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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-recipe-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-recipe-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: 0.1036 |
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- Precision: 0.7976 |
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- Recall: 0.7874 |
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- F1: 0.7925 |
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- Accuracy: 0.9663 |
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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-07 |
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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: 5 |
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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 | 0.5 | 400 | 0.3117 | 0.3718 | 0.0264 | 0.0493 | 0.8933 | |
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| 0.4072 | 1.01 | 800 | 0.2093 | 0.7573 | 0.2371 | 0.3612 | 0.9160 | |
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| 0.2943 | 1.51 | 1200 | 0.1434 | 0.7922 | 0.6436 | 0.7102 | 0.9523 | |
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| 0.2159 | 2.01 | 1600 | 0.1269 | 0.7806 | 0.7091 | 0.7431 | 0.9581 | |
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| 0.1737 | 2.51 | 2000 | 0.1183 | 0.7974 | 0.7365 | 0.7657 | 0.9610 | |
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| 0.1737 | 3.02 | 2400 | 0.1111 | 0.8044 | 0.7674 | 0.7855 | 0.9638 | |
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| 0.1483 | 3.52 | 2800 | 0.1076 | 0.8085 | 0.7765 | 0.7922 | 0.9653 | |
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| 0.1423 | 4.02 | 3200 | 0.1051 | 0.8061 | 0.7797 | 0.7927 | 0.9658 | |
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| 0.1385 | 4.52 | 3600 | 0.1036 | 0.7976 | 0.7874 | 0.7925 | 0.9663 | |
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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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