---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-large-cased-ner-food-combined-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-combined-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: 0.0681
- Precision: 0.8554
- Recall: 0.8743
- F1: 0.8647
- Accuracy: 0.9769

## 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-06
- train_batch_size: 16
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2606        | 1.12  | 500  | 0.0822          | 0.7976    | 0.8664 | 0.8306 | 0.9712   |
| 0.0837        | 2.25  | 1000 | 0.0955          | 0.7657    | 0.8764 | 0.8173 | 0.9683   |
| 0.0706        | 3.37  | 1500 | 0.0732          | 0.8322    | 0.8714 | 0.8513 | 0.9750   |
| 0.0631        | 4.49  | 2000 | 0.0681          | 0.8554    | 0.8743 | 0.8647 | 0.9769   |
| 0.0549        | 5.62  | 2500 | 0.0713          | 0.8356    | 0.8868 | 0.8604 | 0.9754   |
| 0.0521        | 6.74  | 3000 | 0.0700          | 0.8425    | 0.8863 | 0.8639 | 0.9759   |
| 0.0493        | 7.87  | 3500 | 0.0721          | 0.8444    | 0.8859 | 0.8647 | 0.9763   |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3