---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-large-cased-ner-food-combined-weighted-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-weighted-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: 0.1182
- Precision: 0.7436
- Recall: 0.8947
- F1: 0.8122
- Accuracy: 0.9642

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5366        | 1.12  | 500  | 0.1525          | 0.6775    | 0.8493 | 0.7537 | 0.9550   |
| 0.1697        | 2.25  | 1000 | 0.1385          | 0.6403    | 0.8580 | 0.7333 | 0.9457   |
| 0.1279        | 3.37  | 1500 | 0.1340          | 0.7899    | 0.8768 | 0.8311 | 0.9693   |
| 0.1178        | 4.49  | 2000 | 0.1247          | 0.7750    | 0.8876 | 0.8275 | 0.9679   |
| 0.1021        | 5.62  | 2500 | 0.1182          | 0.7436    | 0.8947 | 0.8122 | 0.9642   |
| 0.0957        | 6.74  | 3000 | 0.1192          | 0.7344    | 0.8876 | 0.8038 | 0.9626   |
| 0.0882        | 7.87  | 3500 | 0.1226          | 0.7641    | 0.8901 | 0.8223 | 0.9667   |
| 0.0802        | 8.99  | 4000 | 0.1323          | 0.7872    | 0.8901 | 0.8355 | 0.9695   |


### Framework versions

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