---
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: 0.1478
- Precision: 0.8033
- Recall: 0.8867
- F1: 0.8429
- Accuracy: 0.9708

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.5   | 400  | 0.1619          | 0.6591    | 0.8147 | 0.7287 | 0.9507   |
| 0.4091        | 1.01  | 800  | 0.1488          | 0.7832    | 0.8762 | 0.8271 | 0.9689   |
| 0.1678        | 1.51  | 1200 | 0.1538          | 0.8116    | 0.8862 | 0.8473 | 0.9712   |
| 0.1452        | 2.01  | 1600 | 0.1374          | 0.7638    | 0.8653 | 0.8114 | 0.9652   |
| 0.1359        | 2.51  | 2000 | 0.1450          | 0.7837    | 0.8858 | 0.8316 | 0.9678   |
| 0.1359        | 3.02  | 2400 | 0.1403          | 0.778     | 0.8853 | 0.8282 | 0.9676   |
| 0.1143        | 3.52  | 2800 | 0.1515          | 0.8128    | 0.8812 | 0.8456 | 0.9721   |
| 0.1189        | 4.02  | 3200 | 0.1420          | 0.8069    | 0.8862 | 0.8447 | 0.9711   |
| 0.1165        | 4.52  | 3600 | 0.1460          | 0.7861    | 0.8848 | 0.8325 | 0.9687   |


### Framework versions

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