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--- |
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base_model: FacebookAI/xlm-roberta-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-xlmr_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-kd-pre-ner-full-xlmr_data-univner_full66 |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 48.2869 |
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- Precision: 0.8199 |
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- Recall: 0.8357 |
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- F1: 0.8277 |
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- Accuracy: 0.9820 |
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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: 66 |
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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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| 125.1691 | 0.2911 | 500 | 87.0081 | 0.7214 | 0.7142 | 0.7178 | 0.9733 | |
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| 79.398 | 0.5822 | 1000 | 74.2576 | 0.7483 | 0.7873 | 0.7673 | 0.9766 | |
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| 71.1356 | 0.8732 | 1500 | 69.3294 | 0.7983 | 0.7680 | 0.7829 | 0.9781 | |
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| 66.3695 | 1.1643 | 2000 | 65.7687 | 0.7888 | 0.8071 | 0.7978 | 0.9797 | |
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| 63.0397 | 1.4554 | 2500 | 63.3432 | 0.7887 | 0.8071 | 0.7978 | 0.9795 | |
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| 60.9741 | 1.7465 | 3000 | 61.3539 | 0.8072 | 0.8042 | 0.8057 | 0.9801 | |
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| 59.1711 | 2.0375 | 3500 | 59.9207 | 0.7996 | 0.8064 | 0.8030 | 0.9800 | |
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| 56.9342 | 2.3286 | 4000 | 58.2970 | 0.7933 | 0.8205 | 0.8067 | 0.9805 | |
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| 55.7631 | 2.6197 | 4500 | 57.1782 | 0.8095 | 0.8228 | 0.8161 | 0.9811 | |
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| 54.9048 | 2.9108 | 5000 | 56.2284 | 0.8047 | 0.8214 | 0.8129 | 0.9809 | |
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| 53.4488 | 3.2019 | 5500 | 55.2710 | 0.8025 | 0.8282 | 0.8151 | 0.9811 | |
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| 52.5197 | 3.4929 | 6000 | 54.6004 | 0.8184 | 0.8101 | 0.8142 | 0.9809 | |
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| 51.7352 | 3.7840 | 6500 | 53.8065 | 0.8014 | 0.8316 | 0.8163 | 0.9807 | |
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| 51.0298 | 4.0751 | 7000 | 53.1329 | 0.8129 | 0.8261 | 0.8195 | 0.9812 | |
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| 50.2097 | 4.3662 | 7500 | 52.4983 | 0.8155 | 0.8272 | 0.8213 | 0.9815 | |
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| 49.7445 | 4.6573 | 8000 | 52.1048 | 0.8134 | 0.8225 | 0.8179 | 0.9814 | |
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| 49.2896 | 4.9483 | 8500 | 51.5581 | 0.8169 | 0.8335 | 0.8251 | 0.9818 | |
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| 48.7639 | 5.2394 | 9000 | 51.2521 | 0.8145 | 0.8282 | 0.8213 | 0.9817 | |
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| 48.2298 | 5.5305 | 9500 | 50.7723 | 0.8134 | 0.8295 | 0.8213 | 0.9816 | |
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| 47.942 | 5.8216 | 10000 | 50.5179 | 0.8131 | 0.8323 | 0.8226 | 0.9817 | |
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| 47.4768 | 6.1126 | 10500 | 50.1601 | 0.8179 | 0.8322 | 0.8250 | 0.9818 | |
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| 47.176 | 6.4037 | 11000 | 49.9205 | 0.8154 | 0.8335 | 0.8243 | 0.9819 | |
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| 46.9979 | 6.6948 | 11500 | 49.6460 | 0.8201 | 0.8321 | 0.8260 | 0.9819 | |
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| 46.7367 | 6.9859 | 12000 | 49.4427 | 0.8145 | 0.8310 | 0.8227 | 0.9815 | |
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| 46.3668 | 7.2770 | 12500 | 49.3164 | 0.8139 | 0.8287 | 0.8213 | 0.9815 | |
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| 46.1975 | 7.5680 | 13000 | 49.0327 | 0.8137 | 0.8336 | 0.8235 | 0.9817 | |
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| 46.1108 | 7.8591 | 13500 | 48.9452 | 0.8249 | 0.8298 | 0.8273 | 0.9819 | |
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| 45.8657 | 8.1502 | 14000 | 48.7530 | 0.8234 | 0.8352 | 0.8293 | 0.9823 | |
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| 45.6956 | 8.4413 | 14500 | 48.5710 | 0.8210 | 0.8387 | 0.8298 | 0.9823 | |
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| 45.6259 | 8.7324 | 15000 | 48.5836 | 0.8200 | 0.8325 | 0.8262 | 0.9818 | |
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| 45.5612 | 9.0234 | 15500 | 48.4547 | 0.8214 | 0.8341 | 0.8277 | 0.9822 | |
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| 45.4012 | 9.3145 | 16000 | 48.3506 | 0.8224 | 0.8373 | 0.8298 | 0.9821 | |
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| 45.3943 | 9.6056 | 16500 | 48.3722 | 0.8207 | 0.8325 | 0.8265 | 0.9820 | |
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| 45.3452 | 9.8967 | 17000 | 48.2869 | 0.8199 | 0.8357 | 0.8277 | 0.9820 | |
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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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