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gpt2-ner-invoiceSenderRecipient_all_inv_03_01

Token Classification
Transformers
PyTorch
gpt2
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
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  ---
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  tags:
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  - generated_from_trainer
 
 
 
 
 
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  model-index:
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  - name: gpt2-ner-invoiceSenderRecipient_all_inv_03_01
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  results: []
@@ -11,17 +16,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # gpt2-ner-invoiceSenderRecipient_all_inv_03_01
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- This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - eval_loss: 0.0334
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- - eval_precision: 0.7661
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- - eval_recall: 0.8493
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- - eval_f1: 0.8056
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- - eval_accuracy: 0.9885
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- - eval_runtime: 984.499
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- - eval_samples_per_second: 28.965
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- - eval_steps_per_second: 7.241
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- - step: 0
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  ## Model description
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@@ -49,9 +50,94 @@ The following hyperparameters were used during training:
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  - num_epochs: 1
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  - mixed_precision_training: Native AMP
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  ### Framework versions
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  - Transformers 4.22.0
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  - Pytorch 1.10.0
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- - Datasets 2.3.2
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  - Tokenizers 0.12.1
 
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  ---
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  tags:
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  - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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  model-index:
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  - name: gpt2-ner-invoiceSenderRecipient_all_inv_03_01
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  results: []
 
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  # gpt2-ner-invoiceSenderRecipient_all_inv_03_01
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+ This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0307
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+ - Precision: 0.7932
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+ - Recall: 0.8488
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+ - F1: 0.8201
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+ - Accuracy: 0.9895
 
 
 
 
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  ## Model description
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  - num_epochs: 1
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  - mixed_precision_training: Native AMP
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0363 | 0.01 | 500 | 0.0338 | 0.7846 | 0.7969 | 0.7907 | 0.9884 |
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+ | 0.0392 | 0.02 | 1000 | 0.0346 | 0.7665 | 0.8211 | 0.7929 | 0.9881 |
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+ | 0.0363 | 0.04 | 1500 | 0.0347 | 0.7701 | 0.8075 | 0.7884 | 0.9880 |
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+ | 0.0396 | 0.05 | 2000 | 0.0347 | 0.7454 | 0.8375 | 0.7888 | 0.9879 |
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+ | 0.0366 | 0.06 | 2500 | 0.0350 | 0.7519 | 0.8345 | 0.7911 | 0.9879 |
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+ | 0.0382 | 0.07 | 3000 | 0.0356 | 0.7500 | 0.8434 | 0.7939 | 0.9877 |
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+ | 0.0424 | 0.09 | 3500 | 0.0358 | 0.7517 | 0.8287 | 0.7883 | 0.9877 |
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+ | 0.0385 | 0.1 | 4000 | 0.0352 | 0.7605 | 0.8225 | 0.7903 | 0.9880 |
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+ | 0.0382 | 0.11 | 4500 | 0.0361 | 0.7494 | 0.8159 | 0.7813 | 0.9874 |
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+ | 0.0372 | 0.12 | 5000 | 0.0345 | 0.7817 | 0.8044 | 0.7929 | 0.9885 |
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+ | 0.0377 | 0.14 | 5500 | 0.0346 | 0.7749 | 0.8238 | 0.7986 | 0.9884 |
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+ | 0.0383 | 0.15 | 6000 | 0.0359 | 0.7568 | 0.8341 | 0.7936 | 0.9879 |
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+ | 0.0372 | 0.16 | 6500 | 0.0356 | 0.7548 | 0.8356 | 0.7932 | 0.9879 |
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+ | 0.0371 | 0.17 | 7000 | 0.0352 | 0.7540 | 0.8477 | 0.7981 | 0.9880 |
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+ | 0.0368 | 0.19 | 7500 | 0.0349 | 0.7662 | 0.8310 | 0.7973 | 0.9881 |
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+ | 0.0388 | 0.2 | 8000 | 0.0339 | 0.7648 | 0.8336 | 0.7977 | 0.9883 |
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+ | 0.0368 | 0.21 | 8500 | 0.0336 | 0.7729 | 0.8305 | 0.8006 | 0.9886 |
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+ | 0.0389 | 0.22 | 9000 | 0.0340 | 0.7750 | 0.8208 | 0.7972 | 0.9884 |
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+ | 0.0384 | 0.24 | 9500 | 0.0349 | 0.7549 | 0.8499 | 0.7996 | 0.9880 |
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+ | 0.0376 | 0.25 | 10000 | 0.0358 | 0.7531 | 0.8390 | 0.7938 | 0.9875 |
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+ | 0.0354 | 0.26 | 10500 | 0.0346 | 0.7650 | 0.8318 | 0.7970 | 0.9882 |
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+ | 0.0358 | 0.27 | 11000 | 0.0338 | 0.7694 | 0.8397 | 0.8030 | 0.9886 |
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+ | 0.0389 | 0.28 | 11500 | 0.0341 | 0.7586 | 0.8502 | 0.8018 | 0.9882 |
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+ | 0.0383 | 0.3 | 12000 | 0.0342 | 0.7688 | 0.8275 | 0.7971 | 0.9881 |
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+ | 0.0355 | 0.31 | 12500 | 0.0337 | 0.7783 | 0.8281 | 0.8024 | 0.9885 |
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+ | 0.0372 | 0.32 | 13000 | 0.0338 | 0.7703 | 0.8399 | 0.8036 | 0.9884 |
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+ | 0.0369 | 0.33 | 13500 | 0.0331 | 0.7683 | 0.8427 | 0.8038 | 0.9886 |
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+ | 0.0361 | 0.35 | 14000 | 0.0336 | 0.7699 | 0.8322 | 0.7999 | 0.9885 |
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+ | 0.0361 | 0.36 | 14500 | 0.0336 | 0.7735 | 0.8390 | 0.8049 | 0.9885 |
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+ | 0.0372 | 0.37 | 15000 | 0.0333 | 0.7747 | 0.8343 | 0.8034 | 0.9887 |
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+ | 0.0366 | 0.38 | 15500 | 0.0343 | 0.7646 | 0.8468 | 0.8036 | 0.9883 |
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+ | 0.0345 | 0.4 | 16000 | 0.0333 | 0.7790 | 0.8334 | 0.8053 | 0.9887 |
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+ | 0.0363 | 0.41 | 16500 | 0.0329 | 0.7783 | 0.8301 | 0.8034 | 0.9887 |
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+ | 0.0348 | 0.42 | 17000 | 0.0341 | 0.7626 | 0.8533 | 0.8054 | 0.9884 |
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+ | 0.0391 | 0.43 | 17500 | 0.0324 | 0.7873 | 0.8295 | 0.8079 | 0.9889 |
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+ | 0.0344 | 0.45 | 18000 | 0.0334 | 0.7769 | 0.8369 | 0.8058 | 0.9887 |
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+ | 0.0378 | 0.46 | 18500 | 0.0337 | 0.7741 | 0.8394 | 0.8054 | 0.9886 |
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+ | 0.035 | 0.47 | 19000 | 0.0328 | 0.7827 | 0.8323 | 0.8067 | 0.9888 |
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+ | 0.0351 | 0.48 | 19500 | 0.0327 | 0.7815 | 0.8371 | 0.8083 | 0.9889 |
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+ | 0.037 | 0.5 | 20000 | 0.0328 | 0.7793 | 0.8388 | 0.8079 | 0.9888 |
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+ | 0.0346 | 0.51 | 20500 | 0.0325 | 0.7804 | 0.8416 | 0.8099 | 0.9890 |
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+ | 0.0364 | 0.52 | 21000 | 0.0323 | 0.7861 | 0.8339 | 0.8093 | 0.9889 |
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+ | 0.0356 | 0.53 | 21500 | 0.0327 | 0.7729 | 0.8510 | 0.8101 | 0.9889 |
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+ | 0.0346 | 0.54 | 22000 | 0.0325 | 0.7791 | 0.8407 | 0.8087 | 0.9889 |
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+ | 0.0342 | 0.56 | 22500 | 0.0334 | 0.7790 | 0.8443 | 0.8104 | 0.9889 |
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+ | 0.0368 | 0.57 | 23000 | 0.0322 | 0.7869 | 0.8323 | 0.8089 | 0.9890 |
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+ | 0.0371 | 0.58 | 23500 | 0.0320 | 0.7890 | 0.8356 | 0.8116 | 0.9891 |
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+ | 0.0344 | 0.59 | 24000 | 0.0321 | 0.7910 | 0.8321 | 0.8110 | 0.9892 |
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+ | 0.0342 | 0.61 | 24500 | 0.0319 | 0.7881 | 0.8356 | 0.8111 | 0.9892 |
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+ | 0.0339 | 0.62 | 25000 | 0.0320 | 0.7889 | 0.8317 | 0.8097 | 0.9892 |
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+ | 0.0347 | 0.63 | 25500 | 0.0316 | 0.7909 | 0.8347 | 0.8122 | 0.9892 |
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+ | 0.034 | 0.64 | 26000 | 0.0318 | 0.7887 | 0.8324 | 0.8100 | 0.9891 |
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+ | 0.0347 | 0.66 | 26500 | 0.0317 | 0.7791 | 0.8525 | 0.8141 | 0.9891 |
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+ | 0.0345 | 0.67 | 27000 | 0.0318 | 0.7870 | 0.8384 | 0.8119 | 0.9892 |
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+ | 0.0347 | 0.68 | 27500 | 0.0317 | 0.7903 | 0.8426 | 0.8157 | 0.9893 |
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+ | 0.0371 | 0.69 | 28000 | 0.0311 | 0.7965 | 0.8332 | 0.8144 | 0.9894 |
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+ | 0.0338 | 0.71 | 28500 | 0.0316 | 0.7863 | 0.8442 | 0.8142 | 0.9892 |
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+ | 0.0352 | 0.72 | 29000 | 0.0315 | 0.7810 | 0.8537 | 0.8157 | 0.9892 |
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+ | 0.0344 | 0.73 | 29500 | 0.0314 | 0.7953 | 0.8353 | 0.8148 | 0.9894 |
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+ | 0.0322 | 0.74 | 30000 | 0.0320 | 0.7836 | 0.8449 | 0.8131 | 0.9891 |
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+ | 0.0355 | 0.76 | 30500 | 0.0312 | 0.7877 | 0.8480 | 0.8167 | 0.9894 |
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+ | 0.035 | 0.77 | 31000 | 0.0313 | 0.7864 | 0.8504 | 0.8171 | 0.9893 |
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+ | 0.0346 | 0.78 | 31500 | 0.0310 | 0.7931 | 0.8424 | 0.8170 | 0.9895 |
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+ | 0.0339 | 0.79 | 32000 | 0.0316 | 0.7857 | 0.8501 | 0.8166 | 0.9893 |
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+ | 0.033 | 0.8 | 32500 | 0.0311 | 0.7975 | 0.8406 | 0.8185 | 0.9895 |
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+ | 0.0337 | 0.82 | 33000 | 0.0314 | 0.7886 | 0.8457 | 0.8162 | 0.9894 |
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+ | 0.0357 | 0.83 | 33500 | 0.0311 | 0.7923 | 0.8437 | 0.8172 | 0.9894 |
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+ | 0.0348 | 0.84 | 34000 | 0.0312 | 0.7909 | 0.8490 | 0.8189 | 0.9894 |
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+ | 0.0343 | 0.85 | 34500 | 0.0311 | 0.7856 | 0.8528 | 0.8179 | 0.9893 |
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+ | 0.0323 | 0.87 | 35000 | 0.0311 | 0.7884 | 0.8505 | 0.8183 | 0.9894 |
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+ | 0.0329 | 0.88 | 35500 | 0.0307 | 0.7981 | 0.8399 | 0.8185 | 0.9896 |
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+ | 0.0324 | 0.89 | 36000 | 0.0313 | 0.7830 | 0.8576 | 0.8186 | 0.9893 |
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+ | 0.0336 | 0.9 | 36500 | 0.0312 | 0.7836 | 0.8566 | 0.8185 | 0.9893 |
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+ | 0.0327 | 0.92 | 37000 | 0.0309 | 0.7887 | 0.8501 | 0.8182 | 0.9895 |
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+ | 0.0338 | 0.93 | 37500 | 0.0312 | 0.7887 | 0.8514 | 0.8188 | 0.9894 |
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+ | 0.0327 | 0.94 | 38000 | 0.0311 | 0.7873 | 0.8534 | 0.8190 | 0.9894 |
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+ | 0.0326 | 0.95 | 38500 | 0.0308 | 0.7953 | 0.8459 | 0.8198 | 0.9895 |
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+ | 0.0338 | 0.97 | 39000 | 0.0307 | 0.7932 | 0.8488 | 0.8201 | 0.9895 |
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+ | 0.0354 | 0.98 | 39500 | 0.0308 | 0.7916 | 0.8502 | 0.8198 | 0.9895 |
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+ | 0.0313 | 0.99 | 40000 | 0.0309 | 0.7897 | 0.8523 | 0.8198 | 0.9895 |
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+
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+
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  ### Framework versions
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  - Transformers 4.22.0
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  - Pytorch 1.10.0
 
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  - Tokenizers 0.12.1