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--- |
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base_model: haryoaw/scenario-TCR-NER_data-univner_en |
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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-po-ner-full-xlmr_data-univner_en66 |
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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-po-ner-full-xlmr_data-univner_en66 |
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This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1477 |
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- Precision: 0.8101 |
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- Recall: 0.7992 |
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- F1: 0.8046 |
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- Accuracy: 0.9836 |
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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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| 0.0036 | 1.2755 | 500 | 0.1194 | 0.8112 | 0.7785 | 0.7945 | 0.9831 | |
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| 0.0035 | 2.5510 | 1000 | 0.1172 | 0.7899 | 0.8137 | 0.8016 | 0.9838 | |
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| 0.0029 | 3.8265 | 1500 | 0.1173 | 0.7804 | 0.7909 | 0.7856 | 0.9822 | |
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| 0.0019 | 5.1020 | 2000 | 0.1175 | 0.7907 | 0.7940 | 0.7924 | 0.9841 | |
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| 0.0018 | 6.3776 | 2500 | 0.1271 | 0.7935 | 0.7836 | 0.7885 | 0.9830 | |
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| 0.0021 | 7.6531 | 3000 | 0.1281 | 0.7975 | 0.7909 | 0.7942 | 0.9837 | |
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| 0.0012 | 8.9286 | 3500 | 0.1170 | 0.7908 | 0.8023 | 0.7965 | 0.9843 | |
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| 0.0018 | 10.2041 | 4000 | 0.1324 | 0.8084 | 0.7950 | 0.8017 | 0.9840 | |
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| 0.0011 | 11.4796 | 4500 | 0.1304 | 0.7926 | 0.8188 | 0.8055 | 0.9839 | |
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| 0.0007 | 12.7551 | 5000 | 0.1370 | 0.7958 | 0.7867 | 0.7913 | 0.9838 | |
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| 0.0011 | 14.0306 | 5500 | 0.1297 | 0.7867 | 0.8095 | 0.7980 | 0.9837 | |
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| 0.0012 | 15.3061 | 6000 | 0.1254 | 0.7772 | 0.8126 | 0.7945 | 0.9830 | |
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| 0.0007 | 16.5816 | 6500 | 0.1374 | 0.8304 | 0.7909 | 0.8102 | 0.9831 | |
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| 0.0006 | 17.8571 | 7000 | 0.1369 | 0.7903 | 0.8116 | 0.8008 | 0.9832 | |
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| 0.0003 | 19.1327 | 7500 | 0.1379 | 0.7961 | 0.8043 | 0.8002 | 0.9841 | |
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| 0.0003 | 20.4082 | 8000 | 0.1365 | 0.7953 | 0.8002 | 0.7977 | 0.9838 | |
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| 0.0004 | 21.6837 | 8500 | 0.1458 | 0.7879 | 0.8230 | 0.8051 | 0.9835 | |
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| 0.0004 | 22.9592 | 9000 | 0.1475 | 0.8101 | 0.7992 | 0.8046 | 0.9835 | |
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| 0.0004 | 24.2347 | 9500 | 0.1405 | 0.7931 | 0.8054 | 0.7992 | 0.9837 | |
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| 0.0001 | 25.5102 | 10000 | 0.1391 | 0.7949 | 0.8147 | 0.8047 | 0.9843 | |
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| 0.0002 | 26.7857 | 10500 | 0.1432 | 0.8067 | 0.8033 | 0.8050 | 0.9840 | |
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| 0.0002 | 28.0612 | 11000 | 0.1439 | 0.8067 | 0.7992 | 0.8029 | 0.9836 | |
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| 0.0001 | 29.3367 | 11500 | 0.1477 | 0.8101 | 0.7992 | 0.8046 | 0.9836 | |
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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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