Initial Commit
Browse files- README.md +160 -0
- config.json +53 -0
- eval_result_ner.json +1 -0
- model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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---
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library_name: transformers
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license: mit
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base_model: microsoft/mdeberta-v3-base
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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: scenario-non-kd-scr-ner-half-mdeberta_data-univner_full66
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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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# scenario-non-kd-scr-ner-half-mdeberta_data-univner_full66
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3499
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- Precision: 0.6154
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- Recall: 0.5897
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- F1: 0.6023
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- Accuracy: 0.9611
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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: 3e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 66
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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: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 59 |
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| 0.3609 | 0.2910 | 500 | 0.2851 | 0.3209 | 0.1130 | 0.1671 | 0.9290 |
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| 0.2321 | 0.5821 | 1000 | 0.2099 | 0.3566 | 0.2637 | 0.3032 | 0.9388 |
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| 0.1747 | 0.8731 | 1500 | 0.1777 | 0.4101 | 0.3851 | 0.3972 | 0.9463 |
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| 0.1363 | 1.1641 | 2000 | 0.1660 | 0.4506 | 0.4555 | 0.4530 | 0.9506 |
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| 0.1117 | 1.4552 | 2500 | 0.1619 | 0.5167 | 0.4565 | 0.4847 | 0.9539 |
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| 0.1012 | 1.7462 | 3000 | 0.1538 | 0.5115 | 0.5142 | 0.5128 | 0.9554 |
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| 0.0907 | 2.0373 | 3500 | 0.1585 | 0.5395 | 0.5129 | 0.5258 | 0.9571 |
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| 0.0664 | 2.3283 | 4000 | 0.1525 | 0.5463 | 0.5485 | 0.5474 | 0.9578 |
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| 0.0682 | 2.6193 | 4500 | 0.1520 | 0.5493 | 0.5535 | 0.5514 | 0.9587 |
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| 0.0664 | 2.9104 | 5000 | 0.1517 | 0.5673 | 0.5574 | 0.5623 | 0.9593 |
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| 0.0475 | 3.2014 | 5500 | 0.1593 | 0.5643 | 0.5921 | 0.5779 | 0.9595 |
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| 0.0454 | 3.4924 | 6000 | 0.1682 | 0.6008 | 0.5367 | 0.5669 | 0.9597 |
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| 0.0443 | 3.7835 | 6500 | 0.1660 | 0.5801 | 0.5598 | 0.5698 | 0.9597 |
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| 0.043 | 4.0745 | 7000 | 0.1704 | 0.5708 | 0.5790 | 0.5748 | 0.9595 |
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| 0.031 | 4.3655 | 7500 | 0.1762 | 0.5875 | 0.5742 | 0.5808 | 0.9607 |
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| 0.0324 | 4.6566 | 8000 | 0.1809 | 0.5954 | 0.5755 | 0.5853 | 0.9607 |
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| 0.0321 | 4.9476 | 8500 | 0.1770 | 0.6079 | 0.5777 | 0.5924 | 0.9609 |
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| 0.0229 | 5.2386 | 9000 | 0.1911 | 0.5959 | 0.5874 | 0.5916 | 0.9613 |
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| 0.0223 | 5.5297 | 9500 | 0.1995 | 0.5844 | 0.5673 | 0.5757 | 0.9598 |
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| 0.023 | 5.8207 | 10000 | 0.1923 | 0.6045 | 0.5862 | 0.5952 | 0.9611 |
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| 0.0212 | 6.1118 | 10500 | 0.2031 | 0.5884 | 0.5803 | 0.5843 | 0.9603 |
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| 0.0162 | 6.4028 | 11000 | 0.2115 | 0.5941 | 0.5732 | 0.5835 | 0.9604 |
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| 0.0161 | 6.6938 | 11500 | 0.2081 | 0.5892 | 0.6002 | 0.5947 | 0.9610 |
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| 0.0177 | 6.9849 | 12000 | 0.2148 | 0.5894 | 0.5895 | 0.5895 | 0.9607 |
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| 0.0116 | 7.2759 | 12500 | 0.2198 | 0.5906 | 0.5986 | 0.5946 | 0.9604 |
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| 0.0124 | 7.5669 | 13000 | 0.2216 | 0.5892 | 0.5882 | 0.5887 | 0.9601 |
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| 0.0121 | 7.8580 | 13500 | 0.2298 | 0.5806 | 0.5944 | 0.5874 | 0.9600 |
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| 0.0108 | 8.1490 | 14000 | 0.2334 | 0.6057 | 0.5864 | 0.5959 | 0.9605 |
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| 87 |
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| 0.0091 | 8.4400 | 14500 | 0.2351 | 0.5856 | 0.5950 | 0.5903 | 0.9603 |
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| 88 |
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| 0.0099 | 8.7311 | 15000 | 0.2362 | 0.6142 | 0.5801 | 0.5967 | 0.9611 |
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| 89 |
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| 0.01 | 9.0221 | 15500 | 0.2449 | 0.6103 | 0.5791 | 0.5943 | 0.9612 |
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| 90 |
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| 0.0075 | 9.3132 | 16000 | 0.2437 | 0.5953 | 0.5878 | 0.5915 | 0.9608 |
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| 91 |
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| 0.0068 | 9.6042 | 16500 | 0.2511 | 0.6068 | 0.5868 | 0.5966 | 0.9610 |
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| 92 |
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| 0.0073 | 9.8952 | 17000 | 0.2512 | 0.6068 | 0.5832 | 0.5948 | 0.9608 |
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| 93 |
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| 0.0063 | 10.1863 | 17500 | 0.2585 | 0.6027 | 0.5747 | 0.5883 | 0.9606 |
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| 94 |
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| 0.0061 | 10.4773 | 18000 | 0.2576 | 0.5965 | 0.5885 | 0.5925 | 0.9606 |
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| 95 |
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| 0.0055 | 10.7683 | 18500 | 0.2605 | 0.5960 | 0.5918 | 0.5939 | 0.9608 |
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| 96 |
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| 0.0054 | 11.0594 | 19000 | 0.2740 | 0.6263 | 0.5721 | 0.5979 | 0.9613 |
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| 97 |
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| 0.0045 | 11.3504 | 19500 | 0.2675 | 0.6033 | 0.5939 | 0.5986 | 0.9608 |
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| 98 |
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| 0.0044 | 11.6414 | 20000 | 0.2714 | 0.5989 | 0.5871 | 0.5929 | 0.9609 |
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| 99 |
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| 0.0045 | 11.9325 | 20500 | 0.2729 | 0.6060 | 0.5866 | 0.5961 | 0.9609 |
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| 0.0036 | 12.2235 | 21000 | 0.2828 | 0.6109 | 0.5748 | 0.5923 | 0.9609 |
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| 0.0039 | 12.5146 | 21500 | 0.2813 | 0.5940 | 0.5901 | 0.5920 | 0.9608 |
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| 0.0036 | 12.8056 | 22000 | 0.2906 | 0.6061 | 0.5734 | 0.5893 | 0.9607 |
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| 0.0038 | 13.0966 | 22500 | 0.2862 | 0.6153 | 0.5853 | 0.5999 | 0.9610 |
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| 0.0026 | 13.3877 | 23000 | 0.2903 | 0.5862 | 0.5933 | 0.5897 | 0.9604 |
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| 105 |
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| 0.0032 | 13.6787 | 23500 | 0.2971 | 0.6209 | 0.5783 | 0.5988 | 0.9614 |
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| 106 |
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| 0.0031 | 13.9697 | 24000 | 0.2959 | 0.6150 | 0.5770 | 0.5954 | 0.9611 |
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| 107 |
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| 0.0024 | 14.2608 | 24500 | 0.3010 | 0.6219 | 0.5747 | 0.5973 | 0.9612 |
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| 0.0023 | 14.5518 | 25000 | 0.2963 | 0.5897 | 0.5953 | 0.5925 | 0.9600 |
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| 0.0031 | 14.8428 | 25500 | 0.2970 | 0.6097 | 0.5905 | 0.6000 | 0.9608 |
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| 0.0022 | 15.1339 | 26000 | 0.3033 | 0.6091 | 0.5859 | 0.5973 | 0.9607 |
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| 0.0024 | 15.4249 | 26500 | 0.3074 | 0.6041 | 0.5793 | 0.5914 | 0.9604 |
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| 112 |
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| 0.0024 | 15.7159 | 27000 | 0.3101 | 0.6194 | 0.5627 | 0.5897 | 0.9606 |
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| 113 |
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| 0.0022 | 16.0070 | 27500 | 0.3111 | 0.6188 | 0.5732 | 0.5952 | 0.9612 |
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| 0.0016 | 16.2980 | 28000 | 0.3141 | 0.6232 | 0.5768 | 0.5991 | 0.9613 |
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| 0.0019 | 16.5891 | 28500 | 0.3146 | 0.6036 | 0.5794 | 0.5913 | 0.9605 |
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| 0.0024 | 16.8801 | 29000 | 0.3034 | 0.5896 | 0.5965 | 0.5930 | 0.9605 |
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| 117 |
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| 0.0018 | 17.1711 | 29500 | 0.3130 | 0.6203 | 0.5823 | 0.6007 | 0.9613 |
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| 0.0018 | 17.4622 | 30000 | 0.3108 | 0.6077 | 0.5853 | 0.5963 | 0.9612 |
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| 119 |
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| 0.0013 | 17.7532 | 30500 | 0.3150 | 0.5994 | 0.5891 | 0.5942 | 0.9605 |
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| 0.0014 | 18.0442 | 31000 | 0.3220 | 0.6135 | 0.5820 | 0.5974 | 0.9614 |
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| 121 |
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| 0.0012 | 18.3353 | 31500 | 0.3171 | 0.6095 | 0.5864 | 0.5977 | 0.9610 |
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| 122 |
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| 0.0013 | 18.6263 | 32000 | 0.3155 | 0.6078 | 0.5855 | 0.5964 | 0.9608 |
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| 0.0014 | 18.9173 | 32500 | 0.3296 | 0.6237 | 0.5700 | 0.5957 | 0.9608 |
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| 0.001 | 19.2084 | 33000 | 0.3331 | 0.6292 | 0.5649 | 0.5953 | 0.9610 |
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| 0.0014 | 19.4994 | 33500 | 0.3258 | 0.6224 | 0.5832 | 0.6022 | 0.9615 |
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| 126 |
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| 0.0013 | 19.7905 | 34000 | 0.3342 | 0.5947 | 0.5729 | 0.5836 | 0.9596 |
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| 127 |
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| 0.0011 | 20.0815 | 34500 | 0.3256 | 0.6125 | 0.5888 | 0.6004 | 0.9609 |
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| 128 |
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| 0.0011 | 20.3725 | 35000 | 0.3253 | 0.6046 | 0.5898 | 0.5971 | 0.9607 |
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| 129 |
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| 0.0011 | 20.6636 | 35500 | 0.3359 | 0.6229 | 0.5689 | 0.5947 | 0.9612 |
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| 130 |
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| 0.0009 | 20.9546 | 36000 | 0.3225 | 0.6012 | 0.5952 | 0.5982 | 0.9608 |
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| 131 |
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| 0.0007 | 21.2456 | 36500 | 0.3266 | 0.6081 | 0.5917 | 0.5998 | 0.9614 |
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| 132 |
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| 0.0008 | 21.5367 | 37000 | 0.3333 | 0.6298 | 0.5822 | 0.6050 | 0.9615 |
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| 133 |
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| 0.0011 | 21.8277 | 37500 | 0.3299 | 0.6083 | 0.5869 | 0.5974 | 0.9611 |
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| 134 |
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| 0.0007 | 22.1187 | 38000 | 0.3318 | 0.6055 | 0.5901 | 0.5977 | 0.9610 |
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| 135 |
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| 0.0006 | 22.4098 | 38500 | 0.3395 | 0.6092 | 0.5859 | 0.5973 | 0.9609 |
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| 136 |
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| 0.0008 | 22.7008 | 39000 | 0.3404 | 0.6262 | 0.5752 | 0.5996 | 0.9612 |
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| 137 |
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| 0.0008 | 22.9919 | 39500 | 0.3368 | 0.6081 | 0.5869 | 0.5973 | 0.9609 |
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| 138 |
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| 0.0006 | 23.2829 | 40000 | 0.3406 | 0.6143 | 0.5842 | 0.5989 | 0.9609 |
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| 139 |
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| 0.0006 | 23.5739 | 40500 | 0.3407 | 0.6060 | 0.5934 | 0.5997 | 0.9609 |
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| 140 |
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| 0.0006 | 23.8650 | 41000 | 0.3420 | 0.6044 | 0.5871 | 0.5956 | 0.9608 |
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| 141 |
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| 0.0007 | 24.1560 | 41500 | 0.3405 | 0.6226 | 0.5838 | 0.6025 | 0.9614 |
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| 142 |
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| 0.0005 | 24.4470 | 42000 | 0.3399 | 0.6177 | 0.5855 | 0.6011 | 0.9615 |
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| 143 |
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| 0.0004 | 24.7381 | 42500 | 0.3459 | 0.6208 | 0.5825 | 0.6010 | 0.9613 |
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| 144 |
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| 0.0004 | 25.0291 | 43000 | 0.3446 | 0.6153 | 0.5884 | 0.6015 | 0.9610 |
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| 145 |
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| 0.0003 | 25.3201 | 43500 | 0.3487 | 0.6108 | 0.5840 | 0.5971 | 0.9609 |
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| 146 |
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| 0.0003 | 25.6112 | 44000 | 0.3573 | 0.6233 | 0.5741 | 0.5977 | 0.9611 |
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| 147 |
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| 0.0006 | 25.9022 | 44500 | 0.3550 | 0.6173 | 0.5780 | 0.5970 | 0.9609 |
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| 148 |
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| 0.0003 | 26.1932 | 45000 | 0.3516 | 0.6136 | 0.5804 | 0.5966 | 0.9610 |
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| 149 |
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| 0.0004 | 26.4843 | 45500 | 0.3515 | 0.6242 | 0.5817 | 0.6022 | 0.9612 |
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| 150 |
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| 0.0005 | 26.7753 | 46000 | 0.3545 | 0.6228 | 0.5783 | 0.5997 | 0.9612 |
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| 151 |
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| 0.0005 | 27.0664 | 46500 | 0.3504 | 0.6166 | 0.5858 | 0.6008 | 0.9611 |
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| 0.0004 | 27.3574 | 47000 | 0.3499 | 0.6154 | 0.5897 | 0.6023 | 0.9611 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.1.1+cu121
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- Datasets 2.14.5
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "microsoft/mdeberta-v3-base",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"DebertaV2ForTokenClassification"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"hidden_act": "gelu",
|
| 8 |
+
"hidden_dropout_prob": 0.1,
|
| 9 |
+
"hidden_size": 384,
|
| 10 |
+
"id2label": {
|
| 11 |
+
"0": "LABEL_0",
|
| 12 |
+
"1": "LABEL_1",
|
| 13 |
+
"2": "LABEL_2",
|
| 14 |
+
"3": "LABEL_3",
|
| 15 |
+
"4": "LABEL_4",
|
| 16 |
+
"5": "LABEL_5",
|
| 17 |
+
"6": "LABEL_6"
|
| 18 |
+
},
|
| 19 |
+
"initializer_range": 0.02,
|
| 20 |
+
"intermediate_size": 1536,
|
| 21 |
+
"label2id": {
|
| 22 |
+
"LABEL_0": 0,
|
| 23 |
+
"LABEL_1": 1,
|
| 24 |
+
"LABEL_2": 2,
|
| 25 |
+
"LABEL_3": 3,
|
| 26 |
+
"LABEL_4": 4,
|
| 27 |
+
"LABEL_5": 5,
|
| 28 |
+
"LABEL_6": 6
|
| 29 |
+
},
|
| 30 |
+
"layer_norm_eps": 1e-07,
|
| 31 |
+
"max_position_embeddings": 512,
|
| 32 |
+
"max_relative_positions": -1,
|
| 33 |
+
"model_type": "deberta-v2",
|
| 34 |
+
"norm_rel_ebd": "layer_norm",
|
| 35 |
+
"num_attention_heads": 12,
|
| 36 |
+
"num_hidden_layers": 6,
|
| 37 |
+
"pad_token_id": 0,
|
| 38 |
+
"pooler_dropout": 0,
|
| 39 |
+
"pooler_hidden_act": "gelu",
|
| 40 |
+
"pooler_hidden_size": 768,
|
| 41 |
+
"pos_att_type": [
|
| 42 |
+
"p2c",
|
| 43 |
+
"c2p"
|
| 44 |
+
],
|
| 45 |
+
"position_biased_input": false,
|
| 46 |
+
"position_buckets": 256,
|
| 47 |
+
"relative_attention": true,
|
| 48 |
+
"share_att_key": true,
|
| 49 |
+
"torch_dtype": "float32",
|
| 50 |
+
"transformers_version": "4.44.2",
|
| 51 |
+
"type_vocab_size": 0,
|
| 52 |
+
"vocab_size": 251000
|
| 53 |
+
}
|
eval_result_ner.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"ceb_gja": {"precision": 0.21978021978021978, "recall": 0.40816326530612246, "f1": 0.2857142857142857, "accuracy": 0.915057915057915}, "en_pud": {"precision": 0.4936268829663963, "recall": 0.39627906976744187, "f1": 0.43962848297213625, "accuracy": 0.947912731394031}, "de_pud": {"precision": 0.13747454175152748, "recall": 0.2598652550529355, "f1": 0.1798201798201798, "accuracy": 0.866813557732877}, "pt_pud": {"precision": 0.5722831505483549, "recall": 0.5222929936305732, "f1": 0.5461465271170314, "accuracy": 0.9596701841329517}, "ru_pud": {"precision": 0.01573588398642394, "recall": 0.04922779922779923, "f1": 0.023848491933598316, "accuracy": 0.6990958408679928}, "sv_pud": {"precision": 0.5519591141396933, "recall": 0.31486880466472306, "f1": 0.40099009900990107, "accuracy": 0.9454288110715034}, "tl_trg": {"precision": 0.2037037037037037, "recall": 0.4782608695652174, "f1": 0.2857142857142857, "accuracy": 0.9305177111716622}, "tl_ugnayan": {"precision": 0.044444444444444446, "recall": 0.12121212121212122, "f1": 0.06504065040650406, "accuracy": 0.886964448495898}, "zh_gsd": {"precision": 0.5648267008985879, "recall": 0.5736636245110821, "f1": 0.5692108667529108, "accuracy": 0.9443056943056943}, "zh_gsdsimp": {"precision": 0.5918097754293263, "recall": 0.5871559633027523, "f1": 0.5894736842105264, "accuracy": 0.9463869463869464}, "hr_set": {"precision": 0.7509051412020276, "recall": 0.7391304347826086, "f1": 0.744971264367816, "accuracy": 0.9699917559769168}, "da_ddt": {"precision": 0.6805555555555556, "recall": 0.5480984340044742, "f1": 0.6071871127633208, "accuracy": 0.9710665469420333}, "en_ewt": {"precision": 0.6167023554603854, "recall": 0.5294117647058824, "f1": 0.5697329376854599, "accuracy": 0.9590787743555007}, "pt_bosque": {"precision": 0.6741472172351886, "recall": 0.6181069958847737, "f1": 0.6449119793902963, "accuracy": 0.9674322561947544}, "sr_set": {"precision": 0.8050239234449761, "recall": 0.79456906729634, "f1": 0.799762329174094, "accuracy": 0.9693546974870852}, "sk_snk": {"precision": 0.4032846715328467, "recall": 0.24153005464480876, "f1": 0.30211893369788106, "accuracy": 0.9125314070351759}, "sv_talbanken": {"precision": 0.7317073170731707, "recall": 0.6122448979591837, "f1": 0.6666666666666666, "accuracy": 0.9942091573833244}}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:27d3789789d1d64c621341386454c13f4cae5007c112381c1839633e650d3fc6
|
| 3 |
+
size 428939068
|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:30c766fa89866a420bd6a15f3cb5cd869feea2e60d4bf47b1eab2f46de4bc113
|
| 3 |
+
size 5304
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