 0.35 44000 2.3562
3.7 0.35 44500 2.7340
3.0512 0.36 45000 2.5456
3.0512 0.36 45500 2.6832
3.0512 0.37 46000 2.5833
3.0512 0.37 46500 2.5323
3.0512 0.37 47000 2.4608
3.0512 0.38 47500 2.5094
3.0512 0.38 48000 2.2950
3.0512 0.39 48500 2.3787
3.0512 0.39 49000 2.3364
3.0512 0.39 49500 2.2081
2.7005 0.4 50000 2.4490
2.7005 0.4 50500 2.5215
2.7005 0.41 51000 2.2109
2.7005 0.41 51500 2.0476
2.7005 0.41 52000 2.5112
2.7005 0.42 52500 2.3243
2.7005 0.42 53000 2.1928
2.7005 0.43 53500 2.2190
2.7005 0.43 54000 2.2165
2.7005 0.43 54500 2.1837
2.4756 0.44 55000 2.1097
2.4756 0.44 55500 2.1694
2.4756 0.45 56000 2.0265
2.4756 0.45 56500 2.0210
2.4756 0.45 57000 1.9137
2.4756 0.46 57500 2.0189
2.4756 0.46 58000 2.1363
2.4756 0.47 58500 2.0439
2.4756 0.47 59000 2.1116
2.4756 0.47 59500 2.0844
2.3096 0.48 60000 2.0552
2.3096 0.48 60500 1.9667
2.3096 0.49 61000 1.8774
2.3096 0.49 61500 2.0857
2.3096 0.49 62000 2.2166
2.3096 0.5 62500 1.9270
2.3096 0.5 63000 1.9487
2.3096 0.51 63500 1.9888
2.3096 0.51 64000 2.0290
2.3096 0.51 64500 2.0329
2.2043 0.52 65000 2.1624
2.2043 0.52 65500 1.7746
2.2043 0.53 66000 2.2028
2.2043 0.53 66500 2.0827
2.2043 0.53 67000 1.9982
2.2043 0.54 67500 2.0323
2.2043 0.54 68000 2.0935
2.2043 0.55 68500 1.8756
2.2043 0.55 69000 2.0685
2.2043 0.55 69500 1.7008
2.1246 0.56 70000 1.8077
2.1246 0.56 70500 1.6410
2.1246 0.57 71000 2.1809
2.1246 0.57 71500 1.9749
2.1246 0.57 72000 2.0454
2.1246 0.58 72500 1.8338
2.1246 0.58 73000 2.0519
2.1246 0.59 73500 1.8969
2.1246 0.59 74000 1.9628
2.1246 0.59 74500 1.8511
2.0501 0.6 75000 1.7241
2.0501 0.6 75500 1.9739
2.0501 0.61 76000 1.7898
2.0501 0.61 76500 1.8359
2.0501 0.61 77000 1.6916
2.0501 0.62 77500 1.8907
2.0501 0.62 78000 1.8675
2.0501 0.63 78500 1.6473
2.0501 0.63 79000 2.0039
2.0501 0.63 79500 1.7961
2.0036 0.64 80000 1.9772
2.0036 0.64 80500 1.9374
2.0036 0.65 81000 1.9039
2.0036 0.65 81500 1.7710
2.0036 0.65 82000 1.7382
2.0036 0.66 82500 1.9952
2.0036 0.66 83000 1.6185
2.0036 0.67 83500 1.8987
2.0036 0.67 84000 1.7178
2.0036 0.67 84500 1.8065
1.9663 0.68 85000 1.6718
1.9663 0.68 85500 1.7911
1.9663 0.69 86000 1.8223
1.9663 0.69 86500 1.7343
1.9663 0.69 87000 1.8141
1.9663 0.7 87500 1.6959
1.9663 0.7 88000 1.7000
1.9663 0.71 88500 1.8956
1.9663 0.71 89000 1.7486
1.9663 0.71 89500 1.7521
1.9217 0.72 90000 1.7994
1.9217 0.72 90500 1.6972
1.9217 0.73 91000 1.7402
1.9217 0.73 91500 2.0969
1.9217 0.73 92000 1.9346
1.9217 0.74 92500 1.7400
1.9217 0.74 93000 1.6087
1.9217 0.75 93500 1.9118
1.9217 0.75 94000 1.5671
1.9217 0.75 94500 1.8391
1.8971 0.76 95000 1.5498
1.8971 0.76 95500 1.8260
1.8971 0.77 96000 1.9168
1.8971 0.77 96500 1.6989
1.8971 0.77 97000 1.6661
1.8971 0.78 97500 1.6856
1.8971 0.78 98000 1.7222
1.8971 0.79 98500 1.6734
1.8971 0.79 99000 1.7253
1.8971 0.79 99500 1.5505
1.8712 0.8 100000 1.6383
1.8712 0.8 100500 1.8282
1.8712 0.81 101000 1.6067
1.8712 0.81 101500 1.7311
1.8712 0.81 102000 1.6562
1.8712 0.82 102500 1.5626
1.8712 0.82 103000 1.7117
1.8712 0.83 103500 1.6085
1.8712 0.83 104000 1.6914
1.8712 0.83 104500 1.7433
1.8537 0.84 105000 1.5394
1.8537 0.84 105500 1.6920
1.8537 0.85 106000 1.8206
1.8537 0.85 106500 1.7831
1.8537 0.85 107000 1.7058
1.8537 0.86 107500 1.6986
1.8537 0.86 108000 1.5653
1.8537 0.86 108500 1.8101
1.8537 0.87 109000 1.6472
1.8537 0.87 109500 1.7624
1.8317 0.88 110000 1.7655
1.8317 0.88 110500 1.6391
1.8317 0.88 111000 1.6167
1.8317 0.89 111500 1.6827
1.8317 0.89 112000 1.6433
1.8317 0.9 112500 1.7570
1.8317 0.9 113000 1.6109
1.8317 0.9 113500 1.5238
1.8317 0.91 114000 1.6575
1.8317 0.91 114500 1.6388
1.8231 0.92 115000 1.7069
1.8231 0.92 115500 1.5599
1.8231 0.92 116000 1.5553
1.8231 0.93 116500 1.7457
1.8231 0.93 117000 1.5716
1.8231 0.94 117500 1.7186
1.8231 0.94 118000 1.6921
1.8231 0.94 118500 1.5303
1.8231 0.95 119000 1.6168
1.8231 0.95 119500 1.6569
1.8113 0.96 120000 1.7487
1.8113 0.96 120500 1.7703
1.8113 0.96 121000 1.5803
1.8113 0.97 121500 1.7256
1.8113 0.97 122000 1.5522
1.8113 0.98 122500 1.8039
1.8113 0.98 123000 1.6774
1.8113 0.98 123500 1.8046
1.8113 0.99 124000 1.6236
1.8113 0.99 124500 1.7422
1.8063 1.0 125000 1.6765

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.1