hoang.dang1
commited on
Commit
•
7cea16a
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Parent(s):
a0a1200
model push
Browse files- README.md +121 -0
- added_tokens.json +3 -0
- all_results.json +129 -0
- config.json +94 -0
- eval_results.json +64 -0
- model.safetensors +3 -0
- predict_results.json +62 -0
- predictions.txt +0 -0
- runs/Dec06_06-57-47_ntq/events.out.tfevents.1701845877.ntq.2445174.0 +3 -0
- runs/Dec06_06-57-47_ntq/events.out.tfevents.1701848269.ntq.2445174.1 +3 -0
- special_tokens_map.json +15 -0
- spm.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +58 -0
- train_results.json +8 -0
- trainer_state.json +735 -0
- training_args.bin +3 -0
README.md
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---
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license: mit
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---
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---
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license: mit
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base_model: microsoft/deberta-v3-large
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tags:
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- generated_from_trainer
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model-index:
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- name: bbc-ner-deberta-large_baseline2_dims
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# bbc-ner-deberta-large_baseline2_dims
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1481
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- Cargo Dimension Precision: 0.7606
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- Cargo Dimension Recall: 0.9474
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- Cargo Dimension F1: 0.8437
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- Cargo Dimension Number: 114
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- Cargo Quantity Precision: 0.8079
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- Cargo Quantity Recall: 0.8937
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- Cargo Quantity F1: 0.8486
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- Cargo Quantity Number: 207
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- Cargo Requirements Precision: 0.4962
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- Cargo Requirements Recall: 0.6535
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- Cargo Requirements F1: 0.5641
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- Cargo Requirements Number: 202
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- Cargo Stowage Factor Precision: 0.8226
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- Cargo Stowage Factor Recall: 0.8361
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- Cargo Stowage Factor F1: 0.8293
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- Cargo Stowage Factor Number: 122
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- Cargo Type Precision: 0.7885
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- Cargo Type Recall: 0.8183
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- Cargo Type F1: 0.8031
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- Cargo Type Number: 688
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- Cargo Weigh Volume Precision: 0.8528
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- Cargo Weigh Volume Recall: 0.9026
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- Cargo Weigh Volume F1: 0.8770
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- Cargo Weigh Volume Number: 719
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- Commission Rate Precision: 0.7955
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- Commission Rate Recall: 0.8452
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- Commission Rate F1: 0.8196
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- Commission Rate Number: 336
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- Discharging Port Precision: 0.8706
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- Discharging Port Recall: 0.9015
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- Discharging Port F1: 0.8858
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- Discharging Port Number: 843
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- Laycan Date Precision: 0.8260
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- Laycan Date Recall: 0.8710
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- Laycan Date F1: 0.8479
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- Laycan Date Number: 496
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- Loading Discharging Terms Precision: 0.7211
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- Loading Discharging Terms Recall: 0.7975
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- Loading Discharging Terms F1: 0.7574
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- Loading Discharging Terms Number: 321
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- Loading Port Precision: 0.8906
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- Loading Port Recall: 0.9232
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- Loading Port F1: 0.9066
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- Loading Port Number: 899
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- Shipment Terms Precision: 0.6780
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- Shipment Terms Recall: 0.6780
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- Shipment Terms F1: 0.6780
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- Shipment Terms Number: 118
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- Vessel Requirements Precision: 0.3786
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- Vessel Requirements Recall: 0.5132
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- Vessel Requirements F1: 0.4358
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- Vessel Requirements Number: 76
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- Overall Precision: 0.8041
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- Overall Recall: 0.8598
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- Overall F1: 0.8310
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- Overall Accuracy: 0.9688
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10.0
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### Training results
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| 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 |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:----------------------------:|:-------------------------:|:---------------------:|:-------------------------:|:-------------------------:|:----------------------:|:------------------:|:----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-----------------------------------:|:--------------------------------:|:----------------------------:|:--------------------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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### Framework versions
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- Transformers 4.36.0.dev0
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- Pytorch 2.1.0+cu121
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- Datasets 2.14.5
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- Tokenizers 0.15.0
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added_tokens.json
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{
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"[MASK]": 128000
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}
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all_results.json
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{
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"epoch": 9.96,
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"eval_Cargo_Dimension_f1": 0.8437499999999999,
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"eval_Cargo_Dimension_number": 114,
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"eval_Cargo_Dimension_precision": 0.7605633802816901,
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"eval_Cargo_Dimension_recall": 0.9473684210526315,
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"eval_Cargo_Quantity_f1": 0.8486238532110092,
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"eval_Cargo_Quantity_number": 207,
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"eval_Cargo_Quantity_precision": 0.8078602620087336,
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"eval_Cargo_Quantity_recall": 0.893719806763285,
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"eval_Cargo_Requirements_f1": 0.5641025641025641,
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"eval_Cargo_Requirements_number": 202,
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"eval_Cargo_Requirements_precision": 0.49624060150375937,
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"eval_Cargo_Requirements_recall": 0.6534653465346535,
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"eval_Cargo_Stowage_Factor_f1": 0.829268292682927,
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"eval_Cargo_Stowage_Factor_number": 122,
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"eval_Cargo_Stowage_Factor_precision": 0.8225806451612904,
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"eval_Cargo_Stowage_Factor_recall": 0.8360655737704918,
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"eval_Cargo_Type_f1": 0.8031383737517832,
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"eval_Cargo_Type_number": 688,
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|
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|
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|
config.json
ADDED
@@ -0,0 +1,94 @@
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|
1 |
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{
|
2 |
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|
3 |
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"architectures": [
|
4 |
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"DebertaV2ForTokenClassification"
|
5 |
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|
6 |
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|
7 |
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|
8 |
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|
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|
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|
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|
12 |
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|
13 |
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"1": "B-Cargo_Quantity",
|
14 |
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"2": "B-Cargo_Requirements",
|
15 |
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"3": "B-Cargo_Stowage_Factor",
|
16 |
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"4": "B-Cargo_Type",
|
17 |
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"5": "B-Cargo_Weigh_Volume",
|
18 |
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"6": "B-Commission_rate",
|
19 |
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|
20 |
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"8": "B-Laycan_date",
|
21 |
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"9": "B-Loading_Discharging_terms",
|
22 |
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"10": "B-Loading_port",
|
23 |
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"11": "B-Shipment_terms",
|
24 |
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|
25 |
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|
26 |
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|
27 |
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"15": "I-Cargo_Requirements",
|
28 |
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"16": "I-Cargo_Stowage_Factor",
|
29 |
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"17": "I-Cargo_Type",
|
30 |
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"18": "I-Cargo_Weigh_Volume",
|
31 |
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"19": "I-Commission_rate",
|
32 |
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"20": "I-Discharging_port",
|
33 |
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"21": "I-Laycan_date",
|
34 |
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"22": "I-Loading_Discharging_terms",
|
35 |
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"23": "I-Loading_port",
|
36 |
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"24": "I-Shipment_terms",
|
37 |
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"25": "I-Vessel_Requirements",
|
38 |
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|
39 |
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},
|
40 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
67 |
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|
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|
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|
70 |
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|
71 |
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|
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|
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|
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|
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|
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|
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|
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|
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"pos_att_type": [
|
83 |
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"p2c",
|
84 |
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"c2p"
|
85 |
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],
|
86 |
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"position_biased_input": false,
|
87 |
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"position_buckets": 256,
|
88 |
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|
89 |
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|
90 |
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|
91 |
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|
92 |
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|
94 |
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}
|
eval_results.json
ADDED
@@ -0,0 +1,64 @@
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|
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{
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predict_results.json
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predictions.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
runs/Dec06_06-57-47_ntq/events.out.tfevents.1701845877.ntq.2445174.0
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special_tokens_map.json
ADDED
@@ -0,0 +1,15 @@
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spm.model
ADDED
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tokenizer.json
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The diff for this file is too large to render.
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tokenizer_config.json
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ADDED
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