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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-non-kd-pre-ner-full-xlmr_data-univner_en44 |
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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-pre-ner-full-xlmr_data-univner_en44 |
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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: 0.1507 |
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- Precision: 0.7345 |
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- Recall: 0.7588 |
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- F1: 0.7464 |
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- Accuracy: 0.9803 |
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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: 44 |
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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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| 0.0869 | 1.2755 | 500 | 0.0715 | 0.6808 | 0.7308 | 0.7049 | 0.9773 | |
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| 0.0328 | 2.5510 | 1000 | 0.0781 | 0.7035 | 0.7319 | 0.7174 | 0.9784 | |
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| 0.0202 | 3.8265 | 1500 | 0.0805 | 0.7083 | 0.7340 | 0.7209 | 0.9791 | |
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| 0.0121 | 5.1020 | 2000 | 0.0914 | 0.7255 | 0.7495 | 0.7373 | 0.9792 | |
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| 0.0071 | 6.3776 | 2500 | 0.0988 | 0.7178 | 0.7557 | 0.7363 | 0.9787 | |
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| 0.005 | 7.6531 | 3000 | 0.1089 | 0.7178 | 0.7609 | 0.7387 | 0.9795 | |
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| 0.0033 | 8.9286 | 3500 | 0.1177 | 0.7353 | 0.7246 | 0.7299 | 0.9792 | |
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| 0.0033 | 10.2041 | 4000 | 0.1134 | 0.7219 | 0.7391 | 0.7304 | 0.9794 | |
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| 0.0022 | 11.4796 | 4500 | 0.1251 | 0.7243 | 0.7588 | 0.7412 | 0.9801 | |
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| 0.0023 | 12.7551 | 5000 | 0.1279 | 0.7070 | 0.7619 | 0.7334 | 0.9792 | |
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| 0.0017 | 14.0306 | 5500 | 0.1231 | 0.7165 | 0.7588 | 0.7371 | 0.9793 | |
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| 0.0014 | 15.3061 | 6000 | 0.1378 | 0.7289 | 0.7598 | 0.7440 | 0.9792 | |
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| 0.0014 | 16.5816 | 6500 | 0.1507 | 0.6986 | 0.7774 | 0.7359 | 0.9782 | |
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| 0.001 | 17.8571 | 7000 | 0.1421 | 0.7242 | 0.7474 | 0.7356 | 0.9793 | |
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| 0.0009 | 19.1327 | 7500 | 0.1396 | 0.7284 | 0.7578 | 0.7428 | 0.9796 | |
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| 0.0008 | 20.4082 | 8000 | 0.1394 | 0.7402 | 0.7226 | 0.7313 | 0.9788 | |
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| 0.0006 | 21.6837 | 8500 | 0.1425 | 0.7542 | 0.7371 | 0.7455 | 0.9797 | |
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| 0.0006 | 22.9592 | 9000 | 0.1446 | 0.7339 | 0.7308 | 0.7324 | 0.9793 | |
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| 0.0005 | 24.2347 | 9500 | 0.1459 | 0.7374 | 0.7557 | 0.7464 | 0.9804 | |
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| 0.0004 | 25.5102 | 10000 | 0.1443 | 0.7323 | 0.7505 | 0.7413 | 0.9800 | |
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| 0.0002 | 26.7857 | 10500 | 0.1485 | 0.7299 | 0.7526 | 0.7411 | 0.9801 | |
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| 0.0003 | 28.0612 | 11000 | 0.1507 | 0.7408 | 0.7484 | 0.7446 | 0.9803 | |
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| 0.0003 | 29.3367 | 11500 | 0.1507 | 0.7345 | 0.7588 | 0.7464 | 0.9803 | |
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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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