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bert-base-multilingual-cased-allnli_tr

Zero-Shot Classification
Transformers
PyTorch
Turkish
bert
text-classification
nli
Inference Endpoints
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metadata
language:
  - tr
tags:
  - zero-shot-classification
  - nli
  - pytorch
pipeline_tag: zero-shot-classification
license: mit
datasets:
  - nli_tr
metrics:
  - accuracy
widget:
  - text: Dolar yükselmeye devam ediyor.
    candidate_labels: ekonomi, siyaset, spor
  - text: Senaryo çok saçmaydı, beğendim diyemem.
    candidate_labels: olumlu, olumsuz

bert-base-multilingual-cased_allnli_tr

This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6144
  • Accuracy: 0.7662

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.8623 0.03 1000 0.9076 0.5917
0.7528 0.07 2000 0.8587 0.6119
0.7074 0.1 3000 0.7867 0.6647
0.6949 0.14 4000 0.7474 0.6772
0.6681 0.17 5000 0.7661 0.6814
0.6597 0.2 6000 0.7264 0.6943
0.6495 0.24 7000 0.7841 0.6781
0.6323 0.27 8000 0.7256 0.6952
0.6308 0.31 9000 0.7319 0.6958
0.6254 0.34 10000 0.7054 0.7004
0.6233 0.37 11000 0.7069 0.7085
0.6165 0.41 12000 0.6880 0.7181
0.6033 0.44 13000 0.6844 0.7197
0.6014 0.48 14000 0.6753 0.7129
0.5947 0.51 15000 0.7000 0.7039
0.5965 0.54 16000 0.6708 0.7263
0.5979 0.58 17000 0.6562 0.7285
0.5787 0.61 18000 0.6554 0.7297
0.58 0.65 19000 0.6544 0.7315
0.574 0.68 20000 0.6549 0.7339
0.5751 0.71 21000 0.6545 0.7289
0.5659 0.75 22000 0.6467 0.7371
0.5732 0.78 23000 0.6448 0.7362
0.5637 0.82 24000 0.6520 0.7355
0.5648 0.85 25000 0.6412 0.7345
0.5622 0.88 26000 0.6350 0.7358
0.5579 0.92 27000 0.6347 0.7393
0.5518 0.95 28000 0.6417 0.7392
0.5547 0.99 29000 0.6321 0.7437
0.524 1.02 30000 0.6430 0.7412
0.4982 1.05 31000 0.6253 0.7458
0.5002 1.09 32000 0.6316 0.7418
0.4993 1.12 33000 0.6197 0.7487
0.4963 1.15 34000 0.6307 0.7462
0.504 1.19 35000 0.6272 0.7480
0.4922 1.22 36000 0.6410 0.7433
0.5016 1.26 37000 0.6295 0.7461
0.4957 1.29 38000 0.6183 0.7506
0.4883 1.32 39000 0.6261 0.7502
0.4985 1.36 40000 0.6315 0.7496
0.4885 1.39 41000 0.6189 0.7529
0.4909 1.43 42000 0.6189 0.7473
0.4894 1.46 43000 0.6314 0.7433
0.4912 1.49 44000 0.6184 0.7446
0.4851 1.53 45000 0.6258 0.7461
0.4879 1.56 46000 0.6286 0.7480
0.4907 1.6 47000 0.6196 0.7512
0.4884 1.63 48000 0.6157 0.7526
0.4755 1.66 49000 0.6056 0.7591
0.4811 1.7 50000 0.5977 0.7582
0.4787 1.73 51000 0.5915 0.7621
0.4779 1.77 52000 0.6014 0.7583
0.4767 1.8 53000 0.6041 0.7623
0.4737 1.83 54000 0.6093 0.7563
0.4836 1.87 55000 0.6001 0.7568
0.4765 1.9 56000 0.6109 0.7601
0.4776 1.94 57000 0.6046 0.7599
0.4769 1.97 58000 0.5970 0.7568
0.4654 2.0 59000 0.6147 0.7614
0.4144 2.04 60000 0.6439 0.7566
0.4101 2.07 61000 0.6373 0.7527
0.4192 2.11 62000 0.6136 0.7575
0.4128 2.14 63000 0.6283 0.7560
0.4204 2.17 64000 0.6187 0.7625
0.4114 2.21 65000 0.6127 0.7621
0.4097 2.24 66000 0.6188 0.7626
0.4129 2.28 67000 0.6156 0.7639
0.4085 2.31 68000 0.6232 0.7616
0.4074 2.34 69000 0.6240 0.7605
0.409 2.38 70000 0.6153 0.7591
0.4046 2.41 71000 0.6375 0.7587
0.4117 2.45 72000 0.6145 0.7629
0.4002 2.48 73000 0.6279 0.7610
0.4042 2.51 74000 0.6176 0.7646
0.4055 2.55 75000 0.6277 0.7643
0.4021 2.58 76000 0.6196 0.7642
0.4081 2.62 77000 0.6127 0.7659
0.408 2.65 78000 0.6237 0.7638
0.3997 2.68 79000 0.6190 0.7636
0.4093 2.72 80000 0.6152 0.7648
0.4095 2.75 81000 0.6155 0.7627
0.4088 2.79 82000 0.6130 0.7641
0.4063 2.82 83000 0.6072 0.7646
0.3978 2.85 84000 0.6128 0.7662
0.4034 2.89 85000 0.6157 0.7627
0.4044 2.92 86000 0.6127 0.7661
0.403 2.96 87000 0.6126 0.7664
0.4033 2.99 88000 0.6144 0.7662

Framework versions

  • Transformers 4.12.3
  • Pytorch 1.10.0+cu102
  • Datasets 1.15.1
  • Tokenizers 0.10.3