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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-kd-pre-ner-full-mdeberta_data-univner_full44 |
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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-kd-pre-ner-full-mdeberta_data-univner_full44 |
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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: 46.6459 |
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- Precision: 0.8272 |
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- Recall: 0.8335 |
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- F1: 0.8303 |
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- Accuracy: 0.9822 |
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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: 8 |
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- eval_batch_size: 32 |
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- seed: 44 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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 |
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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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| 149.9231 | 0.2911 | 500 | 110.8748 | 0.5070 | 0.1208 | 0.1951 | 0.9347 | |
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| 101.1776 | 0.5822 | 1000 | 93.5222 | 0.7123 | 0.6586 | 0.6844 | 0.9701 | |
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| 89.9222 | 0.8732 | 1500 | 86.5450 | 0.7487 | 0.7276 | 0.7380 | 0.9748 | |
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| 83.6167 | 1.1643 | 2000 | 81.4135 | 0.7818 | 0.7501 | 0.7656 | 0.9769 | |
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| 78.8225 | 1.4554 | 2500 | 77.5955 | 0.7905 | 0.7547 | 0.7722 | 0.9777 | |
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| 75.3094 | 1.7465 | 3000 | 74.1885 | 0.7825 | 0.7798 | 0.7812 | 0.9783 | |
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| 71.9149 | 2.0375 | 3500 | 71.4168 | 0.7893 | 0.8020 | 0.7956 | 0.9790 | |
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| 68.8017 | 2.3286 | 4000 | 68.6904 | 0.8194 | 0.7778 | 0.7981 | 0.9794 | |
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| 66.2935 | 2.6197 | 4500 | 66.3018 | 0.7981 | 0.8070 | 0.8025 | 0.9802 | |
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| 64.1282 | 2.9108 | 5000 | 64.3227 | 0.7988 | 0.8130 | 0.8059 | 0.9803 | |
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| 61.983 | 3.2019 | 5500 | 62.6362 | 0.8141 | 0.8114 | 0.8128 | 0.9808 | |
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| 60.0914 | 3.4929 | 6000 | 60.8145 | 0.8106 | 0.8149 | 0.8127 | 0.9808 | |
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| 58.497 | 3.7840 | 6500 | 59.2819 | 0.8126 | 0.8158 | 0.8142 | 0.9812 | |
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| 57.0173 | 4.0751 | 7000 | 58.0187 | 0.8126 | 0.7990 | 0.8058 | 0.9804 | |
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| 55.5793 | 4.3662 | 7500 | 56.7794 | 0.8033 | 0.8240 | 0.8135 | 0.9808 | |
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| 54.4031 | 4.6573 | 8000 | 55.5089 | 0.8072 | 0.8287 | 0.8178 | 0.9812 | |
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| 53.2147 | 4.9483 | 8500 | 54.5450 | 0.8128 | 0.8094 | 0.8111 | 0.9810 | |
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| 52.0438 | 5.2394 | 9000 | 53.6043 | 0.8145 | 0.8222 | 0.8184 | 0.9814 | |
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| 51.102 | 5.5305 | 9500 | 52.6326 | 0.8100 | 0.8261 | 0.818 | 0.9811 | |
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| 50.3841 | 5.8216 | 10000 | 51.8428 | 0.8138 | 0.8300 | 0.8219 | 0.9815 | |
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| 49.4812 | 6.1126 | 10500 | 51.1615 | 0.8192 | 0.8296 | 0.8244 | 0.9819 | |
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| 48.7273 | 6.4037 | 11000 | 50.4750 | 0.8156 | 0.8201 | 0.8178 | 0.9813 | |
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| 48.1157 | 6.6948 | 11500 | 49.8869 | 0.8190 | 0.8259 | 0.8224 | 0.9818 | |
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| 47.4821 | 6.9859 | 12000 | 49.2946 | 0.8203 | 0.8279 | 0.8241 | 0.9819 | |
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| 46.889 | 7.2770 | 12500 | 48.8428 | 0.8178 | 0.8224 | 0.8201 | 0.9816 | |
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| 46.3939 | 7.5680 | 13000 | 48.3821 | 0.8264 | 0.8224 | 0.8244 | 0.9819 | |
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| 46.0878 | 7.8591 | 13500 | 47.9867 | 0.8210 | 0.8272 | 0.8241 | 0.9817 | |
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| 45.669 | 8.1502 | 14000 | 47.6715 | 0.8207 | 0.8257 | 0.8232 | 0.9818 | |
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| 45.3064 | 8.4413 | 14500 | 47.3744 | 0.8167 | 0.8336 | 0.8251 | 0.9818 | |
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| 45.0768 | 8.7324 | 15000 | 47.1812 | 0.8221 | 0.8235 | 0.8228 | 0.9821 | |
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| 44.8212 | 9.0234 | 15500 | 46.9769 | 0.8172 | 0.8274 | 0.8223 | 0.9816 | |
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| 44.6107 | 9.3145 | 16000 | 46.8141 | 0.8204 | 0.8298 | 0.8250 | 0.9819 | |
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| 44.4495 | 9.6056 | 16500 | 46.7872 | 0.8189 | 0.8285 | 0.8236 | 0.9819 | |
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| 44.51 | 9.8967 | 17000 | 46.6459 | 0.8272 | 0.8335 | 0.8303 | 0.9822 | |
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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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