---
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: 0.1884
- Precision: 0.8153
- Recall: 0.8947
- F1: 0.8531
- Accuracy: 0.9729

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.45  | 400  | 0.1389          | 0.7251    | 0.8609 | 0.7872 | 0.9622   |
| 0.2073        | 0.9   | 800  | 0.1628          | 0.8309    | 0.8797 | 0.8546 | 0.9747   |
| 0.157         | 1.35  | 1200 | 0.1346          | 0.7899    | 0.8888 | 0.8364 | 0.9710   |
| 0.1362        | 1.8   | 1600 | 0.1191          | 0.7340    | 0.8880 | 0.8037 | 0.9633   |
| 0.1356        | 2.25  | 2000 | 0.1253          | 0.6966    | 0.8888 | 0.7810 | 0.9570   |
| 0.1356        | 2.7   | 2400 | 0.1194          | 0.7556    | 0.8855 | 0.8154 | 0.9659   |
| 0.1175        | 3.15  | 2800 | 0.1546          | 0.8378    | 0.8880 | 0.8622 | 0.9754   |
| 0.1064        | 3.6   | 3200 | 0.1342          | 0.7955    | 0.8909 | 0.8405 | 0.9711   |
| 0.1116        | 4.04  | 3600 | 0.1314          | 0.7981    | 0.8984 | 0.8453 | 0.9713   |
| 0.0981        | 4.49  | 4000 | 0.1433          | 0.8059    | 0.8834 | 0.8429 | 0.9717   |
| 0.0981        | 4.94  | 4400 | 0.1439          | 0.8051    | 0.9026 | 0.8510 | 0.9719   |
| 0.0936        | 5.39  | 4800 | 0.1661          | 0.8180    | 0.8943 | 0.8544 | 0.9735   |
| 0.082         | 5.84  | 5200 | 0.1558          | 0.8179    | 0.8843 | 0.8498 | 0.9727   |
| 0.084         | 6.29  | 5600 | 0.1553          | 0.7918    | 0.8930 | 0.8394 | 0.9699   |
| 0.0782        | 6.74  | 6000 | 0.1457          | 0.7817    | 0.8943 | 0.8342 | 0.9684   |
| 0.0782        | 7.19  | 6400 | 0.1793          | 0.8134    | 0.8913 | 0.8506 | 0.9726   |
| 0.0694        | 7.64  | 6800 | 0.1638          | 0.7974    | 0.8930 | 0.8425 | 0.9707   |
| 0.0757        | 8.09  | 7200 | 0.1690          | 0.8042    | 0.8976 | 0.8483 | 0.9714   |
| 0.0665        | 8.54  | 7600 | 0.1813          | 0.8110    | 0.8951 | 0.8510 | 0.9724   |
| 0.0607        | 8.99  | 8000 | 0.1907          | 0.8226    | 0.8938 | 0.8567 | 0.9738   |
| 0.0607        | 9.44  | 8400 | 0.1848          | 0.8062    | 0.8938 | 0.8478 | 0.9719   |
| 0.0649        | 9.89  | 8800 | 0.1884          | 0.8153    | 0.8947 | 0.8531 | 0.9729   |


### Framework versions

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