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DeBERTa_large_NER_chartering_email

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metadata
license: mit
base_model: microsoft/deberta-v3-large
tags:
  - generated_from_trainer
model-index:
  - name: bbc-ner-deberta-large_baseline2_dims
    results: []

bbc-ner-deberta-large_baseline2_dims

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

  • Loss: 0.1481
  • Cargo Dimension Precision: 0.7606
  • Cargo Dimension Recall: 0.9474
  • Cargo Dimension F1: 0.8437
  • Cargo Dimension Number: 114
  • Cargo Quantity Precision: 0.8079
  • Cargo Quantity Recall: 0.8937
  • Cargo Quantity F1: 0.8486
  • Cargo Quantity Number: 207
  • Cargo Requirements Precision: 0.4962
  • Cargo Requirements Recall: 0.6535
  • Cargo Requirements F1: 0.5641
  • Cargo Requirements Number: 202
  • Cargo Stowage Factor Precision: 0.8226
  • Cargo Stowage Factor Recall: 0.8361
  • Cargo Stowage Factor F1: 0.8293
  • Cargo Stowage Factor Number: 122
  • Cargo Type Precision: 0.7885
  • Cargo Type Recall: 0.8183
  • Cargo Type F1: 0.8031
  • Cargo Type Number: 688
  • Cargo Weigh Volume Precision: 0.8528
  • Cargo Weigh Volume Recall: 0.9026
  • Cargo Weigh Volume F1: 0.8770
  • Cargo Weigh Volume Number: 719
  • Commission Rate Precision: 0.7955
  • Commission Rate Recall: 0.8452
  • Commission Rate F1: 0.8196
  • Commission Rate Number: 336
  • Discharging Port Precision: 0.8706
  • Discharging Port Recall: 0.9015
  • Discharging Port F1: 0.8858
  • Discharging Port Number: 843
  • Laycan Date Precision: 0.8260
  • Laycan Date Recall: 0.8710
  • Laycan Date F1: 0.8479
  • Laycan Date Number: 496
  • Loading Discharging Terms Precision: 0.7211
  • Loading Discharging Terms Recall: 0.7975
  • Loading Discharging Terms F1: 0.7574
  • Loading Discharging Terms Number: 321
  • Loading Port Precision: 0.8906
  • Loading Port Recall: 0.9232
  • Loading Port F1: 0.9066
  • Loading Port Number: 899
  • Shipment Terms Precision: 0.6780
  • Shipment Terms Recall: 0.6780
  • Shipment Terms F1: 0.6780
  • Shipment Terms Number: 118
  • Vessel Requirements Precision: 0.3786
  • Vessel Requirements Recall: 0.5132
  • Vessel Requirements F1: 0.4358
  • Vessel Requirements Number: 76
  • Overall Precision: 0.8041
  • Overall Recall: 0.8598
  • Overall F1: 0.8310
  • Overall Accuracy: 0.9688

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
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Cargo Dimension Precision Cargo Dimension Recall Cargo Dimension F1 Cargo Dimension Number Cargo Quantity Precision Cargo Quantity Recall Cargo Quantity F1 Cargo Quantity Number Cargo Requirements Precision Cargo Requirements Recall Cargo Requirements F1 Cargo Requirements Number Cargo Stowage Factor Precision Cargo Stowage Factor Recall Cargo Stowage Factor F1 Cargo Stowage Factor Number Cargo Type Precision Cargo Type Recall Cargo Type F1 Cargo Type Number Cargo Weigh Volume Precision Cargo Weigh Volume Recall Cargo Weigh Volume F1 Cargo Weigh Volume Number Commission Rate Precision Commission Rate Recall Commission Rate F1 Commission Rate Number Discharging Port Precision Discharging Port Recall Discharging Port F1 Discharging Port Number Laycan Date Precision Laycan Date Recall Laycan Date F1 Laycan Date Number Loading Discharging Terms Precision Loading Discharging Terms Recall Loading Discharging Terms F1 Loading Discharging Terms Number Loading Port Precision Loading Port Recall Loading Port F1 Loading Port Number Shipment Terms Precision Shipment Terms Recall Shipment Terms F1 Shipment Terms Number Vessel Requirements Precision Vessel Requirements Recall Vessel Requirements F1 Vessel Requirements Number Overall Precision Overall Recall Overall F1 Overall Accuracy
0.4476 1.0 119 0.1523 0.6169 0.8333 0.7090 114 0.7087 0.8696 0.7809 207 0.3007 0.4257 0.3525 202 0.6284 0.7623 0.6889 122 0.6478 0.6308 0.6392 688 0.6983 0.8178 0.7534 719 0.7099 0.7649 0.7364 336 0.7212 0.8683 0.7879 843 0.7482 0.8266 0.7854 496 0.5669 0.6729 0.6154 321 0.8280 0.8565 0.8420 899 0.6556 0.5 0.5673 118 0.0658 0.0658 0.0658 76 0.6819 0.7635 0.7204 0.9581
0.132 2.0 239 0.1438 0.6471 0.8684 0.7416 114 0.7212 0.9372 0.8151 207 0.2857 0.4455 0.3482 202 0.7083 0.8361 0.7669 122 0.6586 0.8270 0.7332 688 0.7191 0.8901 0.7955 719 0.7863 0.8542 0.8188 336 0.7639 0.8944 0.8240 843 0.7370 0.8589 0.7933 496 0.5497 0.7757 0.6434 321 0.8533 0.8932 0.8728 899 0.5669 0.6102 0.5878 118 0.216 0.3553 0.2687 76 0.6943 0.8387 0.7597 0.9579
0.1022 3.0 358 0.1217 0.7574 0.9035 0.8240 114 0.8087 0.8986 0.8513 207 0.4081 0.5495 0.4684 202 0.75 0.8361 0.7907 122 0.7301 0.8140 0.7698 688 0.8132 0.8720 0.8416 719 0.7983 0.8482 0.8225 336 0.8123 0.9087 0.8578 843 0.7901 0.8730 0.8295 496 0.7583 0.7819 0.7699 321 0.8783 0.9232 0.9002 899 0.7596 0.6695 0.7117 118 0.3529 0.4737 0.4045 76 0.7744 0.8498 0.8103 0.9677
0.0802 4.0 478 0.1291 0.7589 0.9386 0.8392 114 0.8097 0.8841 0.8453 207 0.4566 0.5990 0.5182 202 0.8347 0.8279 0.8313 122 0.7710 0.7733 0.7721 688 0.8015 0.8818 0.8397 719 0.8203 0.8423 0.8311 336 0.8449 0.9110 0.8767 843 0.8252 0.8851 0.8541 496 0.7202 0.7539 0.7367 321 0.8637 0.9232 0.8925 899 0.7404 0.6525 0.6937 118 0.4359 0.4474 0.4416 76 0.7912 0.8463 0.8179 0.9683
0.0733 5.0 597 0.1269 0.7347 0.9474 0.8276 114 0.7975 0.9130 0.8514 207 0.4306 0.6139 0.5061 202 0.8062 0.8525 0.8287 122 0.7564 0.8169 0.7855 688 0.8142 0.8901 0.8505 719 0.8184 0.8452 0.8316 336 0.8760 0.9051 0.8903 843 0.8180 0.8790 0.8474 496 0.7303 0.8100 0.7681 321 0.8747 0.9321 0.9025 899 0.6231 0.6864 0.6532 118 0.3407 0.4079 0.3713 76 0.7870 0.8598 0.8218 0.9681
0.0541 6.0 717 0.1299 0.7379 0.9386 0.8263 114 0.7899 0.9082 0.8449 207 0.4330 0.6238 0.5112 202 0.8095 0.8361 0.8226 122 0.7681 0.8183 0.7924 688 0.7916 0.8929 0.8392 719 0.8182 0.8571 0.8372 336 0.8491 0.9075 0.8773 843 0.8056 0.8690 0.8361 496 0.6981 0.7850 0.7390 321 0.8828 0.9388 0.9100 899 0.6991 0.6695 0.6840 118 0.38 0.5 0.4318 76 0.7815 0.8607 0.8192 0.9676
0.0463 7.0 836 0.1311 0.7626 0.9298 0.8379 114 0.8243 0.8841 0.8531 207 0.4731 0.6089 0.5325 202 0.7895 0.8607 0.8235 122 0.7875 0.7863 0.7869 688 0.8366 0.8901 0.8625 719 0.8067 0.8571 0.8312 336 0.8928 0.8992 0.8960 843 0.8314 0.8649 0.8478 496 0.7672 0.8006 0.7835 321 0.9013 0.9143 0.9078 899 0.6529 0.6695 0.6611 118 0.3608 0.4605 0.4046 76 0.8096 0.8493 0.8289 0.9686
0.0434 8.0 956 0.1430 0.7448 0.9474 0.8340 114 0.7957 0.9034 0.8462 207 0.4765 0.6535 0.5511 202 0.7863 0.8443 0.8142 122 0.7916 0.8227 0.8068 688 0.8274 0.8999 0.8621 719 0.8141 0.8601 0.8365 336 0.8727 0.9027 0.8875 843 0.8241 0.8690 0.8459 496 0.7298 0.8162 0.7706 321 0.8943 0.9132 0.9037 899 0.5797 0.6780 0.6250 118 0.3491 0.4868 0.4066 76 0.7963 0.8605 0.8271 0.9681
0.0373 9.0 1075 0.1435 0.75 0.9474 0.8372 114 0.8017 0.8986 0.8474 207 0.4815 0.6436 0.5508 202 0.8254 0.8525 0.8387 122 0.7762 0.8169 0.7960 688 0.8409 0.9040 0.8713 719 0.8011 0.8512 0.8254 336 0.8648 0.8956 0.8800 843 0.8267 0.875 0.8501 496 0.7227 0.8037 0.7611 321 0.8874 0.9288 0.9076 899 0.6349 0.6780 0.6557 118 0.3645 0.5132 0.4262 76 0.7969 0.8611 0.8278 0.9686
0.0348 9.96 1190 0.1481 0.7606 0.9474 0.8437 114 0.8079 0.8937 0.8486 207 0.4962 0.6535 0.5641 202 0.8226 0.8361 0.8293 122 0.7885 0.8183 0.8031 688 0.8528 0.9026 0.8770 719 0.7955 0.8452 0.8196 336 0.8706 0.9015 0.8858 843 0.8260 0.8710 0.8479 496 0.7211 0.7975 0.7574 321 0.8906 0.9232 0.9066 899 0.6780 0.6780 0.6780 118 0.3786 0.5132 0.4358 76 0.8041 0.8598 0.8310 0.9688

Framework versions

  • Transformers 4.36.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.15.0