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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: 0.1478 |
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- Precision: 0.8033 |
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- Recall: 0.8867 |
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- F1: 0.8429 |
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- Accuracy: 0.9708 |
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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: 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 | 0.1619 | 0.6591 | 0.8147 | 0.7287 | 0.9507 | |
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| 0.4091 | 1.01 | 800 | 0.1488 | 0.7832 | 0.8762 | 0.8271 | 0.9689 | |
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| 0.1678 | 1.51 | 1200 | 0.1538 | 0.8116 | 0.8862 | 0.8473 | 0.9712 | |
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| 0.1452 | 2.01 | 1600 | 0.1374 | 0.7638 | 0.8653 | 0.8114 | 0.9652 | |
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| 0.1359 | 2.51 | 2000 | 0.1450 | 0.7837 | 0.8858 | 0.8316 | 0.9678 | |
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| 0.1359 | 3.02 | 2400 | 0.1403 | 0.778 | 0.8853 | 0.8282 | 0.9676 | |
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| 0.1143 | 3.52 | 2800 | 0.1515 | 0.8128 | 0.8812 | 0.8456 | 0.9721 | |
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| 0.1189 | 4.02 | 3200 | 0.1420 | 0.8069 | 0.8862 | 0.8447 | 0.9711 | |
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| 0.1165 | 4.52 | 3600 | 0.1460 | 0.7861 | 0.8848 | 0.8325 | 0.9687 | |
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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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