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--- |
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base_model: microsoft/mdeberta-v3-base |
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library_name: transformers |
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license: mit |
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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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tags: |
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- generated_from_trainer |
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model-index: |
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- name: scenario-non-kd-scr-ner-full-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-full-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: 0.3771 |
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- Precision: 0.6326 |
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- Recall: 0.6068 |
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- F1: 0.6194 |
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- Accuracy: 0.9635 |
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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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| 0.3111 | 0.2910 | 500 | 0.2388 | 0.3312 | 0.2252 | 0.2681 | 0.9340 | |
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| 0.1965 | 0.5821 | 1000 | 0.2031 | 0.4073 | 0.2821 | 0.3333 | 0.9425 | |
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| 0.1493 | 0.8731 | 1500 | 0.1645 | 0.4478 | 0.4533 | 0.4505 | 0.9510 | |
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| 0.1123 | 1.1641 | 2000 | 0.1545 | 0.5054 | 0.5240 | 0.5146 | 0.9559 | |
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| 0.0879 | 1.4552 | 2500 | 0.1620 | 0.5659 | 0.4996 | 0.5307 | 0.9578 | |
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| 0.0809 | 1.7462 | 3000 | 0.1505 | 0.5293 | 0.5598 | 0.5441 | 0.9585 | |
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| 0.0722 | 2.0373 | 3500 | 0.1621 | 0.5762 | 0.5591 | 0.5675 | 0.9603 | |
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| 0.0457 | 2.3283 | 4000 | 0.1587 | 0.5676 | 0.5806 | 0.5740 | 0.9602 | |
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| 0.0478 | 2.6193 | 4500 | 0.1604 | 0.5524 | 0.5852 | 0.5683 | 0.9599 | |
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| 0.047 | 2.9104 | 5000 | 0.1550 | 0.5787 | 0.5865 | 0.5826 | 0.9612 | |
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| 0.0296 | 3.2014 | 5500 | 0.1791 | 0.5980 | 0.5989 | 0.5985 | 0.9622 | |
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| 0.0271 | 3.4924 | 6000 | 0.1783 | 0.6204 | 0.5865 | 0.6030 | 0.9623 | |
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| 0.0272 | 3.7835 | 6500 | 0.1794 | 0.5971 | 0.6074 | 0.6022 | 0.9620 | |
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| 0.0259 | 4.0745 | 7000 | 0.1968 | 0.6020 | 0.6136 | 0.6077 | 0.9627 | |
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| 0.0155 | 4.3655 | 7500 | 0.2028 | 0.5972 | 0.6053 | 0.6012 | 0.9626 | |
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| 0.0175 | 4.6566 | 8000 | 0.2095 | 0.6046 | 0.5754 | 0.5896 | 0.9615 | |
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| 0.0173 | 4.9476 | 8500 | 0.2121 | 0.5892 | 0.5931 | 0.5912 | 0.9619 | |
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| 0.0103 | 5.2386 | 9000 | 0.2274 | 0.6150 | 0.6024 | 0.6086 | 0.9627 | |
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| 0.0102 | 5.5297 | 9500 | 0.2231 | 0.6210 | 0.5905 | 0.6054 | 0.9623 | |
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| 0.0115 | 5.8207 | 10000 | 0.2175 | 0.6135 | 0.5966 | 0.6049 | 0.9624 | |
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| 0.0096 | 6.1118 | 10500 | 0.2394 | 0.5723 | 0.6358 | 0.6024 | 0.9613 | |
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| 0.0068 | 6.4028 | 11000 | 0.2474 | 0.6202 | 0.5957 | 0.6077 | 0.9629 | |
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| 0.007 | 6.6938 | 11500 | 0.2500 | 0.6095 | 0.6104 | 0.6100 | 0.9629 | |
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| 0.0085 | 6.9849 | 12000 | 0.2514 | 0.5995 | 0.5992 | 0.5994 | 0.9624 | |
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| 0.0054 | 7.2759 | 12500 | 0.2613 | 0.6161 | 0.5956 | 0.6057 | 0.9627 | |
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| 0.0052 | 7.5669 | 13000 | 0.2684 | 0.6083 | 0.6077 | 0.6080 | 0.9626 | |
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| 0.0056 | 7.8580 | 13500 | 0.2655 | 0.5795 | 0.6211 | 0.5996 | 0.9612 | |
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| 0.0048 | 8.1490 | 14000 | 0.2718 | 0.5925 | 0.6057 | 0.5990 | 0.9612 | |
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| 0.004 | 8.4400 | 14500 | 0.2794 | 0.6129 | 0.6094 | 0.6112 | 0.9624 | |
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| 0.0041 | 8.7311 | 15000 | 0.2811 | 0.6038 | 0.5937 | 0.5987 | 0.9618 | |
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| 0.0045 | 9.0221 | 15500 | 0.2814 | 0.6154 | 0.5878 | 0.6013 | 0.9622 | |
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| 0.0033 | 9.3132 | 16000 | 0.2879 | 0.5954 | 0.6203 | 0.6076 | 0.9621 | |
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| 0.0034 | 9.6042 | 16500 | 0.2963 | 0.6251 | 0.5956 | 0.6100 | 0.9631 | |
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| 0.0032 | 9.8952 | 17000 | 0.2935 | 0.5800 | 0.6321 | 0.6049 | 0.9615 | |
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| 0.0031 | 10.1863 | 17500 | 0.2909 | 0.6003 | 0.6194 | 0.6097 | 0.9625 | |
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| 0.0025 | 10.4773 | 18000 | 0.2991 | 0.5960 | 0.6096 | 0.6027 | 0.9619 | |
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| 0.0026 | 10.7683 | 18500 | 0.2983 | 0.6080 | 0.6086 | 0.6083 | 0.9623 | |
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| 0.0027 | 11.0594 | 19000 | 0.2975 | 0.6146 | 0.6054 | 0.6100 | 0.9624 | |
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| 0.0016 | 11.3504 | 19500 | 0.3092 | 0.6172 | 0.5900 | 0.6033 | 0.9626 | |
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| 0.0023 | 11.6414 | 20000 | 0.3168 | 0.6292 | 0.5918 | 0.6100 | 0.9630 | |
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| 0.0025 | 11.9325 | 20500 | 0.3036 | 0.6216 | 0.5972 | 0.6091 | 0.9627 | |
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| 0.0015 | 12.2235 | 21000 | 0.3222 | 0.6164 | 0.5918 | 0.6039 | 0.9621 | |
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| 0.0017 | 12.5146 | 21500 | 0.3158 | 0.6127 | 0.6089 | 0.6108 | 0.9626 | |
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| 0.0018 | 12.8056 | 22000 | 0.3223 | 0.6023 | 0.6008 | 0.6015 | 0.9623 | |
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| 0.0019 | 13.0966 | 22500 | 0.3197 | 0.6047 | 0.5910 | 0.5977 | 0.9618 | |
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| 0.0013 | 13.3877 | 23000 | 0.3190 | 0.6128 | 0.5985 | 0.6055 | 0.9620 | |
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| 0.0013 | 13.6787 | 23500 | 0.3279 | 0.6144 | 0.5904 | 0.6022 | 0.9622 | |
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| 0.0014 | 13.9697 | 24000 | 0.3278 | 0.6181 | 0.6089 | 0.6135 | 0.9624 | |
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| 0.0011 | 14.2608 | 24500 | 0.3384 | 0.6119 | 0.5927 | 0.6022 | 0.9623 | |
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| 0.0014 | 14.5518 | 25000 | 0.3270 | 0.6225 | 0.5993 | 0.6107 | 0.9621 | |
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| 0.0015 | 14.8428 | 25500 | 0.3320 | 0.5971 | 0.5969 | 0.5970 | 0.9616 | |
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| 0.001 | 15.1339 | 26000 | 0.3442 | 0.6174 | 0.5936 | 0.6053 | 0.9623 | |
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| 0.0008 | 15.4249 | 26500 | 0.3344 | 0.6091 | 0.6154 | 0.6122 | 0.9624 | |
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| 0.0009 | 15.7159 | 27000 | 0.3347 | 0.6242 | 0.5982 | 0.6109 | 0.9625 | |
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| 0.0011 | 16.0070 | 27500 | 0.3407 | 0.6225 | 0.6064 | 0.6143 | 0.9625 | |
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| 0.0008 | 16.2980 | 28000 | 0.3376 | 0.6217 | 0.6081 | 0.6148 | 0.9626 | |
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| 0.0008 | 16.5891 | 28500 | 0.3476 | 0.6030 | 0.6130 | 0.6080 | 0.9627 | |
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| 0.0009 | 16.8801 | 29000 | 0.3390 | 0.6224 | 0.5988 | 0.6103 | 0.9626 | |
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| 0.0009 | 17.1711 | 29500 | 0.3427 | 0.6094 | 0.6195 | 0.6144 | 0.9624 | |
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| 0.0006 | 17.4622 | 30000 | 0.3451 | 0.6200 | 0.6126 | 0.6163 | 0.9629 | |
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| 0.0004 | 17.7532 | 30500 | 0.3485 | 0.6190 | 0.6078 | 0.6134 | 0.9630 | |
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| 0.0008 | 18.0442 | 31000 | 0.3532 | 0.6237 | 0.5973 | 0.6102 | 0.9628 | |
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| 0.0007 | 18.3353 | 31500 | 0.3454 | 0.6143 | 0.6019 | 0.6080 | 0.9628 | |
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| 0.0006 | 18.6263 | 32000 | 0.3426 | 0.6253 | 0.6093 | 0.6172 | 0.9629 | |
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| 0.0006 | 18.9173 | 32500 | 0.3503 | 0.6205 | 0.6018 | 0.6110 | 0.9628 | |
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| 0.0004 | 19.2084 | 33000 | 0.3580 | 0.6344 | 0.6034 | 0.6185 | 0.9633 | |
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| 0.0004 | 19.4994 | 33500 | 0.3527 | 0.6072 | 0.6203 | 0.6137 | 0.9626 | |
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| 0.0006 | 19.7905 | 34000 | 0.3473 | 0.6173 | 0.6115 | 0.6144 | 0.9628 | |
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| 0.0005 | 20.0815 | 34500 | 0.3550 | 0.6208 | 0.6106 | 0.6157 | 0.9630 | |
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| 0.0003 | 20.3725 | 35000 | 0.3623 | 0.6153 | 0.6074 | 0.6113 | 0.9626 | |
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| 0.0004 | 20.6636 | 35500 | 0.3639 | 0.6264 | 0.5989 | 0.6123 | 0.9628 | |
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| 0.0005 | 20.9546 | 36000 | 0.3505 | 0.6167 | 0.6179 | 0.6173 | 0.9631 | |
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| 0.0004 | 21.2456 | 36500 | 0.3570 | 0.6237 | 0.6093 | 0.6164 | 0.9631 | |
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| 0.0003 | 21.5367 | 37000 | 0.3608 | 0.6302 | 0.6089 | 0.6194 | 0.9634 | |
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| 0.0005 | 21.8277 | 37500 | 0.3597 | 0.6158 | 0.6027 | 0.6092 | 0.9626 | |
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| 0.0002 | 22.1187 | 38000 | 0.3595 | 0.6252 | 0.6070 | 0.6160 | 0.9632 | |
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| 0.0003 | 22.4098 | 38500 | 0.3615 | 0.6186 | 0.6135 | 0.6160 | 0.9631 | |
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| 0.0002 | 22.7008 | 39000 | 0.3630 | 0.6311 | 0.5983 | 0.6143 | 0.9633 | |
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| 0.0003 | 22.9919 | 39500 | 0.3694 | 0.6344 | 0.5825 | 0.6073 | 0.9629 | |
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| 0.0001 | 23.2829 | 40000 | 0.3673 | 0.6284 | 0.6071 | 0.6176 | 0.9634 | |
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| 0.0002 | 23.5739 | 40500 | 0.3693 | 0.6187 | 0.6063 | 0.6124 | 0.9630 | |
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| 0.0003 | 23.8650 | 41000 | 0.3704 | 0.6153 | 0.6087 | 0.6120 | 0.9630 | |
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| 0.0001 | 24.1560 | 41500 | 0.3663 | 0.6219 | 0.6070 | 0.6143 | 0.9633 | |
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| 0.0001 | 24.4470 | 42000 | 0.3667 | 0.6228 | 0.6161 | 0.6194 | 0.9637 | |
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| 0.0002 | 24.7381 | 42500 | 0.3736 | 0.6456 | 0.5926 | 0.6179 | 0.9633 | |
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| 0.0002 | 25.0291 | 43000 | 0.3742 | 0.6280 | 0.5953 | 0.6112 | 0.9633 | |
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| 0.0001 | 25.3201 | 43500 | 0.3714 | 0.6217 | 0.6016 | 0.6115 | 0.9629 | |
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| 0.0002 | 25.6112 | 44000 | 0.3720 | 0.6348 | 0.5933 | 0.6133 | 0.9632 | |
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| 0.0001 | 25.9022 | 44500 | 0.3726 | 0.6136 | 0.6152 | 0.6144 | 0.9631 | |
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| 0.0001 | 26.1932 | 45000 | 0.3694 | 0.6366 | 0.5963 | 0.6158 | 0.9636 | |
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| 0.0001 | 26.4843 | 45500 | 0.3678 | 0.6113 | 0.6227 | 0.6170 | 0.9632 | |
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| 0.0001 | 26.7753 | 46000 | 0.3702 | 0.6348 | 0.6016 | 0.6178 | 0.9636 | |
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| 0.0001 | 27.0664 | 46500 | 0.3747 | 0.6323 | 0.5998 | 0.6156 | 0.9634 | |
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| 0.0001 | 27.3574 | 47000 | 0.3738 | 0.6352 | 0.6006 | 0.6174 | 0.9635 | |
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| 0.0001 | 27.6484 | 47500 | 0.3701 | 0.6215 | 0.6135 | 0.6174 | 0.9633 | |
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| 0.0001 | 27.9395 | 48000 | 0.3718 | 0.6252 | 0.6122 | 0.6186 | 0.9633 | |
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| 0.0001 | 28.2305 | 48500 | 0.3743 | 0.6308 | 0.6066 | 0.6184 | 0.9634 | |
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| 0.0 | 28.5215 | 49000 | 0.3785 | 0.6333 | 0.5957 | 0.6139 | 0.9634 | |
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| 0.0001 | 28.8126 | 49500 | 0.3764 | 0.6258 | 0.6087 | 0.6171 | 0.9633 | |
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| 0.0001 | 29.1036 | 50000 | 0.3761 | 0.6266 | 0.6103 | 0.6183 | 0.9634 | |
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| 0.0001 | 29.3946 | 50500 | 0.3770 | 0.6333 | 0.6051 | 0.6189 | 0.9634 | |
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| 0.0001 | 29.6857 | 51000 | 0.3780 | 0.6346 | 0.6037 | 0.6188 | 0.9635 | |
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| 0.0 | 29.9767 | 51500 | 0.3771 | 0.6326 | 0.6068 | 0.6194 | 0.9635 | |
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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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