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---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-large-cased-ner-food-recipe-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlnet-large-cased-ner-food-recipe-v2
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1036
- Precision: 0.7976
- Recall: 0.7874
- F1: 0.7925
- Accuracy: 0.9663
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.5 | 400 | 0.3117 | 0.3718 | 0.0264 | 0.0493 | 0.8933 |
| 0.4072 | 1.01 | 800 | 0.2093 | 0.7573 | 0.2371 | 0.3612 | 0.9160 |
| 0.2943 | 1.51 | 1200 | 0.1434 | 0.7922 | 0.6436 | 0.7102 | 0.9523 |
| 0.2159 | 2.01 | 1600 | 0.1269 | 0.7806 | 0.7091 | 0.7431 | 0.9581 |
| 0.1737 | 2.51 | 2000 | 0.1183 | 0.7974 | 0.7365 | 0.7657 | 0.9610 |
| 0.1737 | 3.02 | 2400 | 0.1111 | 0.8044 | 0.7674 | 0.7855 | 0.9638 |
| 0.1483 | 3.52 | 2800 | 0.1076 | 0.8085 | 0.7765 | 0.7922 | 0.9653 |
| 0.1423 | 4.02 | 3200 | 0.1051 | 0.8061 | 0.7797 | 0.7927 | 0.9658 |
| 0.1385 | 4.52 | 3600 | 0.1036 | 0.7976 | 0.7874 | 0.7925 | 0.9663 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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