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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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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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model-index: |
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- name: xlnet-large-cased-ner-food-combined-v2 |
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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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# xlnet-large-cased-ner-food-combined-v2 |
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This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1884 |
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- Precision: 0.8153 |
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- Recall: 0.8947 |
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- F1: 0.8531 |
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- Accuracy: 0.9729 |
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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: 5e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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| No log | 0.45 | 400 | 0.1389 | 0.7251 | 0.8609 | 0.7872 | 0.9622 | |
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| 0.2073 | 0.9 | 800 | 0.1628 | 0.8309 | 0.8797 | 0.8546 | 0.9747 | |
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| 0.157 | 1.35 | 1200 | 0.1346 | 0.7899 | 0.8888 | 0.8364 | 0.9710 | |
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| 0.1362 | 1.8 | 1600 | 0.1191 | 0.7340 | 0.8880 | 0.8037 | 0.9633 | |
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| 0.1356 | 2.25 | 2000 | 0.1253 | 0.6966 | 0.8888 | 0.7810 | 0.9570 | |
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| 0.1356 | 2.7 | 2400 | 0.1194 | 0.7556 | 0.8855 | 0.8154 | 0.9659 | |
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| 0.1175 | 3.15 | 2800 | 0.1546 | 0.8378 | 0.8880 | 0.8622 | 0.9754 | |
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| 0.1064 | 3.6 | 3200 | 0.1342 | 0.7955 | 0.8909 | 0.8405 | 0.9711 | |
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| 0.1116 | 4.04 | 3600 | 0.1314 | 0.7981 | 0.8984 | 0.8453 | 0.9713 | |
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| 0.0981 | 4.49 | 4000 | 0.1433 | 0.8059 | 0.8834 | 0.8429 | 0.9717 | |
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| 0.0981 | 4.94 | 4400 | 0.1439 | 0.8051 | 0.9026 | 0.8510 | 0.9719 | |
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| 0.0936 | 5.39 | 4800 | 0.1661 | 0.8180 | 0.8943 | 0.8544 | 0.9735 | |
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| 0.082 | 5.84 | 5200 | 0.1558 | 0.8179 | 0.8843 | 0.8498 | 0.9727 | |
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| 0.084 | 6.29 | 5600 | 0.1553 | 0.7918 | 0.8930 | 0.8394 | 0.9699 | |
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| 0.0782 | 6.74 | 6000 | 0.1457 | 0.7817 | 0.8943 | 0.8342 | 0.9684 | |
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| 0.0782 | 7.19 | 6400 | 0.1793 | 0.8134 | 0.8913 | 0.8506 | 0.9726 | |
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| 0.0694 | 7.64 | 6800 | 0.1638 | 0.7974 | 0.8930 | 0.8425 | 0.9707 | |
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| 0.0757 | 8.09 | 7200 | 0.1690 | 0.8042 | 0.8976 | 0.8483 | 0.9714 | |
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| 0.0665 | 8.54 | 7600 | 0.1813 | 0.8110 | 0.8951 | 0.8510 | 0.9724 | |
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| 0.0607 | 8.99 | 8000 | 0.1907 | 0.8226 | 0.8938 | 0.8567 | 0.9738 | |
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| 0.0607 | 9.44 | 8400 | 0.1848 | 0.8062 | 0.8938 | 0.8478 | 0.9719 | |
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| 0.0649 | 9.89 | 8800 | 0.1884 | 0.8153 | 0.8947 | 0.8531 | 0.9729 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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