---
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